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defi-protocol-templates

Implement DeFi protocols with production-ready templates for

coding
⭐1
# DeFi Protocol Templates Production-ready templates for common DeFi protocols including staking, AMMs, governance, lending, and flash loans. ## When to Use This Skill - Building staking platforms with reward distribution - Implementing AMM (Automated Market Maker) protocols - Creating governance token systems - Developing lending/borrowing protocols - Integrating flash loan functionality - Launching yield farming platforms ## Staking Contract ```solidity // SPDX-License-Identifier: MIT pragma solidity ^0.8.0; import "@openzeppelin/contracts/token/ERC20/IERC20.sol"; import "@openzeppelin/contracts/security/ReentrancyGuard.sol"; import "@openzeppelin/contracts/access/Ownable.sol"; contract StakingRewards is ReentrancyGuard, Ownable { IERC20 public stakingToken; IERC20 public rewardsToken; uint256 public rewardRate = 100; // Rewards per second uint256 public lastUpdateTime; uint256 public rewardPerTokenStored; mapping(address => uint256) public userRewardPerTokenPaid; mapping(address => uint256) public rewards; mapping(address => uint256) public balances; uint256 private _totalSupply; event Staked(address indexed user, uint256 amount); event Withdrawn(address indexed user, uint256 amount); event RewardPaid(address indexed user, uint256 reward); constructor(address _stakingToken, address _rewardsToken) { stakingToken = IERC20(_stakingToken); rewardsToken = IERC20(_rewardsToken); } modifier updateReward(address account) { rewardPerTokenStored = rewardPerToken(); lastUpdateTime = block.timestamp; if (account != address(0)) { rewards[account] = earned(account); userRewardPerTokenPaid[account] = rewardPerTokenStored; } _; } function rewardPerToken() public view returns (uint256) { if (_totalSupply == 0) { return rewardPerTokenStored; } return rewardPerTokenStored + ((block.timestamp - lastUpdateTime) * rewardRate * 1e18) / _totalSupply; } function earned(address account) public view returns (uint256) { return (balances[account] * (rewardPerToken() - userRewardPerTokenPaid[account])) / 1e18 + rewards[account]; } function stake(uint256 amount) external nonReentrant updateReward(msg.sender) { require(amount > 0, "Cannot stake 0"); _totalSupply += amount; balances[msg.sender] += amount; stakingToken.transferFrom(msg.sender, address(this), amount); emit Staked(msg.sender, amount); } function withdraw(uint256 amount) public nonReentrant updateReward(msg.sender) { require(amount > 0, "Cannot withdraw 0"); _totalSupply -= amount; balances[msg.sender] -= amount; stakingToken.transfer(msg.sender, amount); emit Withdrawn(msg.sender, amount); } function getReward() public nonReentrant updateReward(msg.sender) { uint256 reward = rewards[msg.sender]; if (reward > 0) { rewards[msg.sender] = 0; rewardsToken.transfer(msg.sender, reward); emit RewardPaid(msg.sender, reward); } } function exit() external { withdraw(balances[msg.sender]); getReward(); } } ``` ## AMM (Automated Market Maker) ```solidity // SPDX-License-Identifier: MIT pragma solidity ^0.8.0; import "@openzeppelin/contracts/token/ERC20/IERC20.sol"; contract SimpleAMM { IERC20 public token0; IERC20 public token1; uint256 public reserve0; uint256 public reserve1; uint256 public totalSupply; mapping(address => uint256) public balanceOf; event Mint(address indexed to, uint256 amount); event Burn(address indexed from, uint256 amount); event Swap(address indexed trader, uint256 amount0In, uint256 amount1In, uint256 amount0Out, uint256 amount1Out); constructor(address _token0, address _token1) { token0 = IERC20(_token0); token1 = IERC20(_token1); } function addLiquidity(uint256 amount0, uint256 amount1) external returns (uint256 shares) { token0.transferFrom(msg.sender, address(this), amount0); token1.transferFrom(msg.sender, address(this), amount1); if (totalSupply == 0) { shares = sqrt(amount0 * amount1); } else { shares = min( (amount0 * totalSupply) / reserve0, (amount1 * totalSupply) / reserve1 ); } require(shares > 0, "Shares = 0"); _mint(msg.sender, shares); _update( token0.balanceOf(address(this)), token1.balanceOf(address(this)) ); emit Mint(msg.sender, shares); } function removeLiquidity(uint256 shares) external returns (uint256 amount0, uint256 amount1) { uint256 bal0 = token0.balanceOf(address(this)); uint256 bal1 = token1.balanceOf(address(this)); amount0 = (shares * bal0) / totalSupply; amount1 = (shares * bal1) / totalSupply; require(amount0 > 0 && amount1 > 0, "Amount0 or amount1 = 0"); _burn(msg.sender, shares); _update(bal0 - amount0, bal1 - amount1); token0.transfer(msg.sender, amount0); token1.transfer(msg.sender, amount1); emit Burn(msg.sender, shares); } function swap(address tokenIn, uint256 amountIn) external returns (uint256 amountOut) { require(tokenIn == address(token0) || tokenIn == address(token1), "Invalid token"); bool isToken0 = tokenIn == address(token0); (IERC20 tokenIn_, IERC20 tokenOut, uint256 resIn, uint256 resOut) = isToken0 ? (token0, token1, reserve0, reserve1) : (token1, token0, reserve1, reserve0); tokenIn_.transferFrom(msg.sender, address(this), amountIn); // 0.3% fee uint256 amountInWithFee = (amountIn * 997) / 1000; amountOut = (resOut * amountInWithFee) / (resIn + amountInWithFee); tokenOut.transfer(msg.sender, amountOut); _update( token0.balanceOf(address(this)), token1.balanceOf(address(this)) ); emit Swap(msg.sender, isToken0 ? amountIn : 0, isToken0 ? 0 : amountIn, isToken0 ? 0 : amountOut, isToken0 ? amountOut : 0); } function _mint(address to, uint256 amount) private { balanceOf[to] += amount; totalSupply += amount; } function _burn(address from, uint256 amount) private { balanceOf[from] -= amount; totalSupply -= amount; } function _update(uint256 res0, uint256 res1) private { reserve0 = res0; reserve1 = res1; } function sqrt(uint256 y) private pure returns (uint256 z) { if (y > 3) { z = y; uint256 x = y / 2 + 1; while (x < z) { z = x; x = (y / x + x) / 2; } } else if (y != 0) { z = 1; } } function min(uint256 x, uint256 y) private pure returns (uint256) { return x <= y ? x : y; } } ``` ## Governance Token ```solidity // SPDX-License-Identifier: MIT pragma solidity ^0.8.0; import "@openzeppelin/contracts/token/ERC20/extensions/ERC20Votes.sol"; import "@openzeppelin/contracts/access/Ownable.sol"; contract GovernanceToken is ERC20Votes, Ownable { constructor() ERC20("Governance Token", "GOV") ERC20Permit("Governance Token") { _mint(msg.sender, 1000000 * 10**decimals()); } function _afterTokenTransfer( address from, address to, uint256 amount ) internal override(ERC20Votes) { super._afterTokenTransfer(from, to, amount); } function _mint(address to, uint256 amount) internal override(ERC20Votes) { super._mint(to, amount); } function _burn(address account, uint256 amount) internal override(ERC20Votes) { super._burn(account, amount); } } contract Governor is Ownable { GovernanceToken public governanceToken; struct Proposal { uint256 id; address proposer; string description; uint256 forVotes; uint256 againstVotes; uint256 startBlock; uint256 endBlock; bool executed; mapping(address => bool) hasVoted; } uint256 public proposalCount; mapping(uint256 => Proposal) public proposals; uint256 public votingPeriod = 17280; // ~3 days in blocks uint256 public proposalThreshold = 100000 * 10**18; event ProposalCreated(uint256 indexed proposalId, address proposer, string description); event VoteCast(address indexed voter, uint256 indexed proposalId, bool support, uint256 weight); event ProposalExecuted(uint256 indexed proposalId); constructor(address _governanceToken) { governanceToken = GovernanceToken(_governanceToken); } function propose(string memory description) external returns (uint256) { require( governanceToken.getPastVotes(msg.sender, block.number - 1) >= proposalThreshold, "Proposer votes below threshold" ); proposalCount++; Proposal storage newProposal = proposals[proposalCount]; newProposal.id = proposalCount; newProposal.proposer = msg.sender; newProposal.description = description; newProposal.startBlock = block.number; newProposal.endBlock = block.number + votingPeriod; emit ProposalCreated(proposalCount, msg.sender, description); return proposalCount; } function vote(uint256 proposalId, bool support) external { Proposal storage proposal = proposals[proposalId]; require(block.number >= proposal.startBlock, "Voting not started"); require(block.number <= proposal.endBlock, "Voting ended"); require(!proposal.hasVoted[msg.sender], "Already voted"); uint256 weight = governanceToken.getPastVotes(msg.sender, proposal.startBlock); require(weight > 0, "No voting power"); proposal.hasVoted[msg.sender] = true; if (support) { proposal.forVotes += weight; } else { proposal.againstVotes += weight; } emit VoteCast(msg.sender, proposalId, support, weight); } function execute(uint256 proposalId) external { Proposal storage proposal = proposals[proposalId]; require(block.number > proposal.endBlock, "Voting not ended"); require(!proposal.executed, "Already executed"); require(proposal.forVotes > proposal.againstVotes, "Proposal failed"); proposal.executed = true; // Execute proposal logic here emit ProposalExecuted(proposalId); } } ``` ## Flash Loan ```solidity // SPDX-License-Identifier: MIT pragma solidity ^0.8.0; import "@openzeppelin/contracts/token/ERC20/IERC20.sol"; interface IFlashLoanReceiver { function executeOperation( address asset, uint256 amount, uint256 fee, bytes calldata params ) external returns (bool); } contract FlashLoanProvider { IERC20 public token; uint256 public feePercentage = 9; // 0.09% fee event FlashLoan(address indexed borrower, uint256 amount, uint256 fee); constructor(address _token) { token = IERC20(_token); } function flashLoan( address receiver, uint256 amount, bytes calldata params ) external { uint256 balanceBefore = token.balanceOf(address(this)); require(balanceBefore >= amount, "Insufficient liquidity"); uint256 fee = (amount * feePercentage) / 10000; // Send tokens to receiver token.transfer(receiver, amount); // Execute callback require( IFlashLoanReceiver(receiver).executeOperation( address(token), amount, fee, params ), "Flash loan failed" ); // Verify repayment uint256 balanceAfter = token.balanceOf(address(this)); require(balanceAfter >= balanceBefore + fee, "Flash loan not repaid"); emit FlashLoan(receiver, amount, fee); } } // Example flash loan receiver contract FlashLoanReceiver is IFlashLoanReceiver { function executeOperation( address asset, uint256 amount, uint256 fee, bytes calldata params ) external override returns (bool) { // Decode params and execute arbitrage, liquidation, etc. // ... // Approve repayment IERC20(asset).approve(msg.sender, amount + fee); return true; } } ``` ## Resources - **references/staking.md**: Staking mechanics and reward distribution - **references/liquidity-pools.md**: AMM mathematics and pricing - **references/governance-tokens.md**: Governance and voting systems - **references/lending-protocols.md**: Lending/borrowing implementation - **references/flash-loans.md**: Flash loan security and use cases - **assets/staking-contract.sol**: Production staking template - **assets/amm-contract.sol**: Full AMM implementation - **assets/governance-token.sol**: Governance system - **assets/lending-protocol.sol**: Lending platform template ## Best Practices 1. **Use Established Libraries**: OpenZeppelin, Solmate 2. **Test Thoroughly**: Unit tests, integration tests, fuzzing 3. **Audit Before Launch**: Professional security audits 4. **Start Simple**: MVP first, add features incrementally 5. **Monitor**: Track contract health and user activity 6. **Upgradability**: Consider proxy patterns for upgrades 7. **Emergency Controls**: Pause mechanisms for critical issues ## Common DeFi Patterns - **Time-Weighted Average Price (TWAP)**: Price oracle resistance - **Liquidity Mining**: Incentivize liquidity provision - **Vesting**: Lock tokens with gradual release - **Multisig**: Require multiple signatures for critical operations - **Timelocks**: Delay execution of governance decisions
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👁️0
🤖 Auto-discovered
🤖system prompt•7 months ago

nft-standards

Implement NFT standards (ERC-721, ERC-1155) with proper metadata

coding
⭐1
# NFT Standards Master ERC-721 and ERC-1155 NFT standards, metadata best practices, and advanced NFT features. ## When to Use This Skill - Creating NFT collections (art, gaming, collectibles) - Implementing marketplace functionality - Building on-chain or off-chain metadata - Creating soulbound tokens (non-transferable) - Implementing royalties and revenue sharing - Developing dynamic/evolving NFTs ## ERC-721 (Non-Fungible Token Standard) ```solidity // SPDX-License-Identifier: MIT pragma solidity ^0.8.0; import "@openzeppelin/contracts/token/ERC721/extensions/ERC721URIStorage.sol"; import "@openzeppelin/contracts/token/ERC721/extensions/ERC721Enumerable.sol"; import "@openzeppelin/contracts/access/Ownable.sol"; import "@openzeppelin/contracts/utils/Counters.sol"; contract MyNFT is ERC721URIStorage, ERC721Enumerable, Ownable { using Counters for Counters.Counter; Counters.Counter private _tokenIds; uint256 public constant MAX_SUPPLY = 10000; uint256 public constant MINT_PRICE = 0.08 ether; uint256 public constant MAX_PER_MINT = 20; constructor() ERC721("MyNFT", "MNFT") {} function mint(uint256 quantity) external payable { require(quantity > 0 && quantity <= MAX_PER_MINT, "Invalid quantity"); require(_tokenIds.current() + quantity <= MAX_SUPPLY, "Exceeds max supply"); require(msg.value >= MINT_PRICE * quantity, "Insufficient payment"); for (uint256 i = 0; i < quantity; i++) { _tokenIds.increment(); uint256 newTokenId = _tokenIds.current(); _safeMint(msg.sender, newTokenId); _setTokenURI(newTokenId, generateTokenURI(newTokenId)); } } function generateTokenURI(uint256 tokenId) internal pure returns (string memory) { // Return IPFS URI or on-chain metadata return string(abi.encodePacked("ipfs://QmHash/", Strings.toString(tokenId), ".json")); } // Required overrides function _beforeTokenTransfer( address from, address to, uint256 tokenId, uint256 batchSize ) internal override(ERC721, ERC721Enumerable) { super._beforeTokenTransfer(from, to, tokenId, batchSize); } function _burn(uint256 tokenId) internal override(ERC721, ERC721URIStorage) { super._burn(tokenId); } function tokenURI(uint256 tokenId) public view override(ERC721, ERC721URIStorage) returns (string memory) { return super.tokenURI(tokenId); } function supportsInterface(bytes4 interfaceId) public view override(ERC721, ERC721Enumerable) returns (bool) { return super.supportsInterface(interfaceId); } function withdraw() external onlyOwner { payable(owner()).transfer(address(this).balance); } } ``` ## ERC-1155 (Multi-Token Standard) ```solidity // SPDX-License-Identifier: MIT pragma solidity ^0.8.0; import "@openzeppelin/contracts/token/ERC1155/ERC1155.sol"; import "@openzeppelin/contracts/access/Ownable.sol"; contract GameItems is ERC1155, Ownable { uint256 public constant SWORD = 1; uint256 public constant SHIELD = 2; uint256 public constant POTION = 3; mapping(uint256 => uint256) public tokenSupply; mapping(uint256 => uint256) public maxSupply; constructor() ERC1155("ipfs://QmBaseHash/{id}.json") { maxSupply[SWORD] = 1000; maxSupply[SHIELD] = 500; maxSupply[POTION] = 10000; } function mint( address to, uint256 id, uint256 amount ) external onlyOwner { require(tokenSupply[id] + amount <= maxSupply[id], "Exceeds max supply"); _mint(to, id, amount, ""); tokenSupply[id] += amount; } function mintBatch( address to, uint256[] memory ids, uint256[] memory amounts ) external onlyOwner { for (uint256 i = 0; i < ids.length; i++) { require(tokenSupply[ids[i]] + amounts[i] <= maxSupply[ids[i]], "Exceeds max supply"); tokenSupply[ids[i]] += amounts[i]; } _mintBatch(to, ids, amounts, ""); } function burn( address from, uint256 id, uint256 amount ) external { require(from == msg.sender || isApprovedForAll(from, msg.sender), "Not authorized"); _burn(from, id, amount); tokenSupply[id] -= amount; } } ``` ## Metadata Standards ### Off-Chain Metadata (IPFS) ```json { "name": "NFT #1", "description": "Description of the NFT", "image": "ipfs://QmImageHash", "attributes": [ { "trait_type": "Background", "value": "Blue" }, { "trait_type": "Rarity", "value": "Legendary" }, { "trait_type": "Power", "value": 95, "display_type": "number", "max_value": 100 } ] } ``` ### On-Chain Metadata ```solidity contract OnChainNFT is ERC721 { struct Traits { uint8 background; uint8 body; uint8 head; uint8 rarity; } mapping(uint256 => Traits) public tokenTraits; function tokenURI(uint256 tokenId) public view override returns (string memory) { Traits memory traits = tokenTraits[tokenId]; string memory json = Base64.encode( bytes( string( abi.encodePacked( '{"name": "NFT #', Strings.toString(tokenId), '",', '"description": "On-chain NFT",', '"image": "data:image/svg+xml;base64,', generateSVG(traits), '",', '"attributes": [', '{"trait_type": "Background", "value": "', Strings.toString(traits.background), '"},', '{"trait_type": "Rarity", "value": "', getRarityName(traits.rarity), '"}', ']}' ) ) ) ); return string(abi.encodePacked("data:application/json;base64,", json)); } function generateSVG(Traits memory traits) internal pure returns (string memory) { // Generate SVG based on traits return "..."; } } ``` ## Royalties (EIP-2981) ```solidity import "@openzeppelin/contracts/interfaces/IERC2981.sol"; contract NFTWithRoyalties is ERC721, IERC2981 { address public royaltyRecipient; uint96 public royaltyFee = 500; // 5% constructor() ERC721("Royalty NFT", "RNFT") { royaltyRecipient = msg.sender; } function royaltyInfo(uint256 tokenId, uint256 salePrice) external view override returns (address receiver, uint256 royaltyAmount) { return (royaltyRecipient, (salePrice * royaltyFee) / 10000); } function setRoyalty(address recipient, uint96 fee) external onlyOwner { require(fee <= 1000, "Royalty fee too high"); // Max 10% royaltyRecipient = recipient; royaltyFee = fee; } function supportsInterface(bytes4 interfaceId) public view override(ERC721, IERC165) returns (bool) { return interfaceId == type(IERC2981).interfaceId || super.supportsInterface(interfaceId); } } ``` ## Soulbound Tokens (Non-Transferable) ```solidity contract SoulboundToken is ERC721 { constructor() ERC721("Soulbound", "SBT") {} function _beforeTokenTransfer( address from, address to, uint256 tokenId, uint256 batchSize ) internal virtual override { require(from == address(0) || to == address(0), "Token is soulbound"); super._beforeTokenTransfer(from, to, tokenId, batchSize); } function mint(address to) external { uint256 tokenId = totalSupply() + 1; _safeMint(to, tokenId); } // Burn is allowed (user can destroy their SBT) function burn(uint256 tokenId) external { require(ownerOf(tokenId) == msg.sender, "Not token owner"); _burn(tokenId); } } ``` ## Dynamic NFTs ```solidity contract DynamicNFT is ERC721 { struct TokenState { uint256 level; uint256 experience; uint256 lastUpdated; } mapping(uint256 => TokenState) public tokenStates; function gainExperience(uint256 tokenId, uint256 exp) external { require(ownerOf(tokenId) == msg.sender, "Not token owner"); TokenState storage state = tokenStates[tokenId]; state.experience += exp; // Level up logic if (state.experience >= state.level * 100) { state.level++; } state.lastUpdated = block.timestamp; } function tokenURI(uint256 tokenId) public view override returns (string memory) { TokenState memory state = tokenStates[tokenId]; // Generate metadata based on current state return generateMetadata(tokenId, state); } function generateMetadata(uint256 tokenId, TokenState memory state) internal pure returns (string memory) { // Dynamic metadata generation return ""; } } ``` ## Gas-Optimized Minting (ERC721A) ```solidity import "erc721a/contracts/ERC721A.sol"; contract OptimizedNFT is ERC721A { uint256 public constant MAX_SUPPLY = 10000; uint256 public constant MINT_PRICE = 0.05 ether; constructor() ERC721A("Optimized NFT", "ONFT") {} function mint(uint256 quantity) external payable { require(_totalMinted() + quantity <= MAX_SUPPLY, "Exceeds max supply"); require(msg.value >= MINT_PRICE * quantity, "Insufficient payment"); _mint(msg.sender, quantity); } function _baseURI() internal pure override returns (string memory) { return "ipfs://QmBaseHash/"; } } ``` ## Resources - **references/erc721.md**: ERC-721 specification details - **references/erc1155.md**: ERC-1155 multi-token standard - **references/metadata-standards.md**: Metadata best practices - **references/enumeration.md**: Token enumeration patterns - **assets/erc721-contract.sol**: Production ERC-721 template - **assets/erc1155-contract.sol**: Production ERC-1155 template - **assets/metadata-schema.json**: Standard metadata format - **assets/metadata-uploader.py**: IPFS upload utility ## Best Practices 1. **Use OpenZeppelin**: Battle-tested implementations 2. **Pin Metadata**: Use IPFS with pinning service 3. **Implement Royalties**: EIP-2981 for marketplace compatibility 4. **Gas Optimization**: Use ERC721A for batch minting 5. **Reveal Mechanism**: Placeholder → reveal pattern 6. **Enumeration**: Support walletOfOwner for marketplaces 7. **Whitelist**: Merkle trees for efficient whitelisting ## Marketplace Integration - OpenSea: ERC-721/1155, metadata standards - LooksRare: Royalty enforcement - Rarible: Protocol fees, lazy minting - Blur: Gas-optimized trading
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👁️0
🤖 Auto-discovered
🤖system prompt•7 months ago

data-storytelling

Transform data into compelling narratives using visualization,

data
⭐1
# Data Storytelling Transform raw data into compelling narratives that drive decisions and inspire action. ## When to Use This Skill - Presenting analytics to executives - Creating quarterly business reviews - Building investor presentations - Writing data-driven reports - Communicating insights to non-technical audiences - Making recommendations based on data ## Core Concepts ### 1. Story Structure ``` Setup → Conflict → Resolution Setup: Context and baseline Conflict: The problem or opportunity Resolution: Insights and recommendations ``` ### 2. Narrative Arc ``` 1. Hook: Grab attention with surprising insight 2. Context: Establish the baseline 3. Rising Action: Build through data points 4. Climax: The key insight 5. Resolution: Recommendations 6. Call to Action: Next steps ``` ### 3. Three Pillars | Pillar | Purpose | Components | | ------------- | -------- | -------------------------------- | | **Data** | Evidence | Numbers, trends, comparisons | | **Narrative** | Meaning | Context, causation, implications | | **Visuals** | Clarity | Charts, diagrams, highlights | ## Story Frameworks ### Framework 1: The Problem-Solution Story ```markdown # Customer Churn Analysis ## The Hook "We're losing $2.4M annually to preventable churn." ## The Context - Current churn rate: 8.5% (industry average: 5%) - Average customer lifetime value: $4,800 - 500 customers churned last quarter ## The Problem Analysis of churned customers reveals a pattern: - 73% churned within first 90 days - Common factor: < 3 support interactions - Low feature adoption in first month ## The Insight [Show engagement curve visualization] Customers who don't engage in the first 14 days are 4x more likely to churn. ## The Solution 1. Implement 14-day onboarding sequence 2. Proactive outreach at day 7 3. Feature adoption tracking ## Expected Impact - Reduce early churn by 40% - Save $960K annually - Payback period: 3 months ## Call to Action Approve $50K budget for onboarding automation. ``` ### Framework 2: The Trend Story ```markdown # Q4 Performance Analysis ## Where We Started Q3 ended with $1.2M MRR, 15% below target. Team morale was low after missed goals. ## What Changed [Timeline visualization] - Oct: Launched self-serve pricing - Nov: Reduced friction in signup - Dec: Added customer success calls ## The Transformation [Before/after comparison chart] | Metric | Q3 | Q4 | Change | |----------------|--------|--------|--------| | Trial → Paid | 8% | 15% | +87% | | Time to Value | 14 days| 5 days | -64% | | Expansion Rate | 2% | 8% | +300% | ## Key Insight Self-serve + high-touch creates compound growth. Customers who self-serve AND get a success call have 3x higher expansion rate. ## Going Forward Double down on hybrid model. Target: $1.8M MRR by Q2. ``` ### Framework 3: The Comparison Story ```markdown # Market Opportunity Analysis ## The Question Should we expand into EMEA or APAC first? ## The Comparison [Side-by-side market analysis] ### EMEA - Market size: $4.2B - Growth rate: 8% - Competition: High - Regulatory: Complex (GDPR) - Language: Multiple ### APAC - Market size: $3.8B - Growth rate: 15% - Competition: Moderate - Regulatory: Varied - Language: Multiple ## The Analysis [Weighted scoring matrix visualization] | Factor | Weight | EMEA Score | APAC Score | | ----------- | ------ | ---------- | ---------- | | Market Size | 25% | 5 | 4 | | Growth | 30% | 3 | 5 | | Competition | 20% | 2 | 4 | | Ease | 25% | 2 | 3 | | **Total** | | **2.9** | **4.1** | ## The Recommendation APAC first. Higher growth, less competition. Start with Singapore hub (English, business-friendly). Enter EMEA in Year 2 with localization ready. ## Risk Mitigation - Timezone coverage: Hire 24/7 support - Cultural fit: Local partnerships - Payment: Multi-currency from day 1 ``` ## Visualization Techniques ### Technique 1: Progressive Reveal ```markdown Start simple, add layers: Slide 1: "Revenue is growing" [single line chart] Slide 2: "But growth is slowing" [add growth rate overlay] Slide 3: "Driven by one segment" [add segment breakdown] Slide 4: "Which is saturating" [add market share] Slide 5: "We need new segments" [add opportunity zones] ``` ### Technique 2: Contrast and Compare ```markdown Before/After: ┌─────────────────┬─────────────────┐ │ BEFORE │ AFTER │ │ │ │ │ Process: 5 days│ Process: 1 day │ │ Errors: 15% │ Errors: 2% │ │ Cost: $50/unit │ Cost: $20/unit │ └─────────────────┴─────────────────┘ This/That (emphasize difference): ┌─────────────────────────────────────┐ │ CUSTOMER A vs B │ │ ┌──────────┐ ┌──────────┐ │ │ │ ████████ │ │ ██ │ │ │ │ $45,000 │ │ $8,000 │ │ │ │ LTV │ │ LTV │ │ │ └──────────┘ └──────────┘ │ │ Onboarded No onboarding │ └─────────────────────────────────────┘ ``` ### Technique 3: Annotation and Highlight ```python import matplotlib.pyplot as plt import pandas as pd fig, ax = plt.subplots(figsize=(12, 6)) # Plot the main data ax.plot(dates, revenue, linewidth=2, color='#2E86AB') # Add annotation for key events ax.annotate( 'Product Launch\n+32% spike', xy=(launch_date, launch_revenue), xytext=(launch_date, launch_revenue * 1.2), fontsize=10, arrowprops=dict(arrowstyle='->', color='#E63946'), color='#E63946' ) # Highlight a region ax.axvspan(growth_start, growth_end, alpha=0.2, color='green', label='Growth Period') # Add threshold line ax.axhline(y=target, color='gray', linestyle='--', label=f'Target: ${target:,.0f}') ax.set_title('Revenue Growth Story', fontsize=14, fontweight='bold') ax.legend() ``` ## Presentation Templates ### Template 1: Executive Summary Slide ``` ┌─────────────────────────────────────────────────────────────┐ │ KEY INSIGHT │ │ ══════════════════════════════════════════════════════════│ │ │ │ "Customers who complete onboarding in week 1 │ │ have 3x higher lifetime value" │ │ │ ├──────────────────────┬──────────────────────────────────────┤ │ │ │ │ THE DATA │ THE IMPLICATION │ │ │ │ │ Week 1 completers: │ ✓ Prioritize onboarding UX │ │ • LTV: $4,500 │ ✓ Add day-1 success milestones │ │ • Retention: 85% │ ✓ Proactive week-1 outreach │ │ • NPS: 72 │ │ │ │ Investment: $75K │ │ Others: │ Expected ROI: 8x │ │ • LTV: $1,500 │ │ │ • Retention: 45% │ │ │ • NPS: 34 │ │ │ │ │ └──────────────────────┴──────────────────────────────────────┘ ``` ### Template 2: Data Story Flow ``` Slide 1: THE HEADLINE "We can grow 40% faster by fixing onboarding" Slide 2: THE CONTEXT Current state metrics Industry benchmarks Gap analysis Slide 3: THE DISCOVERY What the data revealed Surprising finding Pattern identification Slide 4: THE DEEP DIVE Root cause analysis Segment breakdowns Statistical significance Slide 5: THE RECOMMENDATION Proposed actions Resource requirements Timeline Slide 6: THE IMPACT Expected outcomes ROI calculation Risk assessment Slide 7: THE ASK Specific request Decision needed Next steps ``` ### Template 3: One-Page Dashboard Story ```markdown # Monthly Business Review: January 2024 ## THE HEADLINE Revenue up 15% but CAC increasing faster than LTV ## KEY METRICS AT A GLANCE ┌────────┬────────┬────────┬────────┐ │ MRR │ NRR │ CAC │ LTV │ │ $125K │ 108% │ $450 │ $2,200 │ │ ▲15% │ ▲3% │ ▲22% │ ▲8% │ └────────┴────────┴────────┴────────┘ ## WHAT'S WORKING ✓ Enterprise segment growing 25% MoM ✓ Referral program driving 30% of new logos ✓ Support satisfaction at all-time high (94%) ## WHAT NEEDS ATTENTION ✗ SMB acquisition cost up 40% ✗ Trial conversion down 5 points ✗ Time-to-value increased by 3 days ## ROOT CAUSE [Mini chart showing SMB vs Enterprise CAC trend] SMB paid ads becoming less efficient. CPC up 35% while conversion flat. ## RECOMMENDATION 1. Shift $20K/mo from paid to content 2. Launch SMB self-serve trial 3. A/B test shorter onboarding ## NEXT MONTH'S FOCUS - Launch content marketing pilot - Complete self-serve MVP - Reduce time-to-value to < 7 days ``` ## Writing Techniques ### Headlines That Work ```markdown BAD: "Q4 Sales Analysis" GOOD: "Q4 Sales Beat Target by 23% - Here's Why" BAD: "Customer Churn Report" GOOD: "We're Losing $2.4M to Preventable Churn" BAD: "Marketing Performance" GOOD: "Content Marketing Delivers 4x ROI vs. Paid" Formula: [Specific Number] + [Business Impact] + [Actionable Context] ``` ### Transition Phrases ```markdown Building the narrative: • "This leads us to ask..." • "When we dig deeper..." • "The pattern becomes clear when..." • "Contrast this with..." Introducing insights: • "The data reveals..." • "What surprised us was..." • "The inflection point came when..." • "The key finding is..." Moving to action: • "This insight suggests..." • "Based on this analysis..." • "The implication is clear..." • "Our recommendation is..." ``` ### Handling Uncertainty ```markdown Acknowledge limitations: • "With 95% confidence, we can say..." • "The sample size of 500 shows..." • "While correlation is strong, causation requires..." • "This trend holds for [segment], though [caveat]..." Present ranges: • "Impact estimate: $400K-$600K" • "Confidence interval: 15-20% improvement" • "Best case: X, Conservative: Y" ``` ## Best Practices ### Do's - **Start with the "so what"** - Lead with insight - **Use the rule of three** - Three points, three comparisons - **Show, don't tell** - Let data speak - **Make it personal** - Connect to audience goals - **End with action** - Clear next steps ### Don'ts - **Don't data dump** - Curate ruthlessly - **Don't bury the insight** - Front-load key findings - **Don't use jargon** - Match audience vocabulary - **Don't show methodology first** - Context, then method - **Don't forget the narrative** - Numbers need meaning ## Resources - [Storytelling with Data (Cole Nussbaumer)](https://www.storytellingwithdata.com/) - [The Pyramid Principle (Barbara Minto)](https://www.amazon.com/Pyramid-Principle-Logic-Writing-Thinking/dp/0273710516) - [Resonate (Nancy Duarte)](https://www.duarte.com/resonate/)
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🤖 Auto-discovered
🤖system prompt•7 months ago

kpi-dashboard-design

Design effective KPI dashboards with metrics selection,

coding
⭐1
# KPI Dashboard Design Comprehensive patterns for designing effective Key Performance Indicator (KPI) dashboards that drive business decisions. ## When to Use This Skill - Designing executive dashboards - Selecting meaningful KPIs - Building real-time monitoring displays - Creating department-specific metrics views - Improving existing dashboard layouts - Establishing metric governance ## Core Concepts ### 1. KPI Framework | Level | Focus | Update Frequency | Audience | | --------------- | ---------------- | ----------------- | ---------- | | **Strategic** | Long-term goals | Monthly/Quarterly | Executives | | **Tactical** | Department goals | Weekly/Monthly | Managers | | **Operational** | Day-to-day | Real-time/Daily | Teams | ### 2. SMART KPIs ``` Specific: Clear definition Measurable: Quantifiable Achievable: Realistic targets Relevant: Aligned to goals Time-bound: Defined period ``` ### 3. Dashboard Hierarchy ``` ├── Executive Summary (1 page) │ ├── 4-6 headline KPIs │ ├── Trend indicators │ └── Key alerts ├── Department Views │ ├── Sales Dashboard │ ├── Marketing Dashboard │ ├── Operations Dashboard │ └── Finance Dashboard └── Detailed Drilldowns ├── Individual metrics └── Root cause analysis ``` ## Common KPIs by Department ### Sales KPIs ```yaml Revenue Metrics: - Monthly Recurring Revenue (MRR) - Annual Recurring Revenue (ARR) - Average Revenue Per User (ARPU) - Revenue Growth Rate Pipeline Metrics: - Sales Pipeline Value - Win Rate - Average Deal Size - Sales Cycle Length Activity Metrics: - Calls/Emails per Rep - Demos Scheduled - Proposals Sent - Close Rate ``` ### Marketing KPIs ```yaml Acquisition: - Cost Per Acquisition (CPA) - Customer Acquisition Cost (CAC) - Lead Volume - Marketing Qualified Leads (MQL) Engagement: - Website Traffic - Conversion Rate - Email Open/Click Rate - Social Engagement ROI: - Marketing ROI - Campaign Performance - Channel Attribution - CAC Payback Period ``` ### Product KPIs ```yaml Usage: - Daily/Monthly Active Users (DAU/MAU) - Session Duration - Feature Adoption Rate - Stickiness (DAU/MAU) Quality: - Net Promoter Score (NPS) - Customer Satisfaction (CSAT) - Bug/Issue Count - Time to Resolution Growth: - User Growth Rate - Activation Rate - Retention Rate - Churn Rate ``` ### Finance KPIs ```yaml Profitability: - Gross Margin - Net Profit Margin - EBITDA - Operating Margin Liquidity: - Current Ratio - Quick Ratio - Cash Flow - Working Capital Efficiency: - Revenue per Employee - Operating Expense Ratio - Days Sales Outstanding - Inventory Turnover ``` ## Dashboard Layout Patterns ### Pattern 1: Executive Summary ``` ┌─────────────────────────────────────────────────────────────┐ │ EXECUTIVE DASHBOARD [Date Range ▼] │ ├─────────────┬─────────────┬─────────────┬─────────────────┤ │ REVENUE │ PROFIT │ CUSTOMERS │ NPS SCORE │ │ $2.4M │ $450K │ 12,450 │ 72 │ │ ▲ 12% │ ▲ 8% │ ▲ 15% │ ▲ 5pts │ ├─────────────┴─────────────┴─────────────┴─────────────────┤ │ │ │ Revenue Trend │ Revenue by Product │ │ ┌───────────────────────┐ │ ┌──────────────────┐ │ │ │ /\ /\ │ │ │ ████████ 45% │ │ │ │ / \ / \ /\ │ │ │ ██████ 32% │ │ │ │ / \/ \ / \ │ │ │ ████ 18% │ │ │ │ / \/ \ │ │ │ ██ 5% │ │ │ └───────────────────────┘ │ └──────────────────┘ │ │ │ ├─────────────────────────────────────────────────────────────┤ │ 🔴 Alert: Churn rate exceeded threshold (>5%) │ │ 🟡 Warning: Support ticket volume 20% above average │ └─────────────────────────────────────────────────────────────┘ ``` ### Pattern 2: SaaS Metrics Dashboard ``` ┌─────────────────────────────────────────────────────────────┐ │ SAAS METRICS Jan 2024 [Monthly ▼] │ ├──────────────────────┬──────────────────────────────────────┤ │ ┌────────────────┐ │ MRR GROWTH │ │ │ MRR │ │ ┌────────────────────────────────┐ │ │ │ $125,000 │ │ │ /── │ │ │ │ ▲ 8% │ │ │ /────/ │ │ │ └────────────────┘ │ │ /────/ │ │ │ ┌────────────────┐ │ │ /────/ │ │ │ │ ARR │ │ │ /────/ │ │ │ │ $1,500,000 │ │ └────────────────────────────────┘ │ │ │ ▲ 15% │ │ J F M A M J J A S O N D │ │ └────────────────┘ │ │ ├──────────────────────┼──────────────────────────────────────┤ │ UNIT ECONOMICS │ COHORT RETENTION │ │ │ │ │ CAC: $450 │ Month 1: ████████████████████ 100% │ │ LTV: $2,700 │ Month 3: █████████████████ 85% │ │ LTV/CAC: 6.0x │ Month 6: ████████████████ 80% │ │ │ Month 12: ██████████████ 72% │ │ Payback: 4 months │ │ ├──────────────────────┴──────────────────────────────────────┤ │ CHURN ANALYSIS │ │ ┌──────────┬──────────┬──────────┬──────────────────────┐ │ │ │ Gross │ Net │ Logo │ Expansion │ │ │ │ 4.2% │ 1.8% │ 3.1% │ 2.4% │ │ │ └──────────┴──────────┴──────────┴──────────────────────┘ │ └─────────────────────────────────────────────────────────────┘ ``` ### Pattern 3: Real-time Operations ``` ┌─────────────────────────────────────────────────────────────┐ │ OPERATIONS CENTER Live ● Last: 10:42:15 │ ├────────────────────────────┬────────────────────────────────┤ │ SYSTEM HEALTH │ SERVICE STATUS │ │ ┌──────────────────────┐ │ │ │ │ CPU MEM DISK │ │ ● API Gateway Healthy │ │ │ 45% 72% 58% │ │ ● User Service Healthy │ │ │ ███ ████ ███ │ │ ● Payment Service Degraded │ │ │ ███ ████ ███ │ │ ● Database Healthy │ │ │ ███ ████ ███ │ │ ● Cache Healthy │ │ └──────────────────────┘ │ │ ├────────────────────────────┼────────────────────────────────┤ │ REQUEST THROUGHPUT │ ERROR RATE │ │ ┌──────────────────────┐ │ ┌──────────────────────────┐ │ │ │ ▁▂▃▄▅▆▇█▇▆▅▄▃▂▁▂▃▄▅ │ │ │ ▁▁▁▁▁▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁ │ │ │ └──────────────────────┘ │ └──────────────────────────┘ │ │ Current: 12,450 req/s │ Current: 0.02% │ │ Peak: 18,200 req/s │ Threshold: 1.0% │ ├────────────────────────────┴────────────────────────────────┤ │ RECENT ALERTS │ │ 10:40 🟡 High latency on payment-service (p99 > 500ms) │ │ 10:35 🟢 Resolved: Database connection pool recovered │ │ 10:22 🔴 Payment service circuit breaker tripped │ └─────────────────────────────────────────────────────────────┘ ``` ## Implementation Patterns ### SQL for KPI Calculations ```sql -- Monthly Recurring Revenue (MRR) WITH mrr_calculation AS ( SELECT DATE_TRUNC('month', billing_date) AS month, SUM( CASE subscription_interval WHEN 'monthly' THEN amount WHEN 'yearly' THEN amount / 12 WHEN 'quarterly' THEN amount / 3 END ) AS mrr FROM subscriptions WHERE status = 'active' GROUP BY DATE_TRUNC('month', billing_date) ) SELECT month, mrr, LAG(mrr) OVER (ORDER BY month) AS prev_mrr, (mrr - LAG(mrr) OVER (ORDER BY month)) / LAG(mrr) OVER (ORDER BY month) * 100 AS growth_pct FROM mrr_calculation; -- Cohort Retention WITH cohorts AS ( SELECT user_id, DATE_TRUNC('month', created_at) AS cohort_month FROM users ), activity AS ( SELECT user_id, DATE_TRUNC('month', event_date) AS activity_month FROM user_events WHERE event_type = 'active_session' ) SELECT c.cohort_month, EXTRACT(MONTH FROM age(a.activity_month, c.cohort_month)) AS months_since_signup, COUNT(DISTINCT a.user_id) AS active_users, COUNT(DISTINCT a.user_id)::FLOAT / COUNT(DISTINCT c.user_id) * 100 AS retention_rate FROM cohorts c LEFT JOIN activity a ON c.user_id = a.user_id AND a.activity_month >= c.cohort_month GROUP BY c.cohort_month, EXTRACT(MONTH FROM age(a.activity_month, c.cohort_month)) ORDER BY c.cohort_month, months_since_signup; -- Customer Acquisition Cost (CAC) SELECT DATE_TRUNC('month', acquired_date) AS month, SUM(marketing_spend) / NULLIF(COUNT(new_customers), 0) AS cac, SUM(marketing_spend) AS total_spend, COUNT(new_customers) AS customers_acquired FROM ( SELECT DATE_TRUNC('month', u.created_at) AS acquired_date, u.id AS new_customers, m.spend AS marketing_spend FROM users u JOIN marketing_spend m ON DATE_TRUNC('month', u.created_at) = m.month WHERE u.source = 'marketing' ) acquisition GROUP BY DATE_TRUNC('month', acquired_date); ``` ### Python Dashboard Code (Streamlit) ```python import streamlit as st import pandas as pd import plotly.express as px import plotly.graph_objects as go st.set_page_config(page_title="KPI Dashboard", layout="wide") # Header with date filter col1, col2 = st.columns([3, 1]) with col1: st.title("Executive Dashboard") with col2: date_range = st.selectbox( "Period", ["Last 7 Days", "Last 30 Days", "Last Quarter", "YTD"] ) # KPI Cards def metric_card(label, value, delta, prefix="", suffix=""): delta_color = "green" if delta >= 0 else "red" delta_arrow = "▲" if delta >= 0 else "▼" st.metric( label=label, value=f"{prefix}{value:,.0f}{suffix}", delta=f"{delta_arrow} {abs(delta):.1f}%" ) col1, col2, col3, col4 = st.columns(4) with col1: metric_card("Revenue", 2400000, 12.5, prefix="$") with col2: metric_card("Customers", 12450, 15.2) with col3: metric_card("NPS Score", 72, 5.0) with col4: metric_card("Churn Rate", 4.2, -0.8, suffix="%") # Charts col1, col2 = st.columns(2) with col1: st.subheader("Revenue Trend") revenue_data = pd.DataFrame({ 'Month': pd.date_range('2024-01-01', periods=12, freq='M'), 'Revenue': [180000, 195000, 210000, 225000, 240000, 255000, 270000, 285000, 300000, 315000, 330000, 345000] }) fig = px.line(revenue_data, x='Month', y='Revenue', line_shape='spline', markers=True) fig.update_layout(height=300) st.plotly_chart(fig, use_container_width=True) with col2: st.subheader("Revenue by Product") product_data = pd.DataFrame({ 'Product': ['Enterprise', 'Professional', 'Starter', 'Other'], 'Revenue': [45, 32, 18, 5] }) fig = px.pie(product_data, values='Revenue', names='Product', hole=0.4) fig.update_layout(height=300) st.plotly_chart(fig, use_container_width=True) # Cohort Heatmap st.subheader("Cohort Retention") cohort_data = pd.DataFrame({ 'Cohort': ['Jan', 'Feb', 'Mar', 'Apr', 'May'], 'M0': [100, 100, 100, 100, 100], 'M1': [85, 87, 84, 86, 88], 'M2': [78, 80, 76, 79, None], 'M3': [72, 74, 70, None, None], 'M4': [68, 70, None, None, None], }) fig = go.Figure(data=go.Heatmap( z=cohort_data.iloc[:, 1:].values, x=['M0', 'M1', 'M2', 'M3', 'M4'], y=cohort_data['Cohort'], colorscale='Blues', text=cohort_data.iloc[:, 1:].values, texttemplate='%{text}%', textfont={"size": 12}, )) fig.update_layout(height=250) st.plotly_chart(fig, use_container_width=True) # Alerts Section st.subheader("Alerts") alerts = [ {"level": "error", "message": "Churn rate exceeded threshold (>5%)"}, {"level": "warning", "message": "Support ticket volume 20% above average"}, ] for alert in alerts: if alert["level"] == "error": st.error(f"🔴 {alert['message']}") elif alert["level"] == "warning": st.warning(f"🟡 {alert['message']}") ``` ## Best Practices ### Do's - **Limit to 5-7 KPIs** - Focus on what matters - **Show context** - Comparisons, trends, targets - **Use consistent colors** - Red=bad, green=good - **Enable drilldown** - From summary to detail - **Update appropriately** - Match metric frequency ### Don'ts - **Don't show vanity metrics** - Focus on actionable data - **Don't overcrowd** - White space aids comprehension - **Don't use 3D charts** - They distort perception - **Don't hide methodology** - Document calculations - **Don't ignore mobile** - Ensure responsive design ## Resources - [Stephen Few's Dashboard Design](https://www.perceptualedge.com/articles/visual_business_intelligence/rules_for_using_color.pdf) - [Edward Tufte's Principles](https://www.edwardtufte.com/tufte/) - [Google Data Studio Gallery](https://datastudio.google.com/gallery)
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🤖 Auto-discovered
🤖system prompt•7 months ago

employment-contract-templates

Create employment contracts, offer letters, and HR policy documents

coding
⭐1
# Employment Contract Templates Templates and patterns for creating legally sound employment documentation including contracts, offer letters, and HR policies. ## When to Use This Skill - Drafting employment contracts - Creating offer letters - Writing employee handbooks - Developing HR policies - Standardizing employment documentation - Onboarding documentation ## Core Concepts ### 1. Employment Document Types | Document | Purpose | When Used | | ----------------------- | ----------------------- | ------------- | | **Offer Letter** | Initial job offer | Pre-hire | | **Employment Contract** | Formal agreement | Hire | | **Employee Handbook** | Policies & procedures | Onboarding | | **NDA** | Confidentiality | Before access | | **Non-Compete** | Competition restriction | Hire/Exit | ### 2. Key Legal Considerations ``` Employment Relationship: ├── At-Will vs. Contract ├── Employee vs. Contractor ├── Full-Time vs. Part-Time ├── Exempt vs. Non-Exempt └── Jurisdiction-Specific Requirements ``` **DISCLAIMER: These templates are for informational purposes only and do not constitute legal advice. Consult with qualified legal counsel before using any employment documents.** ## Templates ### Template 1: Offer Letter ```markdown # EMPLOYMENT OFFER LETTER [Company Letterhead] Date: [DATE] [Candidate Name] [Address] [City, State ZIP] Dear [Candidate Name], We are pleased to extend an offer of employment for the position of [JOB TITLE] at [COMPANY NAME]. We believe your skills and experience will be valuable additions to our team. ## Position Details **Title:** [Job Title] **Department:** [Department] **Reports To:** [Manager Name/Title] **Location:** [Office Location / Remote] **Start Date:** [Proposed Start Date] **Employment Type:** [Full-Time/Part-Time], [Exempt/Non-Exempt] ## Compensation **Base Salary:** $[AMOUNT] per [year/hour], paid [bi-weekly/semi-monthly/monthly] **Bonus:** [Eligible for annual bonus of up to X% based on company and individual performance / Not applicable] **Equity:** [X shares of stock options vesting over 4 years with 1-year cliff / Not applicable] ## Benefits You will be eligible for our standard benefits package, including: - Health insurance (medical, dental, vision) effective [date] - 401(k) with [X]% company match - [x] days paid time off per year - [x] paid holidays - [Other benefits] Full details will be provided during onboarding. ## Contingencies This offer is contingent upon: - Successful completion of background check - Verification of your right to work in [Country] - Execution of required employment documents including: - Confidentiality Agreement - [Non-Compete Agreement, if applicable] - [IP Assignment Agreement] ## At-Will Employment Please note that employment with [Company Name] is at-will. This means that either you or the Company may terminate the employment relationship at any time, with or without cause or notice. This offer letter does not constitute a contract of employment for any specific period. ## Acceptance To accept this offer, please sign below and return by [DEADLINE DATE]. This offer will expire if not accepted by that date. We are excited about the possibility of you joining our team. If you have any questions, please contact [HR Contact] at [email/phone]. Sincerely, --- [Hiring Manager Name] [Title] [Company Name] --- ## ACCEPTANCE I accept this offer of employment and agree to the terms stated above. Signature: ************\_************ Printed Name: ************\_************ Date: ************\_************ Anticipated Start Date: ************\_************ ``` ### Template 2: Employment Agreement (Contract Position) ```markdown # EMPLOYMENT AGREEMENT This Employment Agreement ("Agreement") is entered into as of [DATE] ("Effective Date") by and between: **Employer:** [COMPANY LEGAL NAME], a [State] [corporation/LLC] with principal offices at [Address] ("Company") **Employee:** [EMPLOYEE NAME], an individual residing at [Address] ("Employee") ## 1. EMPLOYMENT 1.1 **Position.** The Company agrees to employ Employee as [JOB TITLE], reporting to [Manager Title]. Employee accepts such employment subject to the terms of this Agreement. 1.2 **Duties.** Employee shall perform duties consistent with their position, including but not limited to: - [Primary duty 1] - [Primary duty 2] - [Primary duty 3] - Other duties as reasonably assigned 1.3 **Best Efforts.** Employee agrees to devote their full business time, attention, and best efforts to the Company's business during employment. 1.4 **Location.** Employee's primary work location shall be [Location/Remote]. [Travel requirements, if any.] ## 2. TERM 2.1 **Employment Period.** This Agreement shall commence on [START DATE] and continue until terminated as provided herein. 2.2 **At-Will Employment.** [FOR AT-WILL STATES] Notwithstanding anything herein, employment is at-will and may be terminated by either party at any time, with or without cause or notice. [OR FOR FIXED TERM:] 2.2 **Fixed Term.** This Agreement is for a fixed term of [X] months/years, ending on [END DATE], unless terminated earlier as provided herein or extended by mutual written agreement. ## 3. COMPENSATION 3.1 **Base Salary.** Employee shall receive a base salary of $[AMOUNT] per year, payable in accordance with the Company's standard payroll practices, subject to applicable withholdings. 3.2 **Bonus.** Employee may be eligible for an annual discretionary bonus of up to [X]% of base salary, based on [criteria]. Bonus payments are at Company's sole discretion and require active employment at payment date. 3.3 **Equity.** [If applicable] Subject to Board approval and the Company's equity incentive plan, Employee shall be granted [X shares/options] under the terms of a separate Stock Option Agreement. 3.4 **Benefits.** Employee shall be entitled to participate in benefit plans offered to similarly situated employees, subject to plan terms and eligibility requirements. 3.5 **Expenses.** Company shall reimburse Employee for reasonable business expenses incurred in accordance with Company policy. ## 4. CONFIDENTIALITY 4.1 **Confidential Information.** Employee acknowledges access to confidential and proprietary information including: trade secrets, business plans, customer lists, financial data, technical information, and other non-public information ("Confidential Information"). 4.2 **Non-Disclosure.** During and after employment, Employee shall not disclose, use, or permit use of any Confidential Information except as required for their duties or with prior written consent. 4.3 **Return of Materials.** Upon termination, Employee shall immediately return all Company property and Confidential Information in any form. 4.4 **Survival.** Confidentiality obligations survive termination indefinitely for trade secrets and for [3] years for other Confidential Information. ## 5. INTELLECTUAL PROPERTY 5.1 **Work Product.** All inventions, discoveries, works, and developments created by Employee during employment, relating to Company's business, or using Company resources ("Work Product") shall be Company's sole property. 5.2 **Assignment.** Employee hereby assigns to Company all rights in Work Product, including all intellectual property rights. 5.3 **Assistance.** Employee agrees to execute documents and take actions necessary to perfect Company's rights in Work Product. 5.4 **Prior Inventions.** Attached as Exhibit A is a list of any prior inventions that Employee wishes to exclude from this Agreement. ## 6. NON-COMPETITION AND NON-SOLICITATION [NOTE: Enforceability varies by jurisdiction. Consult local counsel.] 6.1 **Non-Competition.** During employment and for [12] months after termination, Employee shall not, directly or indirectly, engage in any business competitive with Company's business within [Geographic Area]. 6.2 **Non-Solicitation of Customers.** During employment and for [12] months after termination, Employee shall not solicit any customer of the Company for competing products or services. 6.3 **Non-Solicitation of Employees.** During employment and for [12] months after termination, Employee shall not recruit or solicit any Company employee to leave Company employment. ## 7. TERMINATION 7.1 **By Company for Cause.** Company may terminate immediately for Cause, defined as: (a) Material breach of this Agreement (b) Conviction of a felony (c) Fraud, dishonesty, or gross misconduct (d) Failure to perform duties after written notice and cure period 7.2 **By Company Without Cause.** Company may terminate without Cause upon [30] days written notice. 7.3 **By Employee.** Employee may terminate upon [30] days written notice. 7.4 **Severance.** [If applicable] Upon termination without Cause, Employee shall receive [X] weeks base salary as severance, contingent upon execution of a release agreement. 7.5 **Effect of Termination.** Upon termination: - All compensation earned through termination date shall be paid - Unvested equity shall be forfeited - Benefits terminate per plan terms - Sections 4, 5, 6, 8, and 9 survive termination ## 8. GENERAL PROVISIONS 8.1 **Entire Agreement.** This Agreement constitutes the entire agreement and supersedes all prior negotiations, representations, and agreements. 8.2 **Amendments.** This Agreement may be amended only by written agreement signed by both parties. 8.3 **Governing Law.** This Agreement shall be governed by the laws of [State], without regard to conflicts of law principles. 8.4 **Dispute Resolution.** [Arbitration clause or jurisdiction selection] 8.5 **Severability.** If any provision is unenforceable, it shall be modified to the minimum extent necessary, and remaining provisions shall remain in effect. 8.6 **Notices.** Notices shall be in writing and delivered to addresses above. 8.7 **Assignment.** Employee may not assign this Agreement. Company may assign to a successor. 8.8 **Waiver.** Failure to enforce any provision shall not constitute waiver. ## 9. ACKNOWLEDGMENTS Employee acknowledges: - Having read and understood this Agreement - Having opportunity to consult with counsel - Agreeing to all terms voluntarily --- IN WITNESS WHEREOF, the parties have executed this Agreement as of the Effective Date. **[COMPANY NAME]** By: ************\_************ Name: [Authorized Signatory] Title: [Title] Date: ************\_************ **EMPLOYEE** Signature: ************\_************ Name: [Employee Name] Date: ************\_************ --- ## EXHIBIT A: PRIOR INVENTIONS [Employee to list any prior inventions, if any, or write "None"] --- ``` ### Template 3: Employee Handbook Policy Section ```markdown # EMPLOYEE HANDBOOK - POLICY SECTION ## EMPLOYMENT POLICIES ### Equal Employment Opportunity [Company Name] is an equal opportunity employer. We do not discriminate based on race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, veteran status, or any other protected characteristic. This policy applies to all employment practices including: - Recruitment and hiring - Compensation and benefits - Training and development - Promotions and transfers - Termination ### Anti-Harassment Policy [Company Name] is committed to providing a workplace free from harassment. Harassment based on any protected characteristic is strictly prohibited. **Prohibited Conduct Includes:** - Unwelcome sexual advances or requests for sexual favors - Offensive comments, jokes, or slurs - Physical conduct such as assault or unwanted touching - Visual conduct such as displaying offensive images - Threatening, intimidating, or hostile acts **Reporting Procedure:** 1. Report to your manager, HR, or any member of leadership 2. Reports may be made verbally or in writing 3. Anonymous reports are accepted via [hotline/email] **Investigation:** All reports will be promptly investigated. Retaliation against anyone who reports harassment is strictly prohibited and will result in disciplinary action up to termination. ### Work Hours and Attendance **Standard Hours:** [8:00 AM - 5:00 PM, Monday through Friday] **Core Hours:** [10:00 AM - 3:00 PM] - Employees expected to be available **Flexible Work:** [Policy on remote work, flexible scheduling] **Attendance Expectations:** - Notify your manager as soon as possible if you will be absent - Excessive unexcused absences may result in disciplinary action - [x] unexcused absences in [Y] days considered excessive ### Paid Time Off (PTO) **PTO Accrual:** | Years of Service | Annual PTO Days | |------------------|-----------------| | 0-2 years | 15 days | | 3-5 years | 20 days | | 6+ years | 25 days | **PTO Guidelines:** - PTO accrues per pay period - Maximum accrual: [X] days (use it or lose it after) - Request PTO at least [2] weeks in advance - Manager approval required - PTO may not be taken during [blackout periods] ### Sick Leave - [x] days sick leave per year - May be used for personal illness or family member care - Doctor's note required for absences exceeding [3] days ### Holidays The following paid holidays are observed: - New Year's Day - Martin Luther King Jr. Day - Presidents Day - Memorial Day - Independence Day - Labor Day - Thanksgiving Day - Day after Thanksgiving - Christmas Day - [Floating holiday] ### Code of Conduct All employees are expected to: - Act with integrity and honesty - Treat colleagues, customers, and partners with respect - Protect company confidential information - Avoid conflicts of interest - Comply with all laws and regulations - Report any violations of this code **Violations may result in disciplinary action up to and including termination.** ### Technology and Communication **Acceptable Use:** - Company technology is for business purposes - Limited personal use is permitted if it doesn't interfere with work - No illegal activities or viewing inappropriate content **Monitoring:** - Company reserves the right to monitor company systems - Employees should have no expectation of privacy on company devices **Security:** - Use strong passwords and enable 2FA - Report security incidents immediately - Lock devices when unattended ### Social Media Policy **Personal Social Media:** - Clearly state opinions are your own, not the company's - Do not share confidential company information - Be respectful and professional **Company Social Media:** - Only authorized personnel may post on behalf of the company - Follow brand guidelines - Escalate negative comments to [Marketing/PR] --- ## ACKNOWLEDGMENT I acknowledge that I have received a copy of the Employee Handbook and understand that: 1. I am responsible for reading and understanding its contents 2. The handbook does not create a contract of employment 3. Policies may be changed at any time at the company's discretion 4. Employment is at-will [if applicable] I agree to abide by the policies and procedures outlined in this handbook. Employee Signature: ************\_************ Employee Name (Print): ************\_************ Date: ************\_************ ``` ## Best Practices ### Do's - **Consult legal counsel** - Employment law varies by jurisdiction - **Keep copies signed** - Document all agreements - **Update regularly** - Laws and policies change - **Be clear and specific** - Avoid ambiguity - **Train managers** - On policies and procedures ### Don'ts - **Don't use generic templates** - Customize for your jurisdiction - **Don't make promises** - That could create implied contracts - **Don't discriminate** - In language or application - **Don't forget at-will language** - Where applicable - **Don't skip review** - Have legal counsel review all documents ## Resources - [SHRM Employment Templates](https://www.shrm.org/) - [Department of Labor](https://www.dol.gov/) - [EEOC Guidance](https://www.eeoc.gov/) - State-specific labor departments
👍0
👁️0
🤖 Auto-discovered
🤖system prompt•7 months ago

gdpr-data-handling

Implement GDPR-compliant data handling with consent management,

data
⭐1
# GDPR Data Handling Practical implementation guide for GDPR-compliant data processing, consent management, and privacy controls. ## When to Use This Skill - Building systems that process EU personal data - Implementing consent management - Handling data subject requests (DSRs) - Conducting GDPR compliance reviews - Designing privacy-first architectures - Creating data processing agreements ## Core Concepts ### 1. Personal Data Categories | Category | Examples | Protection Level | | ---------------------- | --------------------------- | ------------------ | | **Basic** | Name, email, phone | Standard | | **Sensitive (Art. 9)** | Health, religion, ethnicity | Explicit consent | | **Criminal (Art. 10)** | Convictions, offenses | Official authority | | **Children's** | Under 16 data | Parental consent | ### 2. Legal Bases for Processing ``` Article 6 - Lawful Bases: ├── Consent: Freely given, specific, informed ├── Contract: Necessary for contract performance ├── Legal Obligation: Required by law ├── Vital Interests: Protecting someone's life ├── Public Interest: Official functions └── Legitimate Interest: Balanced against rights ``` ### 3. Data Subject Rights ``` Right to Access (Art. 15) ─┐ Right to Rectification (Art. 16) │ Right to Erasure (Art. 17) │ Must respond Right to Restrict (Art. 18) │ within 1 month Right to Portability (Art. 20) │ Right to Object (Art. 21) ─┘ ``` ## Implementation Patterns ### Pattern 1: Consent Management ```javascript // Consent data model const consentSchema = { userId: String, consents: [ { purpose: String, // 'marketing', 'analytics', etc. granted: Boolean, timestamp: Date, source: String, // 'web_form', 'api', etc. version: String, // Privacy policy version ipAddress: String, // For proof userAgent: String, // For proof }, ], auditLog: [ { action: String, // 'granted', 'withdrawn', 'updated' purpose: String, timestamp: Date, source: String, }, ], }; // Consent service class ConsentManager { async recordConsent(userId, purpose, granted, metadata) { const consent = { purpose, granted, timestamp: new Date(), source: metadata.source, version: await this.getCurrentPolicyVersion(), ipAddress: metadata.ipAddress, userAgent: metadata.userAgent, }; // Store consent await this.db.consents.updateOne( { userId }, { $push: { consents: consent, auditLog: { action: granted ? "granted" : "withdrawn", purpose, timestamp: consent.timestamp, source: metadata.source, }, }, }, { upsert: true }, ); // Emit event for downstream systems await this.eventBus.emit("consent.changed", { userId, purpose, granted, timestamp: consent.timestamp, }); } async hasConsent(userId, purpose) { const record = await this.db.consents.findOne({ userId }); if (!record) return false; const latestConsent = record.consents .filter((c) => c.purpose === purpose) .sort((a, b) => b.timestamp - a.timestamp)[0]; return latestConsent?.granted === true; } async getConsentHistory(userId) { const record = await this.db.consents.findOne({ userId }); return record?.auditLog || []; } } ``` ```html <!-- GDPR-compliant consent UI --> <div class="consent-banner" role="dialog" aria-labelledby="consent-title"> <h2 id="consent-title">Cookie Preferences</h2> <p> We use cookies to improve your experience. Select your preferences below. </p> <form id="consent-form"> <!-- Necessary - always on, no consent needed --> <div class="consent-category"> <input type="checkbox" id="necessary" checked disabled /> <label for="necessary"> <strong>Necessary</strong> <span>Required for the website to function. Cannot be disabled.</span> </label> </div> <!-- Analytics - requires consent --> <div class="consent-category"> <input type="checkbox" id="analytics" name="analytics" /> <label for="analytics"> <strong>Analytics</strong> <span>Help us understand how you use our site.</span> </label> </div> <!-- Marketing - requires consent --> <div class="consent-category"> <input type="checkbox" id="marketing" name="marketing" /> <label for="marketing"> <strong>Marketing</strong> <span>Personalized ads based on your interests.</span> </label> </div> <div class="consent-actions"> <button type="button" id="accept-all">Accept All</button> <button type="button" id="reject-all">Reject All</button> <button type="submit">Save Preferences</button> </div> <p class="consent-links"> <a href="/privacy-policy">Privacy Policy</a> | <a href="/cookie-policy">Cookie Policy</a> </p> </form> </div> ``` ### Pattern 2: Data Subject Access Request (DSAR) ```python from datetime import datetime, timedelta from typing import Dict, List, Optional import json class DSARHandler: """Handle Data Subject Access Requests.""" RESPONSE_DEADLINE_DAYS = 30 EXTENSION_ALLOWED_DAYS = 60 # For complex requests def __init__(self, data_sources: List['DataSource']): self.data_sources = data_sources async def submit_request( self, request_type: str, # 'access', 'erasure', 'rectification', 'portability' user_id: str, verified: bool, details: Optional[Dict] = None ) -> str: """Submit a new DSAR.""" request = { 'id': self.generate_request_id(), 'type': request_type, 'user_id': user_id, 'status': 'pending_verification' if not verified else 'processing', 'submitted_at': datetime.utcnow(), 'deadline': datetime.utcnow() + timedelta(days=self.RESPONSE_DEADLINE_DAYS), 'details': details or {}, 'audit_log': [{ 'action': 'submitted', 'timestamp': datetime.utcnow(), 'details': 'Request received' }] } await self.db.dsar_requests.insert_one(request) await self.notify_dpo(request) return request['id'] async def process_access_request(self, request_id: str) -> Dict: """Process a data access request.""" request = await self.get_request(request_id) if request['type'] != 'access': raise ValueError("Not an access request") # Collect data from all sources user_data = {} for source in self.data_sources: try: data = await source.get_user_data(request['user_id']) user_data[source.name] = data except Exception as e: user_data[source.name] = {'error': str(e)} # Format response response = { 'request_id': request_id, 'generated_at': datetime.utcnow().isoformat(), 'data_categories': list(user_data.keys()), 'data': user_data, 'retention_info': await self.get_retention_info(), 'processing_purposes': await self.get_processing_purposes(), 'third_party_recipients': await self.get_recipients() } # Update request status await self.update_request(request_id, 'completed', response) return response async def process_erasure_request(self, request_id: str) -> Dict: """Process a right to erasure request.""" request = await self.get_request(request_id) if request['type'] != 'erasure': raise ValueError("Not an erasure request") results = {} exceptions = [] for source in self.data_sources: try: # Check for legal exceptions can_delete, reason = await source.can_delete(request['user_id']) if can_delete: await source.delete_user_data(request['user_id']) results[source.name] = 'deleted' else: exceptions.append({ 'source': source.name, 'reason': reason # e.g., 'legal retention requirement' }) results[source.name] = f'retained: {reason}' except Exception as e: results[source.name] = f'error: {str(e)}' response = { 'request_id': request_id, 'completed_at': datetime.utcnow().isoformat(), 'results': results, 'exceptions': exceptions } await self.update_request(request_id, 'completed', response) return response async def process_portability_request(self, request_id: str) -> bytes: """Generate portable data export.""" request = await self.get_request(request_id) user_data = await self.process_access_request(request_id) # Convert to machine-readable format (JSON) portable_data = { 'export_date': datetime.utcnow().isoformat(), 'format_version': '1.0', 'data': user_data['data'] } return json.dumps(portable_data, indent=2, default=str).encode() ``` ### Pattern 3: Data Retention ```python from datetime import datetime, timedelta from enum import Enum class RetentionBasis(Enum): CONSENT = "consent" CONTRACT = "contract" LEGAL_OBLIGATION = "legal_obligation" LEGITIMATE_INTEREST = "legitimate_interest" class DataRetentionPolicy: """Define and enforce data retention policies.""" POLICIES = { 'user_account': { 'retention_period_days': 365 * 3, # 3 years after last activity 'basis': RetentionBasis.CONTRACT, 'trigger': 'last_activity_date', 'archive_before_delete': True }, 'transaction_records': { 'retention_period_days': 365 * 7, # 7 years for tax 'basis': RetentionBasis.LEGAL_OBLIGATION, 'trigger': 'transaction_date', 'archive_before_delete': True, 'legal_reference': 'Tax regulations require 7 year retention' }, 'marketing_consent': { 'retention_period_days': 365 * 2, # 2 years 'basis': RetentionBasis.CONSENT, 'trigger': 'consent_date', 'archive_before_delete': False }, 'support_tickets': { 'retention_period_days': 365 * 2, 'basis': RetentionBasis.LEGITIMATE_INTEREST, 'trigger': 'ticket_closed_date', 'archive_before_delete': True }, 'analytics_data': { 'retention_period_days': 365, # 1 year 'basis': RetentionBasis.CONSENT, 'trigger': 'collection_date', 'archive_before_delete': False, 'anonymize_instead': True } } async def apply_retention_policies(self): """Run retention policy enforcement.""" for data_type, policy in self.POLICIES.items(): cutoff_date = datetime.utcnow() - timedelta( days=policy['retention_period_days'] ) if policy.get('anonymize_instead'): await self.anonymize_old_data(data_type, cutoff_date) else: if policy.get('archive_before_delete'): await self.archive_data(data_type, cutoff_date) await self.delete_old_data(data_type, cutoff_date) await self.log_retention_action(data_type, cutoff_date) async def anonymize_old_data(self, data_type: str, before_date: datetime): """Anonymize data instead of deleting.""" # Example: Replace identifying fields with hashes if data_type == 'analytics_data': await self.db.analytics.update_many( {'collection_date': {'$lt': before_date}}, {'$set': { 'user_id': None, 'ip_address': None, 'device_id': None, 'anonymized': True, 'anonymized_date': datetime.utcnow() }} ) ``` ### Pattern 4: Privacy by Design ```python class PrivacyFirstDataModel: """Example of privacy-by-design data model.""" # Separate PII from behavioral data user_profile_schema = { 'user_id': str, # UUID, not sequential 'email_hash': str, # Hashed for lookups 'created_at': datetime, # Minimal data collection 'preferences': { 'language': str, 'timezone': str } } # Encrypted at rest user_pii_schema = { 'user_id': str, 'email': str, # Encrypted 'name': str, # Encrypted 'phone': str, # Encrypted (optional) 'address': dict, # Encrypted (optional) 'encryption_key_id': str } # Pseudonymized behavioral data analytics_schema = { 'session_id': str, # Not linked to user_id 'pseudonym_id': str, # Rotating pseudonym 'events': list, 'device_category': str, # Generalized, not specific 'country': str, # Not city-level } class DataMinimization: """Implement data minimization principles.""" @staticmethod def collect_only_needed(form_data: dict, purpose: str) -> dict: """Filter form data to only fields needed for purpose.""" REQUIRED_FIELDS = { 'account_creation': ['email', 'password'], 'newsletter': ['email'], 'purchase': ['email', 'name', 'address', 'payment'], 'support': ['email', 'message'] } allowed = REQUIRED_FIELDS.get(purpose, []) return {k: v for k, v in form_data.items() if k in allowed} @staticmethod def generalize_location(ip_address: str) -> str: """Generalize IP to country level only.""" import geoip2.database reader = geoip2.database.Reader('GeoLite2-Country.mmdb') try: response = reader.country(ip_address) return response.country.iso_code except: return 'UNKNOWN' ``` ### Pattern 5: Breach Notification ```python from datetime import datetime from enum import Enum class BreachSeverity(Enum): LOW = "low" MEDIUM = "medium" HIGH = "high" CRITICAL = "critical" class BreachNotificationHandler: """Handle GDPR breach notification requirements.""" AUTHORITY_NOTIFICATION_HOURS = 72 AFFECTED_NOTIFICATION_REQUIRED_SEVERITY = BreachSeverity.HIGH async def report_breach( self, description: str, data_types: List[str], affected_count: int, severity: BreachSeverity ) -> dict: """Report and handle a data breach.""" breach = { 'id': self.generate_breach_id(), 'reported_at': datetime.utcnow(), 'description': description, 'data_types_affected': data_types, 'affected_individuals_count': affected_count, 'severity': severity.value, 'status': 'investigating', 'timeline': [{ 'event': 'breach_reported', 'timestamp': datetime.utcnow(), 'details': description }] } await self.db.breaches.insert_one(breach) # Immediate notifications await self.notify_dpo(breach) await self.notify_security_team(breach) # Authority notification required within 72 hours if self.requires_authority_notification(severity, data_types): breach['authority_notification_deadline'] = ( datetime.utcnow() + timedelta(hours=self.AUTHORITY_NOTIFICATION_HOURS) ) await self.schedule_authority_notification(breach) # Affected individuals notification if severity.value in [BreachSeverity.HIGH.value, BreachSeverity.CRITICAL.value]: await self.schedule_individual_notifications(breach) return breach def requires_authority_notification( self, severity: BreachSeverity, data_types: List[str] ) -> bool: """Determine if supervisory authority must be notified.""" # Always notify for sensitive data sensitive_types = ['health', 'financial', 'credentials', 'biometric'] if any(t in sensitive_types for t in data_types): return True # Notify for medium+ severity return severity in [BreachSeverity.MEDIUM, BreachSeverity.HIGH, BreachSeverity.CRITICAL] async def generate_authority_report(self, breach_id: str) -> dict: """Generate report for supervisory authority.""" breach = await self.get_breach(breach_id) return { 'organization': { 'name': self.config.org_name, 'contact': self.config.dpo_contact, 'registration': self.config.registration_number }, 'breach': { 'nature': breach['description'], 'categories_affected': breach['data_types_affected'], 'approximate_number_affected': breach['affected_individuals_count'], 'likely_consequences': self.assess_consequences(breach), 'measures_taken': await self.get_remediation_measures(breach_id), 'measures_proposed': await self.get_proposed_measures(breach_id) }, 'timeline': breach['timeline'], 'submitted_at': datetime.utcnow().isoformat() } ``` ## Compliance Checklist ```markdown ## GDPR Implementation Checklist ### Legal Basis - [ ] Documented legal basis for each processing activity - [ ] Consent mechanisms meet GDPR requirements - [ ] Legitimate interest assessments completed ### Transparency - [ ] Privacy policy is clear and accessible - [ ] Processing purposes clearly stated - [ ] Data retention periods documented ### Data Subject Rights - [ ] Access request process implemented - [ ] Erasure request process implemented - [ ] Portability export available - [ ] Rectification process available - [ ] Response within 30-day deadline ### Security - [ ] Encryption at rest implemented - [ ] Encryption in transit (TLS) - [ ] Access controls in place - [ ] Audit logging enabled ### Breach Response - [ ] Breach detection mechanisms - [ ] 72-hour notification process - [ ] Breach documentation system ### Documentation - [ ] Records of processing activities (Art. 30) - [ ] Data protection impact assessments - [ ] Data processing agreements with vendors ``` ## Best Practices ### Do's - **Minimize data collection** - Only collect what's needed - **Document everything** - Processing activities, legal bases - **Encrypt PII** - At rest and in transit - **Implement access controls** - Need-to-know basis - **Regular audits** - Verify compliance continuously ### Don'ts - **Don't pre-check consent boxes** - Must be opt-in - **Don't bundle consent** - Separate purposes separately - **Don't retain indefinitely** - Defi
👍0
👁️0
🤖 Auto-discovered
🤖system prompt•7 months ago

on-call-handoff-patterns

Master on-call shift handoffs with context transfer, escalation

coding
⭐1
# On-Call Handoff Patterns Effective patterns for on-call shift transitions, ensuring continuity, context transfer, and reliable incident response across shifts. ## When to Use This Skill - Transitioning on-call responsibilities - Writing shift handoff summaries - Documenting ongoing investigations - Establishing on-call rotation procedures - Improving handoff quality - Onboarding new on-call engineers ## Core Concepts ### 1. Handoff Components | Component | Purpose | | -------------------------- | ----------------------- | | **Active Incidents** | What's currently broken | | **Ongoing Investigations** | Issues being debugged | | **Recent Changes** | Deployments, configs | | **Known Issues** | Workarounds in place | | **Upcoming Events** | Maintenance, releases | ### 2. Handoff Timing ``` Recommended: 30 min overlap between shifts Outgoing: ├── 15 min: Write handoff document └── 15 min: Sync call with incoming Incoming: ├── 15 min: Review handoff document ├── 15 min: Sync call with outgoing └── 5 min: Verify alerting setup ``` ## Templates ### Template 1: Shift Handoff Document ````markdown # On-Call Handoff: Platform Team **Outgoing**: @alice (2024-01-15 to 2024-01-22) **Incoming**: @bob (2024-01-22 to 2024-01-29) **Handoff Time**: 2024-01-22 09:00 UTC --- ## 🔴 Active Incidents ### None currently active No active incidents at handoff time. --- ## 🟡 Ongoing Investigations ### 1. Intermittent API Timeouts (ENG-1234) **Status**: Investigating **Started**: 2024-01-20 **Impact**: ~0.1% of requests timing out **Context**: - Timeouts correlate with database backup window (02:00-03:00 UTC) - Suspect backup process causing lock contention - Added extra logging in PR #567 (deployed 01/21) **Next Steps**: - [ ] Review new logs after tonight's backup - [ ] Consider moving backup window if confirmed **Resources**: - Dashboard: [API Latency](https://grafana/d/api-latency) - Thread: #platform-eng (01/20, 14:32) --- ### 2. Memory Growth in Auth Service (ENG-1235) **Status**: Monitoring **Started**: 2024-01-18 **Impact**: None yet (proactive) **Context**: - Memory usage growing ~5% per day - No memory leak found in profiling - Suspect connection pool not releasing properly **Next Steps**: - [ ] Review heap dump from 01/21 - [ ] Consider restart if usage > 80% **Resources**: - Dashboard: [Auth Service Memory](https://grafana/d/auth-memory) - Analysis doc: [Memory Investigation](https://docs/eng-1235) --- ## 🟢 Resolved This Shift ### Payment Service Outage (2024-01-19) - **Duration**: 23 minutes - **Root Cause**: Database connection exhaustion - **Resolution**: Rolled back v2.3.4, increased pool size - **Postmortem**: [POSTMORTEM-89](https://docs/postmortem-89) - **Follow-up tickets**: ENG-1230, ENG-1231 --- ## 📋 Recent Changes ### Deployments | Service | Version | Time | Notes | | ------------ | ------- | ----------- | -------------------------- | | api-gateway | v3.2.1 | 01/21 14:00 | Bug fix for header parsing | | user-service | v2.8.0 | 01/20 10:00 | New profile features | | auth-service | v4.1.2 | 01/19 16:00 | Security patch | ### Configuration Changes - 01/21: Increased API rate limit from 1000 to 1500 RPS - 01/20: Updated database connection pool max from 50 to 75 ### Infrastructure - 01/20: Added 2 nodes to Kubernetes cluster - 01/19: Upgraded Redis from 6.2 to 7.0 --- ## ⚠️ Known Issues & Workarounds ### 1. Slow Dashboard Loading **Issue**: Grafana dashboards slow on Monday mornings **Workaround**: Wait 5 min after 08:00 UTC for cache warm-up **Ticket**: OPS-456 (P3) ### 2. Flaky Integration Test **Issue**: `test_payment_flow` fails intermittently in CI **Workaround**: Re-run failed job (usually passes on retry) **Ticket**: ENG-1200 (P2) --- ## 📅 Upcoming Events | Date | Event | Impact | Contact | | ----------- | -------------------- | ------------------- | ------------- | | 01/23 02:00 | Database maintenance | 5 min read-only | @dba-team | | 01/24 14:00 | Major release v5.0 | Monitor closely | @release-team | | 01/25 | Marketing campaign | 2x traffic expected | @platform | --- ## 📞 Escalation Reminders | Issue Type | First Escalation | Second Escalation | | --------------- | -------------------- | ----------------- | | Payment issues | @payments-oncall | @payments-manager | | Auth issues | @auth-oncall | @security-team | | Database issues | @dba-team | @infra-manager | | Unknown/severe | @engineering-manager | @vp-engineering | --- ## 🔧 Quick Reference ### Common Commands ```bash # Check service health kubectl get pods -A | grep -v Running # Recent deployments kubectl get events --sort-by='.lastTimestamp' | tail -20 # Database connections psql -c "SELECT count(*) FROM pg_stat_activity;" # Clear cache (emergency only) redis-cli FLUSHDB ``` ```` ### Important Links - [Runbooks](https://wiki/runbooks) - [Service Catalog](https://wiki/services) - [Incident Slack](https://slack.com/incidents) - [PagerDuty](https://pagerduty.com/schedules) --- ## Handoff Checklist ### Outgoing Engineer - [x] Document active incidents - [x] Document ongoing investigations - [x] List recent changes - [x] Note known issues - [x] Add upcoming events - [x] Sync with incoming engineer ### Incoming Engineer - [ ] Read this document - [ ] Join sync call - [ ] Verify PagerDuty is routing to you - [ ] Verify Slack notifications working - [ ] Check VPN/access working - [ ] Review critical dashboards ```` ### Template 2: Quick Handoff (Async) ```markdown # Quick Handoff: @alice → @bob ## TL;DR - No active incidents - 1 investigation ongoing (API timeouts, see ENG-1234) - Major release tomorrow (01/24) - be ready for issues ## Watch List 1. API latency around 02:00-03:00 UTC (backup window) 2. Auth service memory (restart if > 80%) ## Recent - Deployed api-gateway v3.2.1 yesterday (stable) - Increased rate limits to 1500 RPS ## Coming Up - 01/23 02:00 - DB maintenance (5 min read-only) - 01/24 14:00 - v5.0 release ## Questions? I'll be available on Slack until 17:00 today. ```` ### Template 3: Incident Handoff (Mid-Incident) ```markdown # INCIDENT HANDOFF: Payment Service Degradation **Incident Start**: 2024-01-22 08:15 UTC **Current Status**: Mitigating **Severity**: SEV2 --- ## Current State - Error rate: 15% (down from 40%) - Mitigation in progress: scaling up pods - ETA to resolution: ~30 min ## What We Know 1. Root cause: Memory pressure on payment-service pods 2. Triggered by: Unusual traffic spike (3x normal) 3. Contributing: Inefficient query in checkout flow ## What We've Done - Scaled payment-service from 5 → 15 pods - Enabled rate limiting on checkout endpoint - Disabled non-critical features ## What Needs to Happen 1. Monitor error rate - should reach <1% in ~15 min 2. If not improving, escalate to @payments-manager 3. Once stable, begin root cause investigation ## Key People - Incident Commander: @alice (handing off) - Comms Lead: @charlie - Technical Lead: @bob (incoming) ## Communication - Status page: Updated at 08:45 - Customer support: Notified - Exec team: Aware ## Resources - Incident channel: #inc-20240122-payment - Dashboard: [Payment Service](https://grafana/d/payments) - Runbook: [Payment Degradation](https://wiki/runbooks/payments) --- **Incoming on-call (@bob) - Please confirm you have:** - [ ] Joined #inc-20240122-payment - [ ] Access to dashboards - [ ] Understand current state - [ ] Know escalation path ``` ## Handoff Sync Meeting ### Agenda (15 minutes) ```markdown ## Handoff Sync: @alice → @bob 1. **Active Issues** (5 min) - Walk through any ongoing incidents - Discuss investigation status - Transfer context and theories 2. **Recent Changes** (3 min) - Deployments to watch - Config changes - Known regressions 3. **Upcoming Events** (3 min) - Maintenance windows - Expected traffic changes - Releases planned 4. **Questions** (4 min) - Clarify anything unclear - Confirm access and alerting - Exchange contact info ``` ## On-Call Best Practices ### Before Your Shift ```markdown ## Pre-Shift Checklist ### Access Verification - [ ] VPN working - [ ] kubectl access to all clusters - [ ] Database read access - [ ] Log aggregator access (Splunk/Datadog) - [ ] PagerDuty app installed and logged in ### Alerting Setup - [ ] PagerDuty schedule shows you as primary - [ ] Phone notifications enabled - [ ] Slack notifications for incident channels - [ ] Test alert received and acknowledged ### Knowledge Refresh - [ ] Review recent incidents (past 2 weeks) - [ ] Check service changelog - [ ] Skim critical runbooks - [ ] Know escalation contacts ### Environment Ready - [ ] Laptop charged and accessible - [ ] Phone charged - [ ] Quiet space available for calls - [ ] Secondary contact identified (if traveling) ``` ### During Your Shift ```markdown ## Daily On-Call Routine ### Morning (start of day) - [ ] Check overnight alerts - [ ] Review dashboards for anomalies - [ ] Check for any P0/P1 tickets created - [ ] Skim incident channels for context ### Throughout Day - [ ] Respond to alerts within SLA - [ ] Document investigation progress - [ ] Update team on significant issues - [ ] Triage incoming pages ### End of Day - [ ] Hand off any active issues - [ ] Update investigation docs - [ ] Note anything for next shift ``` ### After Your Shift ```markdown ## Post-Shift Checklist - [ ] Complete handoff document - [ ] Sync with incoming on-call - [ ] Verify PagerDuty routing changed - [ ] Close/update investigation tickets - [ ] File postmortems for any incidents - [ ] Take time off if shift was stressful ``` ## Escalation Guidelines ### When to Escalate ```markdown ## Escalation Triggers ### Immediate Escalation - SEV1 incident declared - Data breach suspected - Unable to diagnose within 30 min - Customer or legal escalation received ### Consider Escalation - Issue spans multiple teams - Requires expertise you don't have - Business impact exceeds threshold - You're uncertain about next steps ### How to Escalate 1. Page the appropriate escalation path 2. Provide brief context in Slack 3. Stay engaged until escalation acknowledges 4. Hand off cleanly, don't just disappear ``` ## Best Practices ### Do's - **Document everything** - Future you will thank you - **Escalate early** - Better safe than sorry - **Take breaks** - Alert fatigue is real - **Keep handoffs synchronous** - Async loses context - **Test your setup** - Before incidents, not during ### Don'ts - **Don't skip handoffs** - Context loss causes incidents - **Don't hero** - Escalate when needed - **Don't ignore alerts** - Even if they seem minor - **Don't work sick** - Swap shifts instead - **Don't disappear** - Stay reachable during shift ## Resources - [Google SRE - Being On-Call](https://sre.google/sre-book/being-on-call/) - [PagerDuty On-Call Guide](https://www.pagerduty.com/resources/learn/on-call-management/) - [Increment On-Call Issue](https://increment.com/on-call/)
👍0
👁️0
🤖 Auto-discovered
🤖system prompt•7 months ago

paypal-integration

Integrate PayPal payment processing with support for express

business
⭐1
# PayPal Integration Master PayPal payment integration including Express Checkout, IPN handling, recurring billing, and refund workflows. ## When to Use This Skill - Integrating PayPal as a payment option - Implementing express checkout flows - Setting up recurring billing with PayPal - Processing refunds and payment disputes - Handling PayPal webhooks (IPN) - Supporting international payments - Implementing PayPal subscriptions ## Core Concepts ### 1. Payment Products **PayPal Checkout** - One-time payments - Express checkout experience - Guest and PayPal account payments **PayPal Subscriptions** - Recurring billing - Subscription plans - Automatic renewals **PayPal Payouts** - Send money to multiple recipients - Marketplace and platform payments ### 2. Integration Methods **Client-Side (JavaScript SDK)** - Smart Payment Buttons - Hosted payment flow - Minimal backend code **Server-Side (REST API)** - Full control over payment flow - Custom checkout UI - Advanced features ### 3. IPN (Instant Payment Notification) - Webhook-like payment notifications - Asynchronous payment updates - Verification required ## Quick Start ```javascript // Frontend - PayPal Smart Buttons <div id="paypal-button-container"></div> <script src="https://www.paypal.com/sdk/js?client-id=YOUR_CLIENT_ID&currency=USD"></script> <script> paypal.Buttons({ createOrder: function(data, actions) { return actions.order.create({ purchase_units: [{ amount: { value: '25.00' } }] }); }, onApprove: function(data, actions) { return actions.order.capture().then(function(details) { // Payment successful console.log('Transaction completed by ' + details.payer.name.given_name); // Send to backend for verification fetch('/api/paypal/capture', { method: 'POST', headers: {'Content-Type': 'application/json'}, body: JSON.stringify({orderID: data.orderID}) }); }); } }).render('#paypal-button-container'); </script> ``` ```python # Backend - Verify and capture order from paypalrestsdk import Payment import paypalrestsdk paypalrestsdk.configure({ "mode": "sandbox", # or "live" "client_id": "YOUR_CLIENT_ID", "client_secret": "YOUR_CLIENT_SECRET" }) def capture_paypal_order(order_id): """Capture a PayPal order.""" payment = Payment.find(order_id) if payment.execute({"payer_id": payment.payer.payer_info.payer_id}): # Payment successful return { 'status': 'success', 'transaction_id': payment.id, 'amount': payment.transactions[0].amount.total } else: # Payment failed return { 'status': 'failed', 'error': payment.error } ``` ## Express Checkout Implementation ### Server-Side Order Creation ```python import requests import json class PayPalClient: def __init__(self, client_id, client_secret, mode='sandbox'): self.client_id = client_id self.client_secret = client_secret self.base_url = 'https://api-m.sandbox.paypal.com' if mode == 'sandbox' else 'https://api-m.paypal.com' self.access_token = self.get_access_token() def get_access_token(self): """Get OAuth access token.""" url = f"{self.base_url}/v1/oauth2/token" headers = {"Accept": "application/json", "Accept-Language": "en_US"} response = requests.post( url, headers=headers, data={"grant_type": "client_credentials"}, auth=(self.client_id, self.client_secret) ) return response.json()['access_token'] def create_order(self, amount, currency='USD'): """Create a PayPal order.""" url = f"{self.base_url}/v2/checkout/orders" headers = { "Content-Type": "application/json", "Authorization": f"Bearer {self.access_token}" } payload = { "intent": "CAPTURE", "purchase_units": [{ "amount": { "currency_code": currency, "value": str(amount) } }] } response = requests.post(url, headers=headers, json=payload) return response.json() def capture_order(self, order_id): """Capture payment for an order.""" url = f"{self.base_url}/v2/checkout/orders/{order_id}/capture" headers = { "Content-Type": "application/json", "Authorization": f"Bearer {self.access_token}" } response = requests.post(url, headers=headers) return response.json() def get_order_details(self, order_id): """Get order details.""" url = f"{self.base_url}/v2/checkout/orders/{order_id}" headers = { "Authorization": f"Bearer {self.access_token}" } response = requests.get(url, headers=headers) return response.json() ``` ## IPN (Instant Payment Notification) Handling ### IPN Verification and Processing ```python from flask import Flask, request import requests from urllib.parse import parse_qs app = Flask(__name__) @app.route('/ipn', methods=['POST']) def handle_ipn(): """Handle PayPal IPN notifications.""" # Get IPN message ipn_data = request.form.to_dict() # Verify IPN with PayPal if not verify_ipn(ipn_data): return 'IPN verification failed', 400 # Process IPN based on transaction type payment_status = ipn_data.get('payment_status') txn_type = ipn_data.get('txn_type') if payment_status == 'Completed': handle_payment_completed(ipn_data) elif payment_status == 'Refunded': handle_refund(ipn_data) elif payment_status == 'Reversed': handle_chargeback(ipn_data) return 'IPN processed', 200 def verify_ipn(ipn_data): """Verify IPN message authenticity.""" # Add 'cmd' parameter verify_data = ipn_data.copy() verify_data['cmd'] = '_notify-validate' # Send back to PayPal for verification paypal_url = 'https://ipnpb.sandbox.paypal.com/cgi-bin/webscr' # or production URL response = requests.post(paypal_url, data=verify_data) return response.text == 'VERIFIED' def handle_payment_completed(ipn_data): """Process completed payment.""" txn_id = ipn_data.get('txn_id') payer_email = ipn_data.get('payer_email') mc_gross = ipn_data.get('mc_gross') item_name = ipn_data.get('item_name') # Check if already processed (prevent duplicates) if is_transaction_processed(txn_id): return # Update database # Send confirmation email # Fulfill order print(f"Payment completed: {txn_id}, Amount: ${mc_gross}") def handle_refund(ipn_data): """Handle refund.""" parent_txn_id = ipn_data.get('parent_txn_id') mc_gross = ipn_data.get('mc_gross') # Process refund in your system print(f"Refund processed: {parent_txn_id}, Amount: ${mc_gross}") def handle_chargeback(ipn_data): """Handle payment reversal/chargeback.""" txn_id = ipn_data.get('txn_id') reason_code = ipn_data.get('reason_code') # Handle chargeback print(f"Chargeback: {txn_id}, Reason: {reason_code}") ``` ## Subscription/Recurring Billing ### Create Subscription Plan ```python def create_subscription_plan(name, amount, interval='MONTH'): """Create a subscription plan.""" client = PayPalClient(CLIENT_ID, CLIENT_SECRET) url = f"{client.base_url}/v1/billing/plans" headers = { "Content-Type": "application/json", "Authorization": f"Bearer {client.access_token}" } payload = { "product_id": "PRODUCT_ID", # Create product first "name": name, "billing_cycles": [{ "frequency": { "interval_unit": interval, "interval_count": 1 }, "tenure_type": "REGULAR", "sequence": 1, "total_cycles": 0, # Infinite "pricing_scheme": { "fixed_price": { "value": str(amount), "currency_code": "USD" } } }], "payment_preferences": { "auto_bill_outstanding": True, "setup_fee": { "value": "0", "currency_code": "USD" }, "setup_fee_failure_action": "CONTINUE", "payment_failure_threshold": 3 } } response = requests.post(url, headers=headers, json=payload) return response.json() def create_subscription(plan_id, subscriber_email): """Create a subscription for a customer.""" client = PayPalClient(CLIENT_ID, CLIENT_SECRET) url = f"{client.base_url}/v1/billing/subscriptions" headers = { "Content-Type": "application/json", "Authorization": f"Bearer {client.access_token}" } payload = { "plan_id": plan_id, "subscriber": { "email_address": subscriber_email }, "application_context": { "return_url": "https://yourdomain.com/subscription/success", "cancel_url": "https://yourdomain.com/subscription/cancel" } } response = requests.post(url, headers=headers, json=payload) subscription = response.json() # Get approval URL for link in subscription.get('links', []): if link['rel'] == 'approve': return { 'subscription_id': subscription['id'], 'approval_url': link['href'] } ``` ## Refund Workflows ```python def create_refund(capture_id, amount=None, note=None): """Create a refund for a captured payment.""" client = PayPalClient(CLIENT_ID, CLIENT_SECRET) url = f"{client.base_url}/v2/payments/captures/{capture_id}/refund" headers = { "Content-Type": "application/json", "Authorization": f"Bearer {client.access_token}" } payload = {} if amount: payload["amount"] = { "value": str(amount), "currency_code": "USD" } if note: payload["note_to_payer"] = note response = requests.post(url, headers=headers, json=payload) return response.json() def get_refund_details(refund_id): """Get refund details.""" client = PayPalClient(CLIENT_ID, CLIENT_SECRET) url = f"{client.base_url}/v2/payments/refunds/{refund_id}" headers = { "Authorization": f"Bearer {client.access_token}" } response = requests.get(url, headers=headers) return response.json() ``` ## Error Handling ```python class PayPalError(Exception): """Custom PayPal error.""" pass def handle_paypal_api_call(api_function): """Wrapper for PayPal API calls with error handling.""" try: result = api_function() return result except requests.exceptions.RequestException as e: # Network error raise PayPalError(f"Network error: {str(e)}") except Exception as e: # Other errors raise PayPalError(f"PayPal API error: {str(e)}") # Usage try: order = handle_paypal_api_call(lambda: client.create_order(25.00)) except PayPalError as e: # Handle error appropriately log_error(e) ``` ## Testing ```python # Use sandbox credentials SANDBOX_CLIENT_ID = "..." SANDBOX_SECRET = "..." # Test accounts # Create test buyer and seller accounts at developer.paypal.com def test_payment_flow(): """Test complete payment flow.""" client = PayPalClient(SANDBOX_CLIENT_ID, SANDBOX_SECRET, mode='sandbox') # Create order order = client.create_order(10.00) assert 'id' in order # Get approval URL approval_url = next((link['href'] for link in order['links'] if link['rel'] == 'approve'), None) assert approval_url is not None # After approval (manual step with test account) # Capture order # captured = client.capture_order(order['id']) # assert captured['status'] == 'COMPLETED' ``` ## Resources - **references/express-checkout.md**: Express Checkout implementation guide - **references/ipn-handling.md**: IPN verification and processing - **references/refund-workflows.md**: Refund handling patterns - **references/billing-agreements.md**: Recurring billing setup - **assets/paypal-client.py**: Production PayPal client - **assets/ipn-processor.py**: IPN webhook processor - **assets/recurring-billing.py**: Subscription management ## Best Practices 1. **Always Verify IPN**: Never trust IPN without verification 2. **Idempotent Processing**: Handle duplicate IPN notifications 3. **Error Handling**: Implement robust error handling 4. **Logging**: Log all transactions and errors 5. **Test Thoroughly**: Use sandbox extensively 6. **Webhook Backup**: Don't rely solely on client-side callbacks 7. **Currency Handling**: Always specify currency explicitly ## Common Pitfalls - **Not Verifying IPN**: Accepting IPN without verification - **Duplicate Processing**: Not checking for duplicate transactions - **Wrong Environment**: Mixing sandbox and production URLs/credentials - **Missing Webhooks**: Not handling all payment states - **Hardcoded Values**: Not making configurable for different environments
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🤖 Auto-discovered
🤖system prompt•7 months ago

stripe-integration

Implement Stripe payment processing for robust, PCI-compliant

business
⭐1
# Stripe Integration Master Stripe payment processing integration for robust, PCI-compliant payment flows including checkout, subscriptions, webhooks, and refunds. ## When to Use This Skill - Implementing payment processing in web/mobile applications - Setting up subscription billing systems - Handling one-time payments and recurring charges - Processing refunds and disputes - Managing customer payment methods - Implementing SCA (Strong Customer Authentication) for European payments - Building marketplace payment flows with Stripe Connect ## Core Concepts ### 1. Payment Flows **Checkout Sessions** - Recommended for most integrations - Supports all UI paths: - Stripe-hosted checkout page - Embedded checkout form - Custom UI with Elements (Payment Element, Express Checkout Element) using `ui_mode='custom'` - Provides built-in checkout capabilities (line items, discounts, tax, shipping, address collection, saved payment methods, and checkout lifecycle events) - Lower integration and maintenance burden than Payment Intents **Payment Intents (Bespoke control)** - You calculate the final amount with taxes, discounts, subscriptions, and currency conversion yourself. - More complex implementation and long-term maintenance burden - Requires Stripe.js for PCI compliance **Setup Intents (Save Payment Methods)** - Collect payment method without charging - Used for subscriptions and future payments - Requires customer confirmation ### 2. Webhooks **Critical Events:** - `payment_intent.succeeded`: Payment completed - `payment_intent.payment_failed`: Payment failed - `customer.subscription.updated`: Subscription changed - `customer.subscription.deleted`: Subscription canceled - `charge.refunded`: Refund processed - `invoice.payment_succeeded`: Subscription payment successful ### 3. Subscriptions **Components:** - **Product**: What you're selling - **Price**: How much and how often - **Subscription**: Customer's recurring payment - **Invoice**: Generated for each billing cycle ### 4. Customer Management - Create and manage customer records - Store multiple payment methods - Track customer metadata - Manage billing details ## Quick Start ```python import stripe stripe.api_key = "sk_test_..." # Create a checkout session session = stripe.checkout.Session.create( line_items=[{ 'price_data': { 'currency': 'usd', 'product_data': { 'name': 'Premium Subscription', }, 'unit_amount': 2000, # $20.00 'recurring': { 'interval': 'month', }, }, 'quantity': 1, }], mode='subscription', success_url='https://yourdomain.com/success?session_id={CHECKOUT_SESSION_ID}', cancel_url='https://yourdomain.com/cancel' ) # Redirect user to session.url print(session.url) ``` ## Payment Implementation Patterns ### Pattern 1: One-Time Payment (Hosted Checkout) ```python def create_checkout_session(amount, currency='usd'): """Create a one-time payment checkout session.""" try: session = stripe.checkout.Session.create( line_items=[{ 'price_data': { 'currency': currency, 'product_data': { 'name': 'Blue T-shirt', 'images': ['https://example.com/product.jpg'], }, 'unit_amount': amount, # Amount in cents }, 'quantity': 1, }], mode='payment', success_url='https://yourdomain.com/success?session_id={CHECKOUT_SESSION_ID}', cancel_url='https://yourdomain.com/cancel', metadata={ 'order_id': 'order_123', 'user_id': 'user_456' } ) return session except stripe.error.StripeError as e: # Handle error print(f"Stripe error: {e.user_message}") raise ``` ### Pattern 2: Elements with Checkout Sessions ```python def create_checkout_session_for_elements(amount, currency='usd'): """Create a checkout session configured for Payment Element.""" session = stripe.checkout.Session.create( mode='payment', ui_mode='custom', line_items=[{ 'price_data': { 'currency': currency, 'product_data': {'name': 'Blue T-shirt'}, 'unit_amount': amount, }, 'quantity': 1, }], return_url='https://yourdomain.com/complete?session_id={CHECKOUT_SESSION_ID}' ) return session.client_secret # Send to frontend ``` ```javascript const stripe = Stripe("pk_test_..."); const appearance = { theme: "stripe" }; const checkout = stripe.initCheckout({ clientSecret, elementsOptions: { appearance }, }); const loadActionsResult = await checkout.loadActions(); if (loadActionsResult.type === "success") { const { actions } = loadActionsResult; const session = actions.getSession(); const button = document.getElementById("pay-button"); const checkoutContainer = document.getElementById("checkout-container"); const emailInput = document.getElementById("email"); const emailErrors = document.getElementById("email-errors"); const errors = document.getElementById("confirm-errors"); // Display a formatted string representing the total amount checkoutContainer.append(`Total: ${session.total.total.amount}`); // Mount Payment Element const paymentElement = checkout.createPaymentElement(); paymentElement.mount("#payment-element"); // Store email for submission emailInput.addEventListener("blur", () => { actions.updateEmail(emailInput.value).then((result) => { if (result.error) emailErrors.textContent = result.error.message; }); }); // Handle form submission button.addEventListener("click", () => { actions.confirm().then((result) => { if (result.type === "error") errors.textContent = result.error.message; }); }); } ``` ### Pattern 3: Elements with Payment Intents Pattern 2 (Elements with Checkout Sessions) is Stripe's recommended approach, but you can also use Payment Intents as an alternative. ```python def create_payment_intent(amount, currency='usd', customer_id=None): """Create a payment intent for bespoke checkout UI with Payment Element.""" intent = stripe.PaymentIntent.create( amount=amount, currency=currency, customer=customer_id, automatic_payment_methods={ 'enabled': True, }, metadata={ 'integration_check': 'accept_a_payment' } ) return intent.client_secret # Send to frontend ``` ```javascript // Mount Payment Element and confirm via Payment Intents const stripe = Stripe("pk_test_..."); const appearance = { theme: "stripe" }; const elements = stripe.elements({ appearance, clientSecret }); const paymentElement = elements.create("payment"); paymentElement.mount("#payment-element"); document.getElementById("pay-button").addEventListener("click", async () => { const { error } = await stripe.confirmPayment({ elements, confirmParams: { return_url: "https://yourdomain.com/complete", }, }); if (error) { document.getElementById("errors").textContent = error.message; } }); ``` ### Pattern 4: Subscription Creation ```python def create_subscription(customer_id, price_id): """Create a subscription for a customer.""" try: subscription = stripe.Subscription.create( customer=customer_id, items=[{'price': price_id}], payment_behavior='default_incomplete', payment_settings={'save_default_payment_method': 'on_subscription'}, expand=['latest_invoice.payment_intent'], ) return { 'subscription_id': subscription.id, 'client_secret': subscription.latest_invoice.payment_intent.client_secret } except stripe.error.StripeError as e: print(f"Subscription creation failed: {e}") raise ``` ### Pattern 5: Customer Portal ```python def create_customer_portal_session(customer_id): """Create a portal session for customers to manage subscriptions.""" session = stripe.billing_portal.Session.create( customer=customer_id, return_url='https://yourdomain.com/account', ) return session.url # Redirect customer here ``` ## Webhook Handling ### Secure Webhook Endpoint ```python from flask import Flask, request import stripe app = Flask(__name__) endpoint_secret = 'whsec_...' @app.route('/webhook', methods=['POST']) def webhook(): payload = request.data sig_header = request.headers.get('Stripe-Signature') try: event = stripe.Webhook.construct_event( payload, sig_header, endpoint_secret ) except ValueError: # Invalid payload return 'Invalid payload', 400 except stripe.error.SignatureVerificationError: # Invalid signature return 'Invalid signature', 400 # Handle the event if event['type'] == 'payment_intent.succeeded': payment_intent = event['data']['object'] handle_successful_payment(payment_intent) elif event['type'] == 'payment_intent.payment_failed': payment_intent = event['data']['object'] handle_failed_payment(payment_intent) elif event['type'] == 'customer.subscription.deleted': subscription = event['data']['object'] handle_subscription_canceled(subscription) return 'Success', 200 def handle_successful_payment(payment_intent): """Process successful payment.""" customer_id = payment_intent.get('customer') amount = payment_intent['amount'] metadata = payment_intent.get('metadata', {}) # Update your database # Send confirmation email # Fulfill order print(f"Payment succeeded: {payment_intent['id']}") def handle_failed_payment(payment_intent): """Handle failed payment.""" error = payment_intent.get('last_payment_error', {}) print(f"Payment failed: {error.get('message')}") # Notify customer # Update order status def handle_subscription_canceled(subscription): """Handle subscription cancellation.""" customer_id = subscription['customer'] # Update user access # Send cancellation email print(f"Subscription canceled: {subscription['id']}") ``` ### Webhook Best Practices ```python import hashlib import hmac def verify_webhook_signature(payload, signature, secret): """Manually verify webhook signature.""" expected_sig = hmac.new( secret.encode('utf-8'), payload, hashlib.sha256 ).hexdigest() return hmac.compare_digest(signature, expected_sig) def handle_webhook_idempotently(event_id, handler): """Ensure webhook is processed exactly once.""" # Check if event already processed if is_event_processed(event_id): return # Process event try: handler() mark_event_processed(event_id) except Exception as e: log_error(e) # Stripe will retry failed webhooks raise ``` ## Customer Management ```python def create_customer(email, name, payment_method_id=None): """Create a Stripe customer.""" customer = stripe.Customer.create( email=email, name=name, payment_method=payment_method_id, invoice_settings={ 'default_payment_method': payment_method_id } if payment_method_id else None, metadata={ 'user_id': '12345' } ) return customer def attach_payment_method(customer_id, payment_method_id): """Attach a payment method to a customer.""" stripe.PaymentMethod.attach( payment_method_id, customer=customer_id ) # Set as default stripe.Customer.modify( customer_id, invoice_settings={ 'default_payment_method': payment_method_id } ) def list_customer_payment_methods(customer_id): """List all payment methods for a customer.""" payment_methods = stripe.PaymentMethod.list( customer=customer_id, type='card' ) return payment_methods.data ``` ## Refund Handling ```python def create_refund(payment_intent_id, amount=None, reason=None): """Create a refund.""" refund_params = { 'payment_intent': payment_intent_id } if amount: refund_params['amount'] = amount # Partial refund if reason: refund_params['reason'] = reason # 'duplicate', 'fraudulent', 'requested_by_customer' refund = stripe.Refund.create(**refund_params) return refund def handle_dispute(charge_id, evidence): """Update dispute with evidence.""" stripe.Dispute.modify( charge_id, evidence={ 'customer_name': evidence.get('customer_name'), 'customer_email_address': evidence.get('customer_email'), 'shipping_documentation': evidence.get('shipping_proof'), 'customer_communication': evidence.get('communication'), } ) ``` ## Testing ```python # Use test mode keys stripe.api_key = "sk_test_..." # Test card numbers TEST_CARDS = { 'success': '4242424242424242', 'declined': '4000000000000002', '3d_secure': '4000002500003155', 'insufficient_funds': '4000000000009995' } def test_payment_flow(): """Test complete payment flow.""" # Create test customer customer = stripe.Customer.create( email="test@example.com" ) # Create payment intent intent = stripe.PaymentIntent.create( amount=1000, automatic_payment_methods={ 'enabled': True }, currency='usd', customer=customer.id ) # Confirm with test card confirmed = stripe.PaymentIntent.confirm( intent.id, payment_method='pm_card_visa' # Test payment method ) assert confirmed.status == 'succeeded' ``` ## Resources - **references/checkout-flows.md**: Detailed checkout implementation - **references/webhook-handling.md**: Webhook security and processing - **references/subscription-management.md**: Subscription lifecycle - **references/customer-management.md**: Customer and payment method handling - **references/invoice-generation.md**: Invoicing and billing - **assets/stripe-client.py**: Production-ready Stripe client wrapper - **assets/webhook-handler.py**: Complete webhook processor - **assets/checkout-config.json**: Checkout configuration templates ## Best Practices 1. **Always Use Webhooks**: Don't rely solely on client-side confirmation 2. **Idempotency**: Handle webhook events idempotently 3. **Error Handling**: Gracefully handle all Stripe errors 4. **Test Mode**: Thoroughly test with test keys before production 5. **Metadata**: Use metadata to link Stripe objects to your database 6. **Monitoring**: Track payment success rates and errors 7. **PCI Compliance**: Never handle raw card data on your server 8. **SCA Ready**: Implement 3D Secure for European payments ## Common Pitfalls - **Not Verifying Webhooks**: Always verify webhook signatures - **Missing Webhook Events**: Handle all relevant webhook events - **Hardcoded Amounts**: Use cents/smallest currency unit - **No Retry Logic**: Implement retries for API calls - **Ignoring Test Mode**: Test all edge cases with test cards
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👁️0
🤖 Auto-discovered
🤖system prompt•7 months ago

backtesting-frameworks

Build robust backtesting systems for trading strategies with proper

coding
⭐1
# Backtesting Frameworks Build robust, production-grade backtesting systems that avoid common pitfalls and produce reliable strategy performance estimates. ## When to Use This Skill - Developing trading strategy backtests - Building backtesting infrastructure - Validating strategy performance - Avoiding common backtesting biases - Implementing walk-forward analysis - Comparing strategy alternatives ## Core Concepts ### 1. Backtesting Biases | Bias | Description | Mitigation | | ---------------- | ------------------------- | ----------------------- | | **Look-ahead** | Using future information | Point-in-time data | | **Survivorship** | Only testing on survivors | Use delisted securities | | **Overfitting** | Curve-fitting to history | Out-of-sample testing | | **Selection** | Cherry-picking strategies | Pre-registration | | **Transaction** | Ignoring trading costs | Realistic cost models | ### 2. Proper Backtest Structure ``` Historical Data │ ▼ ┌─────────────────────────────────────────┐ │ Training Set │ │ (Strategy Development & Optimization) │ └─────────────────────────────────────────┘ │ ▼ ┌─────────────────────────────────────────┐ │ Validation Set │ │ (Parameter Selection, No Peeking) │ └─────────────────────────────────────────┘ │ ▼ ┌─────────────────────────────────────────┐ │ Test Set │ │ (Final Performance Evaluation) │ └─────────────────────────────────────────┘ ``` ### 3. Walk-Forward Analysis ``` Window 1: [Train──────][Test] Window 2: [Train──────][Test] Window 3: [Train──────][Test] Window 4: [Train──────][Test] ─────▶ Time ``` ## Implementation Patterns ### Pattern 1: Event-Driven Backtester ```python from abc import ABC, abstractmethod from dataclasses import dataclass, field from datetime import datetime from decimal import Decimal from enum import Enum from typing import Dict, List, Optional import pandas as pd import numpy as np class OrderSide(Enum): BUY = "buy" SELL = "sell" class OrderType(Enum): MARKET = "market" LIMIT = "limit" STOP = "stop" @dataclass class Order: symbol: str side: OrderSide quantity: Decimal order_type: OrderType limit_price: Optional[Decimal] = None stop_price: Optional[Decimal] = None timestamp: Optional[datetime] = None @dataclass class Fill: order: Order fill_price: Decimal fill_quantity: Decimal commission: Decimal slippage: Decimal timestamp: datetime @dataclass class Position: symbol: str quantity: Decimal = Decimal("0") avg_cost: Decimal = Decimal("0") realized_pnl: Decimal = Decimal("0") def update(self, fill: Fill) -> None: if fill.order.side == OrderSide.BUY: new_quantity = self.quantity + fill.fill_quantity if new_quantity != 0: self.avg_cost = ( (self.quantity * self.avg_cost + fill.fill_quantity * fill.fill_price) / new_quantity ) self.quantity = new_quantity else: self.realized_pnl += fill.fill_quantity * (fill.fill_price - self.avg_cost) self.quantity -= fill.fill_quantity @dataclass class Portfolio: cash: Decimal positions: Dict[str, Position] = field(default_factory=dict) def get_position(self, symbol: str) -> Position: if symbol not in self.positions: self.positions[symbol] = Position(symbol=symbol) return self.positions[symbol] def process_fill(self, fill: Fill) -> None: position = self.get_position(fill.order.symbol) position.update(fill) if fill.order.side == OrderSide.BUY: self.cash -= fill.fill_price * fill.fill_quantity + fill.commission else: self.cash += fill.fill_price * fill.fill_quantity - fill.commission def get_equity(self, prices: Dict[str, Decimal]) -> Decimal: equity = self.cash for symbol, position in self.positions.items(): if position.quantity != 0 and symbol in prices: equity += position.quantity * prices[symbol] return equity class Strategy(ABC): @abstractmethod def on_bar(self, timestamp: datetime, data: pd.DataFrame) -> List[Order]: pass @abstractmethod def on_fill(self, fill: Fill) -> None: pass class ExecutionModel(ABC): @abstractmethod def execute(self, order: Order, bar: pd.Series) -> Optional[Fill]: pass class SimpleExecutionModel(ExecutionModel): def __init__(self, slippage_bps: float = 10, commission_per_share: float = 0.01): self.slippage_bps = slippage_bps self.commission_per_share = commission_per_share def execute(self, order: Order, bar: pd.Series) -> Optional[Fill]: if order.order_type == OrderType.MARKET: base_price = Decimal(str(bar["open"])) # Apply slippage slippage_mult = 1 + (self.slippage_bps / 10000) if order.side == OrderSide.BUY: fill_price = base_price * Decimal(str(slippage_mult)) else: fill_price = base_price / Decimal(str(slippage_mult)) commission = order.quantity * Decimal(str(self.commission_per_share)) slippage = abs(fill_price - base_price) * order.quantity return Fill( order=order, fill_price=fill_price, fill_quantity=order.quantity, commission=commission, slippage=slippage, timestamp=bar.name ) return None class Backtester: def __init__( self, strategy: Strategy, execution_model: ExecutionModel, initial_capital: Decimal = Decimal("100000") ): self.strategy = strategy self.execution_model = execution_model self.portfolio = Portfolio(cash=initial_capital) self.equity_curve: List[tuple] = [] self.trades: List[Fill] = [] def run(self, data: pd.DataFrame) -> pd.DataFrame: """Run backtest on OHLCV data with DatetimeIndex.""" pending_orders: List[Order] = [] for timestamp, bar in data.iterrows(): # Execute pending orders at today's prices for order in pending_orders: fill = self.execution_model.execute(order, bar) if fill: self.portfolio.process_fill(fill) self.strategy.on_fill(fill) self.trades.append(fill) pending_orders.clear() # Get current prices for equity calculation prices = {data.index.name or "default": Decimal(str(bar["close"]))} equity = self.portfolio.get_equity(prices) self.equity_curve.append((timestamp, float(equity))) # Generate new orders for next bar new_orders = self.strategy.on_bar(timestamp, data.loc[:timestamp]) pending_orders.extend(new_orders) return self._create_results() def _create_results(self) -> pd.DataFrame: equity_df = pd.DataFrame(self.equity_curve, columns=["timestamp", "equity"]) equity_df.set_index("timestamp", inplace=True) equity_df["returns"] = equity_df["equity"].pct_change() return equity_df ``` ### Pattern 2: Vectorized Backtester (Fast) ```python import pandas as pd import numpy as np from typing import Callable, Dict, Any class VectorizedBacktester: """Fast vectorized backtester for simple strategies.""" def __init__( self, initial_capital: float = 100000, commission: float = 0.001, # 0.1% slippage: float = 0.0005 # 0.05% ): self.initial_capital = initial_capital self.commission = commission self.slippage = slippage def run( self, prices: pd.DataFrame, signal_func: Callable[[pd.DataFrame], pd.Series] ) -> Dict[str, Any]: """ Run backtest with signal function. Args: prices: DataFrame with 'close' column signal_func: Function that returns position signals (-1, 0, 1) Returns: Dictionary with results """ # Generate signals (shifted to avoid look-ahead) signals = signal_func(prices).shift(1).fillna(0) # Calculate returns returns = prices["close"].pct_change() # Calculate strategy returns with costs position_changes = signals.diff().abs() trading_costs = position_changes * (self.commission + self.slippage) strategy_returns = signals * returns - trading_costs # Build equity curve equity = (1 + strategy_returns).cumprod() * self.initial_capital # Calculate metrics results = { "equity": equity, "returns": strategy_returns, "signals": signals, "metrics": self._calculate_metrics(strategy_returns, equity) } return results def _calculate_metrics( self, returns: pd.Series, equity: pd.Series ) -> Dict[str, float]: """Calculate performance metrics.""" total_return = (equity.iloc[-1] / self.initial_capital) - 1 annual_return = (1 + total_return) ** (252 / len(returns)) - 1 annual_vol = returns.std() * np.sqrt(252) sharpe = annual_return / annual_vol if annual_vol > 0 else 0 # Drawdown rolling_max = equity.cummax() drawdown = (equity - rolling_max) / rolling_max max_drawdown = drawdown.min() # Win rate winning_days = (returns > 0).sum() total_days = (returns != 0).sum() win_rate = winning_days / total_days if total_days > 0 else 0 return { "total_return": total_return, "annual_return": annual_return, "annual_volatility": annual_vol, "sharpe_ratio": sharpe, "max_drawdown": max_drawdown, "win_rate": win_rate, "num_trades": int((returns != 0).sum()) } # Example usage def momentum_signal(prices: pd.DataFrame, lookback: int = 20) -> pd.Series: """Simple momentum strategy: long when price > SMA, else flat.""" sma = prices["close"].rolling(lookback).mean() return (prices["close"] > sma).astype(int) # Run backtest # backtester = VectorizedBacktester() # results = backtester.run(price_data, lambda p: momentum_signal(p, 50)) ``` ### Pattern 3: Walk-Forward Optimization ```python from typing import Callable, Dict, List, Tuple, Any import pandas as pd import numpy as np from itertools import product class WalkForwardOptimizer: """Walk-forward analysis with anchored or rolling windows.""" def __init__( self, train_period: int, test_period: int, anchored: bool = False, n_splits: int = None ): """ Args: train_period: Number of bars in training window test_period: Number of bars in test window anchored: If True, training always starts from beginning n_splits: Number of train/test splits (auto-calculated if None) """ self.train_period = train_period self.test_period = test_period self.anchored = anchored self.n_splits = n_splits def generate_splits( self, data: pd.DataFrame ) -> List[Tuple[pd.DataFrame, pd.DataFrame]]: """Generate train/test splits.""" splits = [] n = len(data) if self.n_splits: step = (n - self.train_period) // self.n_splits else: step = self.test_period start = 0 while start + self.train_period + self.test_period <= n: if self.anchored: train_start = 0 else: train_start = start train_end = start + self.train_period test_end = min(train_end + self.test_period, n) train_data = data.iloc[train_start:train_end] test_data = data.iloc[train_end:test_end] splits.append((train_data, test_data)) start += step return splits def optimize( self, data: pd.DataFrame, strategy_func: Callable, param_grid: Dict[str, List], metric: str = "sharpe_ratio" ) -> Dict[str, Any]: """ Run walk-forward optimization. Args: data: Full dataset strategy_func: Function(data, **params) -> results dict param_grid: Parameter combinations to test metric: Metric to optimize Returns: Combined results from all test periods """ splits = self.generate_splits(data) all_results = [] optimal_params_history = [] for i, (train_data, test_data) in enumerate(splits): # Optimize on training data best_params, best_metric = self._grid_search( train_data, strategy_func, param_grid, metric ) optimal_params_history.append(best_params) # Test with optimal params test_results = strategy_func(test_data, **best_params) test_results["split"] = i test_results["params"] = best_params all_results.append(test_results) print(f"Split {i+1}/{len(splits)}: " f"Best {metric}={best_metric:.4f}, params={best_params}") return { "split_results": all_results, "param_history": optimal_params_history, "combined_equity": self._combine_equity_curves(all_results) } def _grid_search( self, data: pd.DataFrame, strategy_func: Callable, param_grid: Dict[str, List], metric: str ) -> Tuple[Dict, float]: """Grid search for best parameters.""" best_params = None best_metric = -np.inf # Generate all parameter combinations param_names = list(param_grid.keys()) param_values = list(param_grid.values()) for values in product(*param_values): params = dict(zip(param_names, values)) results = strategy_func(data, **params) if results["metrics"][metric] > best_metric: best_metric = results["metrics"][metric] best_params = params return best_params, best_metric def _combine_equity_curves( self, results: List[Dict] ) -> pd.Series: """Combine equity curves from all test periods.""" combined = pd.concat([r["equity"] for r in results]) return combined ``` ### Pattern 4: Monte Carlo Analysis ```python import numpy as np import pandas as pd from typing import Dict, List class MonteCarloAnalyzer: """Monte Carlo simulation for strategy robustness.""" def __init__(self, n_simulations: int = 1000, confidence: float = 0.95): self.n_simulations = n_simulations self.confidence = confidence def bootstrap_returns( self, returns: pd.Series, n_periods: int = None ) -> np.ndarray: """ Bootstrap simulation by resampling returns. Args: returns: Historical returns series n_periods: Length of each simulation (default: same as input) Returns: Array of shape (n_simulations, n_periods) """ if n_periods is None: n_periods = len(returns) simulations = np.zeros((self.n_simulations, n_periods)) for i in range(self.n_simulations): # Resample with replacement simulated_returns = np.random.choice( returns.values, size=n_periods, replace=True ) simulations[i] = simulated_returns return simulations def analyze_drawdowns( self, returns: pd.Series ) -> Dict[str, float]: """Analyze drawdown distribution via simulation.""" simulations = self.bootstrap_returns(returns) max_drawdowns = [] for sim_returns in simulations: equity = (1 + sim_returns).cumprod() rolling_max = np.maximum.accumulate(equity) drawdowns = (equity - rolling_max) / rolling_max max_drawdowns.append(drawdowns.min()) max_drawdowns = np.array(max_drawdowns) return { "expected_max_dd": np.mean(max_drawdowns), "median_max_dd": np.median(max_drawdowns), f"worst_{int(self.confidence*100)}pct": np.percentile( max_drawdowns, (1 - self.confidence) * 100 ), "worst_case": max_drawdowns.min() } def probability_of_loss( self, returns: pd.Series, holding_periods: List[int] = [21, 63, 126, 252] ) -> Dict[int, float]: """Calculate probability of loss over various holding periods.""" results = {} for period in holding_periods: if period > len(returns): continue simulations = self.bootstrap_returns(returns, period) total_returns = (1 + simulations).prod(axis=1) - 1 prob_loss = (total_returns < 0).mean() results[period] = prob_loss return results def confidence_interval( self, returns: pd.Series, periods: int = 252 ) -> Dict[str, float]: """Calculate confidence interval for future returns.""" simulations = self.bootstrap_returns(returns, periods) total_returns = (1 + simulations).prod(axis=1) - 1 lower = (1 - self.confidence) / 2 upper = 1 - lower return { "expected": total_returns.mean(), "lower_bound": np.percentile(total_returns, lower * 100), "upper_bound": np.percentile(total_returns, upper * 100), "std": total_returns.std() } ``` ## Performance Metrics ```python def calculate_metrics(returns: pd.Series, rf_rate: float = 0.02) -> Dict[str, float]: """Calculate comprehensive performance metrics.""" # Annualization factor (assuming daily returns) ann_factor = 252 # Basic metrics total_return = (1 + returns).prod() - 1 annual_return = (1 + total_return) ** (ann_factor / len(returns)) - 1 annual_vol = returns.std() * np.sqrt(ann_factor) # Risk-adjusted returns sharpe = (annual_return - rf_rate) / annual_vol if annual_vol > 0 else 0 # Sortino (downside deviation) downside_returns = returns[returns < 0] downside_vol = downside_returns.std() * np.sqrt(ann_factor) sortino = (annual_return - rf_rate) / downside_vol if downside_vol > 0 else 0 # Calmar ratio equity = (1 + returns).cumprod() rolling_max = equity.cummax() drawdowns = (equity - rolling_max) / rolling_max max_drawdown = drawdowns.min() calmar = annual_return / abs(max_drawdown) if max_drawdown != 0 else 0 # Win rate and profit factor wins = returns[returns > 0] losses = return
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risk-metrics-calculation

Calculate portfolio risk metrics including VaR, CVaR, Sharpe,

coding
⭐1
# Risk Metrics Calculation Comprehensive risk measurement toolkit for portfolio management, including Value at Risk, Expected Shortfall, and drawdown analysis. ## When to Use This Skill - Measuring portfolio risk - Implementing risk limits - Building risk dashboards - Calculating risk-adjusted returns - Setting position sizes - Regulatory reporting ## Core Concepts ### 1. Risk Metric Categories | Category | Metrics | Use Case | | ----------------- | --------------- | -------------------- | | **Volatility** | Std Dev, Beta | General risk | | **Tail Risk** | VaR, CVaR | Extreme losses | | **Drawdown** | Max DD, Calmar | Capital preservation | | **Risk-Adjusted** | Sharpe, Sortino | Performance | ### 2. Time Horizons ``` Intraday: Minute/hourly VaR for day traders Daily: Standard risk reporting Weekly: Rebalancing decisions Monthly: Performance attribution Annual: Strategic allocation ``` ## Implementation ### Pattern 1: Core Risk Metrics ```python import numpy as np import pandas as pd from scipy import stats from typing import Dict, Optional, Tuple class RiskMetrics: """Core risk metric calculations.""" def __init__(self, returns: pd.Series, rf_rate: float = 0.02): """ Args: returns: Series of periodic returns rf_rate: Annual risk-free rate """ self.returns = returns self.rf_rate = rf_rate self.ann_factor = 252 # Trading days per year # Volatility Metrics def volatility(self, annualized: bool = True) -> float: """Standard deviation of returns.""" vol = self.returns.std() if annualized: vol *= np.sqrt(self.ann_factor) return vol def downside_deviation(self, threshold: float = 0, annualized: bool = True) -> float: """Standard deviation of returns below threshold.""" downside = self.returns[self.returns < threshold] if len(downside) == 0: return 0.0 dd = downside.std() if annualized: dd *= np.sqrt(self.ann_factor) return dd def beta(self, market_returns: pd.Series) -> float: """Beta relative to market.""" aligned = pd.concat([self.returns, market_returns], axis=1).dropna() if len(aligned) < 2: return np.nan cov = np.cov(aligned.iloc[:, 0], aligned.iloc[:, 1]) return cov[0, 1] / cov[1, 1] if cov[1, 1] != 0 else 0 # Value at Risk def var_historical(self, confidence: float = 0.95) -> float: """Historical VaR at confidence level.""" return -np.percentile(self.returns, (1 - confidence) * 100) def var_parametric(self, confidence: float = 0.95) -> float: """Parametric VaR assuming normal distribution.""" z_score = stats.norm.ppf(confidence) return self.returns.mean() - z_score * self.returns.std() def var_cornish_fisher(self, confidence: float = 0.95) -> float: """VaR with Cornish-Fisher expansion for non-normality.""" z = stats.norm.ppf(confidence) s = stats.skew(self.returns) # Skewness k = stats.kurtosis(self.returns) # Excess kurtosis # Cornish-Fisher expansion z_cf = (z + (z**2 - 1) * s / 6 + (z**3 - 3*z) * k / 24 - (2*z**3 - 5*z) * s**2 / 36) return -(self.returns.mean() + z_cf * self.returns.std()) # Conditional VaR (Expected Shortfall) def cvar(self, confidence: float = 0.95) -> float: """Expected Shortfall / CVaR / Average VaR.""" var = self.var_historical(confidence) return -self.returns[self.returns <= -var].mean() # Drawdown Analysis def drawdowns(self) -> pd.Series: """Calculate drawdown series.""" cumulative = (1 + self.returns).cumprod() running_max = cumulative.cummax() return (cumulative - running_max) / running_max def max_drawdown(self) -> float: """Maximum drawdown.""" return self.drawdowns().min() def avg_drawdown(self) -> float: """Average drawdown.""" dd = self.drawdowns() return dd[dd < 0].mean() if (dd < 0).any() else 0 def drawdown_duration(self) -> Dict[str, int]: """Drawdown duration statistics.""" dd = self.drawdowns() in_drawdown = dd < 0 # Find drawdown periods drawdown_starts = in_drawdown & ~in_drawdown.shift(1).fillna(False) drawdown_ends = ~in_drawdown & in_drawdown.shift(1).fillna(False) durations = [] current_duration = 0 for i in range(len(dd)): if in_drawdown.iloc[i]: current_duration += 1 elif current_duration > 0: durations.append(current_duration) current_duration = 0 if current_duration > 0: durations.append(current_duration) return { "max_duration": max(durations) if durations else 0, "avg_duration": np.mean(durations) if durations else 0, "current_duration": current_duration } # Risk-Adjusted Returns def sharpe_ratio(self) -> float: """Annualized Sharpe ratio.""" excess_return = self.returns.mean() * self.ann_factor - self.rf_rate vol = self.volatility(annualized=True) return excess_return / vol if vol > 0 else 0 def sortino_ratio(self) -> float: """Sortino ratio using downside deviation.""" excess_return = self.returns.mean() * self.ann_factor - self.rf_rate dd = self.downside_deviation(threshold=0, annualized=True) return excess_return / dd if dd > 0 else 0 def calmar_ratio(self) -> float: """Calmar ratio (return / max drawdown).""" annual_return = (1 + self.returns).prod() ** (self.ann_factor / len(self.returns)) - 1 max_dd = abs(self.max_drawdown()) return annual_return / max_dd if max_dd > 0 else 0 def omega_ratio(self, threshold: float = 0) -> float: """Omega ratio.""" returns_above = self.returns[self.returns > threshold] - threshold returns_below = threshold - self.returns[self.returns <= threshold] if returns_below.sum() == 0: return np.inf return returns_above.sum() / returns_below.sum() # Information Ratio def information_ratio(self, benchmark_returns: pd.Series) -> float: """Information ratio vs benchmark.""" active_returns = self.returns - benchmark_returns tracking_error = active_returns.std() * np.sqrt(self.ann_factor) active_return = active_returns.mean() * self.ann_factor return active_return / tracking_error if tracking_error > 0 else 0 # Summary def summary(self) -> Dict[str, float]: """Generate comprehensive risk summary.""" dd_stats = self.drawdown_duration() return { # Returns "total_return": (1 + self.returns).prod() - 1, "annual_return": (1 + self.returns).prod() ** (self.ann_factor / len(self.returns)) - 1, # Volatility "annual_volatility": self.volatility(), "downside_deviation": self.downside_deviation(), # VaR & CVaR "var_95_historical": self.var_historical(0.95), "var_99_historical": self.var_historical(0.99), "cvar_95": self.cvar(0.95), # Drawdowns "max_drawdown": self.max_drawdown(), "avg_drawdown": self.avg_drawdown(), "max_drawdown_duration": dd_stats["max_duration"], # Risk-Adjusted "sharpe_ratio": self.sharpe_ratio(), "sortino_ratio": self.sortino_ratio(), "calmar_ratio": self.calmar_ratio(), "omega_ratio": self.omega_ratio(), # Distribution "skewness": stats.skew(self.returns), "kurtosis": stats.kurtosis(self.returns), } ``` ### Pattern 2: Portfolio Risk ```python class PortfolioRisk: """Portfolio-level risk calculations.""" def __init__( self, returns: pd.DataFrame, weights: Optional[pd.Series] = None ): """ Args: returns: DataFrame with asset returns (columns = assets) weights: Portfolio weights (default: equal weight) """ self.returns = returns self.weights = weights if weights is not None else \ pd.Series(1/len(returns.columns), index=returns.columns) self.ann_factor = 252 def portfolio_return(self) -> float: """Weighted portfolio return.""" return (self.returns @ self.weights).mean() * self.ann_factor def portfolio_volatility(self) -> float: """Portfolio volatility.""" cov_matrix = self.returns.cov() * self.ann_factor port_var = self.weights @ cov_matrix @ self.weights return np.sqrt(port_var) def marginal_risk_contribution(self) -> pd.Series: """Marginal contribution to risk by asset.""" cov_matrix = self.returns.cov() * self.ann_factor port_vol = self.portfolio_volatility() # Marginal contribution mrc = (cov_matrix @ self.weights) / port_vol return mrc def component_risk(self) -> pd.Series: """Component contribution to total risk.""" mrc = self.marginal_risk_contribution() return self.weights * mrc def risk_parity_weights(self, target_vol: float = None) -> pd.Series: """Calculate risk parity weights.""" from scipy.optimize import minimize n = len(self.returns.columns) cov_matrix = self.returns.cov() * self.ann_factor def risk_budget_objective(weights): port_vol = np.sqrt(weights @ cov_matrix @ weights) mrc = (cov_matrix @ weights) / port_vol rc = weights * mrc target_rc = port_vol / n # Equal risk contribution return np.sum((rc - target_rc) ** 2) constraints = [ {"type": "eq", "fun": lambda w: np.sum(w) - 1}, # Weights sum to 1 ] bounds = [(0.01, 1.0) for _ in range(n)] # Min 1%, max 100% x0 = np.array([1/n] * n) result = minimize( risk_budget_objective, x0, method="SLSQP", bounds=bounds, constraints=constraints ) return pd.Series(result.x, index=self.returns.columns) def correlation_matrix(self) -> pd.DataFrame: """Asset correlation matrix.""" return self.returns.corr() def diversification_ratio(self) -> float: """Diversification ratio (higher = more diversified).""" asset_vols = self.returns.std() * np.sqrt(self.ann_factor) weighted_vol = (self.weights * asset_vols).sum() port_vol = self.portfolio_volatility() return weighted_vol / port_vol if port_vol > 0 else 1 def tracking_error(self, benchmark_returns: pd.Series) -> float: """Tracking error vs benchmark.""" port_returns = self.returns @ self.weights active_returns = port_returns - benchmark_returns return active_returns.std() * np.sqrt(self.ann_factor) def conditional_correlation( self, threshold_percentile: float = 10 ) -> pd.DataFrame: """Correlation during stress periods.""" port_returns = self.returns @ self.weights threshold = np.percentile(port_returns, threshold_percentile) stress_mask = port_returns <= threshold return self.returns[stress_mask].corr() ``` ### Pattern 3: Rolling Risk Metrics ```python class RollingRiskMetrics: """Rolling window risk calculations.""" def __init__(self, returns: pd.Series, window: int = 63): """ Args: returns: Return series window: Rolling window size (default: 63 = ~3 months) """ self.returns = returns self.window = window def rolling_volatility(self, annualized: bool = True) -> pd.Series: """Rolling volatility.""" vol = self.returns.rolling(self.window).std() if annualized: vol *= np.sqrt(252) return vol def rolling_sharpe(self, rf_rate: float = 0.02) -> pd.Series: """Rolling Sharpe ratio.""" rolling_return = self.returns.rolling(self.window).mean() * 252 rolling_vol = self.rolling_volatility() return (rolling_return - rf_rate) / rolling_vol def rolling_var(self, confidence: float = 0.95) -> pd.Series: """Rolling historical VaR.""" return self.returns.rolling(self.window).apply( lambda x: -np.percentile(x, (1 - confidence) * 100), raw=True ) def rolling_max_drawdown(self) -> pd.Series: """Rolling maximum drawdown.""" def max_dd(returns): cumulative = (1 + returns).cumprod() running_max = cumulative.cummax() drawdowns = (cumulative - running_max) / running_max return drawdowns.min() return self.returns.rolling(self.window).apply(max_dd, raw=False) def rolling_beta(self, market_returns: pd.Series) -> pd.Series: """Rolling beta vs market.""" def calc_beta(window_data): port_ret = window_data.iloc[:, 0] mkt_ret = window_data.iloc[:, 1] cov = np.cov(port_ret, mkt_ret) return cov[0, 1] / cov[1, 1] if cov[1, 1] != 0 else 0 combined = pd.concat([self.returns, market_returns], axis=1) return combined.rolling(self.window).apply( lambda x: calc_beta(x.to_frame()), raw=False ).iloc[:, 0] def volatility_regime( self, low_threshold: float = 0.10, high_threshold: float = 0.20 ) -> pd.Series: """Classify volatility regime.""" vol = self.rolling_volatility() def classify(v): if v < low_threshold: return "low" elif v > high_threshold: return "high" else: return "normal" return vol.apply(classify) ``` ### Pattern 4: Stress Testing ```python class StressTester: """Historical and hypothetical stress testing.""" # Historical crisis periods HISTORICAL_SCENARIOS = { "2008_financial_crisis": ("2008-09-01", "2009-03-31"), "2020_covid_crash": ("2020-02-19", "2020-03-23"), "2022_rate_hikes": ("2022-01-01", "2022-10-31"), "dot_com_bust": ("2000-03-01", "2002-10-01"), "flash_crash_2010": ("2010-05-06", "2010-05-06"), } def __init__(self, returns: pd.Series, weights: pd.Series = None): self.returns = returns self.weights = weights def historical_stress_test( self, scenario_name: str, historical_data: pd.DataFrame ) -> Dict[str, float]: """Test portfolio against historical crisis period.""" if scenario_name not in self.HISTORICAL_SCENARIOS: raise ValueError(f"Unknown scenario: {scenario_name}") start, end = self.HISTORICAL_SCENARIOS[scenario_name] # Get returns during crisis crisis_returns = historical_data.loc[start:end] if self.weights is not None: port_returns = (crisis_returns @ self.weights) else: port_returns = crisis_returns total_return = (1 + port_returns).prod() - 1 max_dd = self._calculate_max_dd(port_returns) worst_day = port_returns.min() return { "scenario": scenario_name, "period": f"{start} to {end}", "total_return": total_return, "max_drawdown": max_dd, "worst_day": worst_day, "volatility": port_returns.std() * np.sqrt(252) } def hypothetical_stress_test( self, shocks: Dict[str, float] ) -> float: """ Test portfolio against hypothetical shocks. Args: shocks: Dict of {asset: shock_return} """ if self.weights is None: raise ValueError("Weights required for hypothetical stress test") total_impact = 0 for asset, shock in shocks.items(): if asset in self.weights.index: total_impact += self.weights[asset] * shock return total_impact def monte_carlo_stress( self, n_simulations: int = 10000, horizon_days: int = 21, vol_multiplier: float = 2.0 ) -> Dict[str, float]: """Monte Carlo stress test with elevated volatility.""" mean = self.returns.mean() vol = self.returns.std() * vol_multiplier simulations = np.random.normal( mean, vol, (n_simulations, horizon_days) ) total_returns = (1 + simulations).prod(axis=1) - 1 return { "expected_loss": -total_returns.mean(), "var_95": -np.percentile(total_returns, 5), "var_99": -np.percentile(total_returns, 1), "worst_case": -total_returns.min(), "prob_10pct_loss": (total_returns < -0.10).mean() } def _calculate_max_dd(self, returns: pd.Series) -> float: cumulative = (1 + returns).cumprod() running_max = cumulative.cummax() drawdowns = (cumulative - running_max) / running_max return drawdowns.min() ``` ## Quick Reference ```python # Daily usage metrics = RiskMetrics(returns) print(f"Sharpe: {metrics.sharpe_ratio():.2f}") print(f"Max DD: {metrics.max_drawdown():.2%}") print(f"VaR 95%: {metrics.var_historical(0.95):.2%}") # Full summary summary = metrics.summary() for metric, value in summary.items(): print(f"{metric}: {value:.4f}") ``` ## Best Practices ### Do's - **Use multiple metrics** - No single metric captures all risk - **Consider tail risk** - VaR isn't enough, use CVaR - **Rolling analysis** - Risk changes over time - **Stress test** - Historical and hypothetical - **Document assumptions** - Distribution, lookback, etc. ### Don'ts - **Don't rely on VaR alone** - Underestimates tail risk - **Don't assume normality** - Returns are fat-tailed - **Don't ignore correlation** - Increases in stress - **Don't use short lookbacks** - Miss regime changes - **Don't forget transaction costs** - Affects realized risk ## Resources - [Risk Management and Financial Institutions (John Hull)](https://www.amazon.com/Risk-Management-Financial-Institutions-5th/dp/1119448115) - [Quantitative Risk Management (McNeil, Frey, Embrechts)](https://www.amazon.com/Quantitative-Risk-Management-Techniques-Princeton/dp/0691166277) - [pyfolio Documentation](https://quantopian.github.io/pyfolio/)
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🤖system prompt•7 months ago

competitive-landscape

This skill should be used when the user asks to "analyze

business
⭐1
# Competitive Landscape Analysis Comprehensive frameworks for analyzing competition, identifying differentiation opportunities, and developing winning market positioning strategies. ## Overview Understand competitive dynamics using proven frameworks (Porter's Five Forces, Blue Ocean Strategy, positioning maps) to identify opportunities and craft defensible competitive advantages. ## Porter's Five Forces Analyze industry attractiveness and competitive intensity. ### Force 1: Threat of New Entrants **Barriers to Entry:** - Capital requirements - Economies of scale - Switching costs - Brand loyalty - Regulatory barriers - Access to distribution - Network effects **High Threat:** Low barriers, easy to enter (e.g., simple SaaS tools) **Low Threat:** High barriers (e.g., regulated industries, hardware) **Analysis Questions:** - How easy is it for new competitors to enter? - What would it cost to launch a competing product? - Are there network effects or switching costs protecting incumbents? ### Force 2: Bargaining Power of Suppliers **Supplier Power Factors:** - Supplier concentration - Availability of substitutes - Importance to supplier - Switching costs - Forward integration threat **High Power:** Few suppliers, critical inputs (e.g., cloud infrastructure providers) **Low Power:** Many alternatives, commoditized (e.g., generic services) **Analysis Questions:** - Who are our critical suppliers? - Could they raise prices or reduce quality? - Can we switch suppliers easily? ### Force 3: Bargaining Power of Buyers **Buyer Power Factors:** - Buyer concentration - Volume purchased - Product differentiation - Price sensitivity - Backward integration threat **High Power:** Few large customers, standardized products (e.g., enterprise deals) **Low Power:** Many small customers, differentiated product (e.g., consumer subscriptions) **Analysis Questions:** - Can customers easily switch to competitors? - Do few customers generate most revenue? - How price-sensitive are buyers? ### Force 4: Threat of Substitutes **Substitute Considerations:** - Alternative solutions - Price-performance tradeoff - Switching costs - Buyer propensity to substitute **High Threat:** Many alternatives, low switching cost (e.g., productivity software) **Low Threat:** Unique solution, high switching cost (e.g., ERP systems) **Analysis Questions:** - What alternative ways can customers solve this problem? - How do substitutes compare on price and performance? - What's the cost to switch to a substitute? ### Force 5: Competitive Rivalry **Rivalry Intensity Factors:** - Number of competitors - Industry growth rate - Product differentiation - Exit barriers - Strategic stakes **High Rivalry:** Many competitors, slow growth, commoditized (e.g., email marketing) **Low Rivalry:** Few competitors, fast growth, differentiated (e.g., emerging AI tools) **Analysis Questions:** - How many direct competitors exist? - Is the market growing or stagnant? - How differentiated are offerings? - Are competitors competing on price or value? ### Forces Analysis Summary Create a scorecard: | Force | Intensity (1-5) | Impact | Key Factors | | -------------- | --------------- | ------ | --------------------------------- | | New Entrants | 3 | Medium | Low barriers but network effects | | Supplier Power | 2 | Low | Many cloud providers | | Buyer Power | 4 | High | Enterprise customers concentrated | | Substitutes | 3 | Medium | Manual processes alternative | | Rivalry | 4 | High | 10+ direct competitors | **Overall Assessment:** Moderate industry attractiveness with high rivalry and buyer power ## Blue Ocean Strategy Identify uncontested market space through value innovation. ### Four Actions Framework **Eliminate:** What factors can be eliminated that the industry takes for granted? **Reduce:** What factors can be reduced well below industry standard? **Raise:** What factors can be raised well above industry standard? **Create:** What factors can be created that the industry never offered? ### Strategy Canvas Map your offering vs. competitors on key factors. **Example: Budget Hotels** ``` High | ★ Traditional Hotels | ★ Budget Hotels (new) | Low |___________________________________ Price Luxury Convenience Cleanliness Budget Hotel Strategy: - Eliminate: Luxury amenities, room service - Reduce: Lobby size, staff - Raise: Cleanliness, online booking - Create: Self-service kiosks, mobile app ``` ### Value Innovation Find the sweet spot: Lower cost + higher value **Steps:** 1. Map industry competing factors 2. Identify factors to eliminate/reduce (cost savings) 3. Identify factors to raise/create (differentiation) 4. Validate that combination creates new market space ## Competitive Positioning ### Positioning Map Plot competitors on 2-3 key dimensions. **Example Dimensions:** - Price vs. Features - Complexity vs. Ease of Use - Enterprise vs. SMB Focus - Self-Service vs. High-Touch - Generalist vs. Specialist **How to Create:** 1. Choose 2 dimensions most important to customers 2. Plot all competitors 3. Identify gaps (white space) 4. Validate gap represents real customer need **Example:** ``` High Price | | ★ Enterprise A ★ Enterprise B | | ● Our Position (gap) | | ★ Competitor C ★ Competitor D | Low Price |____________________________________________ Simple Complex ``` ### Differentiation Strategy **How to Differentiate:** 1. **Product Differentiation** - Unique features - Superior performance - Better design/UX - Integration ecosystem 2. **Service Differentiation** - Customer support quality - Onboarding experience - Response time - Success programs 3. **Brand Differentiation** - Trust and reputation - Thought leadership - Community - Values alignment 4. **Price Differentiation** - Premium positioning - Value positioning - Transparent pricing - Flexible packaging ### Positioning Statement Framework ``` For [target customer] Who [statement of need or opportunity] Our product is [product category] That [statement of key benefit] Unlike [primary competitive alternative] Our product [statement of primary differentiation] ``` **Example:** ``` For e-commerce companies Who struggle with email marketing automation Our product is an AI-powered email platform That increases conversion rates by 40% Unlike Klaviyo and Mailchimp Our product uses AI to personalize at scale ``` ## Competitive Intelligence ### Information Gathering **Public Sources:** - Company websites and blogs - Press releases and news - Job postings (hint at strategy) - Customer reviews (G2, Capterra) - Social media and forums - Glassdoor (employee insights) - SEC filings (public companies) - Patent filings **Direct Research:** - Customer interviews - Win/loss analysis - Sales team feedback - Product demos and trials - Conference attendance ### Competitor Profile Template For each key competitor, document: **Company Overview:** - Founded, HQ, funding, size - Leadership team - Company stage and trajectory **Product:** - Core features - Target customers - Pricing and packaging - Technology stack - Recent launches **Go-to-Market:** - Sales model (self-serve, sales-led) - Marketing strategy - Distribution channels - Partnerships **Strengths:** - What they do better than anyone - Key competitive advantages - Market position **Weaknesses:** - Gaps in product - Customer complaints - Operational challenges **Strategy:** - Stated direction - Inferred priorities - Likely next moves ## Competitive Pricing Analysis ### Price Positioning **Premium (Top 25%):** - Superior product/service - Strong brand - High-touch sales - Enterprise focus **Mid-Market (Middle 50%):** - Balanced value - Standard features - Mixed sales model - Broad market **Value (Bottom 25%):** - Basic functionality - Self-service - Cost leadership - High volume, low margin ### Pricing Comparison Matrix | Competitor | Entry Price | Mid Tier | Enterprise | Model | | ------------ | ----------- | -------- | ---------- | ------------ | | Competitor A | $29/mo | $99/mo | Custom | Subscription | | Competitor B | $49/mo | $199/mo | $499/mo | Subscription | | Us | $39/mo | $129/mo | Custom | Subscription | **Analysis:** - Are we priced competitively? - What does our pricing signal? - Are there gaps in our packaging? ## Go-to-Market Strategy ### Market Entry Strategies **Direct Competition:** - Head-to-head against established players - Requires differentiation and resources - Example: Better features at lower price **Niche Focus:** - Target underserved segment - Become specialist vs. generalist - Example: "Salesforce for real estate" **Disruptive Innovation:** - Target non-consumers or low end - Improve over time to move upmarket - Example: Freemium model disrupting enterprise **Platform Play:** - Build ecosystem and network effects - Aggregate complementary services - Example: Marketplace or API platform ### Beachhead Market **Characteristics of Good Beachhead:** - Specific, reachable segment - Acute pain you solve well - Limited competition - Willing to pay - Can lead to expansion **Example:** Instead of "project management software", target "project management for construction teams" ## Competitive Advantage ### Sustainable Advantages **Network Effects:** - Value increases with users - Example: Slack, marketplaces **Switching Costs:** - High cost to change - Example: CRM systems with data **Economies of Scale:** - Unit costs decrease with volume - Example: Cloud infrastructure **Brand:** - Trust and reputation - Example: Security software **Proprietary Technology:** - Patents or trade secrets - Example: Algorithms, data **Regulatory:** - Licenses or approvals - Example: Fintech, healthcare ### Testing Your Advantage Ask: - Can competitors copy this in < 2 years? - Does this matter to customers? - Do we execute this better than anyone? - Is this advantage durable? If "no" to any, it's not a sustainable advantage. ## Competitive Monitoring ### What to Track **Product Changes:** - New features - Pricing changes - Packaging adjustments **Market Signals:** - Funding announcements - Key hires (especially leadership) - Customer wins/losses - Partnerships **Performance Metrics:** - Revenue (if public or disclosed) - Customer count - Growth rate - Market share estimates ### Monitoring Cadence **Weekly:** - Product release notes - News mentions **Monthly:** - Win/loss analysis review - Positioning map updates **Quarterly:** - Deep competitive review - Strategy adjustment **Annually:** - Major strategy reassessment - Market trends analysis ## Additional Resources ### Reference Files - **`references/frameworks-deep-dive.md`** - Detailed application of each framework with worksheets - **`references/intel-sources.md`** - Comprehensive list of competitive intelligence sources ### Example Files - **`examples/competitor-analysis.md`** - Complete competitive analysis for a SaaS startup - **`examples/positioning-workshop.md`** - Step-by-step positioning development process ## Quick Start To analyze competitive landscape: 1. **Identify competitors** - Direct, indirect, and future threats 2. **Apply Porter's Five Forces** - Assess industry attractiveness 3. **Create positioning map** - Visualize competitive space 4. **Profile top 3-5 competitors** - Deep dive on key rivals 5. **Identify differentiation** - What makes you unique 6. **Analyze pricing** - Where do you fit? 7. **Assess advantages** - What's defensible? 8. **Develop strategy** - How to win For detailed frameworks and examples, see `references/` and `examples/`.
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🤖system prompt•7 months ago

market-sizing-analysis

This skill should be used when the user asks to "calculate TAM",

business
⭐1
# Market Sizing Analysis Comprehensive market sizing methodologies for calculating Total Addressable Market (TAM), Serviceable Available Market (SAM), and Serviceable Obtainable Market (SOM) for startup opportunities. ## Overview Market sizing provides the foundation for startup strategy, fundraising, and business planning. Calculate market opportunity using three complementary methodologies: top-down (industry reports), bottom-up (customer segment calculations), and value theory (willingness to pay). ## Core Concepts ### The Three-Tier Market Framework **TAM (Total Addressable Market)** - Total revenue opportunity if achieving 100% market share - Defines the universe of potential customers - Used for long-term vision and market validation - Example: All email marketing software revenue globally **SAM (Serviceable Available Market)** - Portion of TAM targetable with current product/service - Accounts for geographic, segment, or capability constraints - Represents realistic addressable opportunity - Example: AI-powered email marketing for e-commerce in North America **SOM (Serviceable Obtainable Market)** - Realistic market share achievable in 3-5 years - Accounts for competition, resources, and market dynamics - Used for financial projections and fundraising - Example: 2-5% of SAM based on competitive landscape ### When to Use Each Methodology **Top-Down Analysis** - Use when established market research exists - Best for mature, well-defined markets - Validates market existence and growth - Starts with industry reports and narrows down **Bottom-Up Analysis** - Use when targeting specific customer segments - Best for new or niche markets - Most credible for investors - Builds from customer data and pricing **Value Theory** - Use when creating new market categories - Best for disruptive innovations - Estimates based on value creation - Calculates willingness to pay for problem solution ## Three-Methodology Framework ### Methodology 1: Top-Down Analysis Start with total market size and narrow to addressable segments. **Process:** 1. Identify total market category from research reports 2. Apply geographic filters (target regions) 3. Apply segment filters (target industries/customers) 4. Calculate competitive positioning adjustments **Formula:** ``` TAM = Total Market Category Size SAM = TAM × Geographic % × Segment % SOM = SAM × Realistic Capture Rate (2-5%) ``` **When to use:** Established markets with available research (e.g., SaaS, fintech, e-commerce) **Strengths:** Quick, uses credible data, validates market existence **Limitations:** May overestimate for new categories, less granular ### Methodology 2: Bottom-Up Analysis Build market size from customer segment calculations. **Process:** 1. Define target customer segments 2. Estimate number of potential customers per segment 3. Determine average revenue per customer 4. Calculate realistic penetration rates **Formula:** ``` TAM = Σ (Segment Size × Annual Revenue per Customer) SAM = TAM × (Segments You Can Serve / Total Segments) SOM = SAM × Realistic Penetration Rate (Year 3-5) ``` **When to use:** B2B, niche markets, specific customer segments **Strengths:** Most credible for investors, granular, defensible **Limitations:** Requires detailed customer research, time-intensive ### Methodology 3: Value Theory Calculate based on value created and willingness to pay. **Process:** 1. Identify problem being solved 2. Quantify current cost of problem (time, money, inefficiency) 3. Calculate value of solution (savings, gains, efficiency) 4. Estimate willingness to pay (typically 10-30% of value) 5. Multiply by addressable customer base **Formula:** ``` Value per Customer = Problem Cost × % Solved by Solution Price per Customer = Value × Willingness to Pay % (10-30%) TAM = Total Potential Customers × Price per Customer SAM = TAM × % Meeting Buy Criteria SOM = SAM × Realistic Adoption Rate ``` **When to use:** New categories, disruptive innovations, unclear existing markets **Strengths:** Shows value creation, works for new markets **Limitations:** Requires assumptions, harder to validate ## Step-by-Step Process ### Step 1: Define the Market Clearly specify what market is being measured. **Questions to answer:** - What problem is being solved? - Who are the target customers? - What's the product/service category? - What's the geographic scope? - What's the time horizon? **Example:** - Problem: E-commerce companies struggle with email marketing automation - Customers: E-commerce stores with >$1M annual revenue - Category: AI-powered email marketing software - Geography: North America initially, global expansion - Horizon: 3-5 year opportunity ### Step 2: Gather Data Sources Identify credible data for calculations. **Top-Down Sources:** - Industry research reports (Gartner, Forrester, IDC) - Government statistics (Census, BLS, trade associations) - Public company filings and earnings - Market research firms (Statista, CB Insights, PitchBook) **Bottom-Up Sources:** - Customer interviews and surveys - Sales data and CRM records - Industry databases (LinkedIn, ZoomInfo, Crunchbase) - Competitive intelligence - Academic research **Value Theory Sources:** - Customer problem quantification - Time/cost studies - ROI case studies - Pricing research and willingness-to-pay surveys ### Step 3: Calculate TAM Apply chosen methodology to determine total market. **For Top-Down:** 1. Find total category size from research 2. Document data source and year 3. Apply growth rate if needed 4. Validate with multiple sources **For Bottom-Up:** 1. Count total potential customers 2. Calculate average annual revenue per customer 3. Multiply to get TAM 4. Break down by segment **For Value Theory:** 1. Quantify total addressable customer base 2. Calculate value per customer 3. Estimate pricing based on value 4. Multiply for TAM ### Step 4: Calculate SAM Narrow TAM to serviceable addressable market. **Apply Filters:** - Geographic constraints (regions you can serve) - Product limitations (features you currently have) - Customer requirements (size, industry, use case) - Distribution channel access - Regulatory or compliance restrictions **Formula:** ``` SAM = TAM × (% matching all filters) ``` **Example:** - TAM: $10B global email marketing - Geographic filter: 40% (North America) - Product filter: 30% (e-commerce focus) - Feature filter: 60% (need AI capabilities) - SAM = $10B × 0.40 × 0.30 × 0.60 = $720M ### Step 5: Calculate SOM Determine realistic obtainable market share. **Consider:** - Current market share of competitors - Typical market share for new entrants (2-5%) - Resources available (funding, team, time) - Go-to-market effectiveness - Competitive advantages - Time to achieve (3-5 years typically) **Conservative Approach:** ``` SOM (Year 3) = SAM × 2% SOM (Year 5) = SAM × 5% ``` **Example:** - SAM: $720M - Year 3 SOM: $720M × 2% = $14.4M - Year 5 SOM: $720M × 5% = $36M ### Step 6: Validate and Triangulate Cross-check using multiple methods. **Validation Techniques:** 1. Compare top-down and bottom-up results (should be within 30%) 2. Check against public company revenues in space 3. Validate customer count assumptions 4. Sense-check pricing assumptions 5. Review with industry experts 6. Compare to similar market categories **Red Flags:** - TAM that's too small (< $1B for VC-backed startups) - TAM that's too large (unsupported by data) - SOM that's too aggressive (> 10% in 5 years for new entrant) - Inconsistency between methodologies (> 50% difference) ## Industry-Specific Considerations ### SaaS Markets **Key Metrics:** - Number of potential businesses in target segment - Average contract value (ACV) - Typical market penetration rates - Expansion revenue potential **TAM Calculation:** ``` TAM = Total Target Companies × Average ACV × (1 + Expansion Rate) ``` ### Marketplace Markets **Key Metrics:** - Gross Merchandise Value (GMV) of category - Take rate (% of GMV you capture) - Total transactions or users **TAM Calculation:** ``` TAM = Total Category GMV × Expected Take Rate ``` ### Consumer Markets **Key Metrics:** - Total addressable users/households - Average revenue per user (ARPU) - Engagement frequency **TAM Calculation:** ``` TAM = Total Users × ARPU × Purchase Frequency per Year ``` ### B2B Services **Key Metrics:** - Number of target companies by size/industry - Average project value or retainer - Typical buying frequency **TAM Calculation:** ``` TAM = Total Target Companies × Average Deal Size × Deals per Year ``` ## Presenting Market Sizing ### For Investors **Structure:** 1. Market definition and problem scope 2. TAM/SAM/SOM with methodology 3. Data sources and assumptions 4. Growth projections and drivers 5. Competitive landscape context **Key Points:** - Lead with bottom-up calculation (most credible) - Show triangulation with top-down - Explain conservative assumptions - Link to revenue projections - Highlight market growth rate ### For Strategy **Structure:** 1. Addressable customer segments 2. Prioritization by opportunity size 3. Entry strategy by segment 4. Expected penetration timeline 5. Resource requirements **Key Points:** - Focus on SAM and SOM - Show segment-level detail - Connect to go-to-market plan - Identify expansion opportunities - Discuss competitive positioning ## Common Mistakes to Avoid **Mistake 1: Confusing TAM with SAM** - Don't claim entire market as addressable - Apply realistic product/geographic constraints - Be honest about serviceable market **Mistake 2: Overly Aggressive SOM** - New entrants rarely capture > 5% in 5 years - Account for competition and resources - Show realistic ramp timeline **Mistake 3: Using Only Top-Down** - Investors prefer bottom-up validation - Top-down alone lacks credibility - Always triangulate with multiple methods **Mistake 4: Cherry-Picking Data** - Use consistent, recent data sources - Don't mix methodologies inappropriately - Document all assumptions clearly **Mistake 5: Ignoring Market Dynamics** - Account for market growth/decline - Consider competitive intensity - Factor in switching costs and barriers ## Additional Resources ### Reference Files For detailed methodologies and frameworks: - **`references/methodology-deep-dive.md`** - Comprehensive guide to each methodology with step-by-step worksheets - **`references/data-sources.md`** - Curated list of market research sources, databases, and tools - **`references/industry-templates.md`** - Specific templates for SaaS, marketplace, consumer, B2B, and fintech markets ### Example Files Working examples with complete calculations: - **`examples/saas-market-sizing.md`** - Complete TAM/SAM/SOM for a B2B SaaS product - **`examples/marketplace-sizing.md`** - Marketplace platform market opportunity calculation - **`examples/value-theory-example.md`** - Value-based market sizing for disruptive innovation Use these examples as templates for your own market sizing analysis. Each includes real numbers, data sources, and assumptions documented clearly. ## Quick Start To perform market sizing analysis: 1. **Define the market** - Problem, customers, category, geography 2. **Choose methodology** - Bottom-up (preferred) or top-down + triangulation 3. **Gather data** - Industry reports, customer data, competitive intelligence 4. **Calculate TAM** - Apply methodology formula 5. **Narrow to SAM** - Apply product, geographic, segment filters 6. **Estimate SOM** - 2-5% realistic capture rate 7. **Validate** - Cross-check with alternative methods 8. **Document** - Show methodology, sources, assumptions 9. **Present** - Structure for audience (investors, strategy, operations) For detailed step-by-step guidance on each methodology, reference the files in `references/` directory. For complete worked examples, see `examples/` directory.
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👁️0
🤖 Auto-discovered
🤖system prompt•7 months ago

startup-financial-modeling

This skill should be used when the user asks to "create financial

business
⭐1
# Startup Financial Modeling Build comprehensive 3-5 year financial models with revenue projections, cost structures, cash flow analysis, and scenario planning for early-stage startups. ## Overview Financial modeling provides the quantitative foundation for startup strategy, fundraising, and operational planning. Create realistic projections using cohort-based revenue modeling, detailed cost structures, and scenario analysis to support decision-making and investor presentations. ## Core Components ### Revenue Model **Cohort-Based Projections:** Build revenue from customer acquisition and retention by cohort. **Formula:** ``` MRR = Σ (Cohort Size × Retention Rate × ARPU) ARR = MRR × 12 ``` **Key Inputs:** - Monthly new customer acquisitions - Customer retention rates by month - Average revenue per user (ARPU) - Pricing and packaging assumptions - Expansion revenue (upsells, cross-sells) ### Cost Structure **Operating Expenses Categories:** 1. **Cost of Goods Sold (COGS)** - Hosting and infrastructure - Payment processing fees - Customer support (variable portion) - Third-party services per customer 2. **Sales & Marketing (S&M)** - Customer acquisition cost (CAC) - Marketing programs and advertising - Sales team compensation - Marketing tools and software 3. **Research & Development (R&D)** - Engineering team compensation - Product management - Design and UX - Development tools and infrastructure 4. **General & Administrative (G&A)** - Executive team - Finance, legal, HR - Office and facilities - Insurance and compliance ### Cash Flow Analysis **Components:** - Beginning cash balance - Cash inflows (revenue, fundraising) - Cash outflows (operating expenses, CapEx) - Ending cash balance - Monthly burn rate - Runway (months of cash remaining) **Formula:** ``` Runway = Current Cash Balance / Monthly Burn Rate Monthly Burn = Monthly Revenue - Monthly Expenses ``` ### Headcount Planning **Role-Based Hiring Plan:** Track headcount by department and role. **Key Metrics:** - Fully-loaded cost per employee - Revenue per employee - Headcount by department (% of total) **Typical Ratios (Early-Stage SaaS):** - Engineering: 40-50% - Sales & Marketing: 25-35% - G&A: 10-15% - Customer Success: 5-10% ## Financial Model Structure ### Three-Scenario Framework **Conservative Scenario (P10):** - Slower customer acquisition - Lower pricing or conversion - Higher churn rates - Extended sales cycles - Used for cash management **Base Scenario (P50):** - Most likely outcomes - Realistic assumptions - Primary planning scenario - Used for board reporting **Optimistic Scenario (P90):** - Faster growth - Better unit economics - Lower churn - Used for upside planning ### Time Horizon **Detailed Projections: 3 Years** - Monthly detail for Year 1 - Monthly detail for Year 2 - Quarterly detail for Year 3 **High-Level Projections: Years 4-5** - Annual projections - Key metrics only - Support long-term planning ## Step-by-Step Process ### Step 1: Define Business Model Clarify revenue model and pricing. **SaaS Model:** - Subscription pricing tiers - Annual vs. monthly contracts - Free trial or freemium approach - Expansion revenue strategy **Marketplace Model:** - GMV projections - Take rate (% of transactions) - Buyer and seller economics - Transaction frequency **Transactional Model:** - Transaction volume - Revenue per transaction - Frequency and seasonality ### Step 2: Build Revenue Projections Use cohort-based methodology for accuracy. **Monthly Customer Acquisition:** Define new customers acquired each month. **Retention Curve:** Model customer retention over time. **Typical SaaS Retention:** - Month 1: 100% - Month 3: 90% - Month 6: 85% - Month 12: 75% - Month 24: 70% **Revenue Calculation:** For each cohort, calculate retained customers × ARPU for each month. ### Step 3: Model Cost Structure Break down costs by category and behavior. **Fixed vs. Variable:** - Fixed: Salaries, software, rent - Variable: Hosting, payment processing, support **Scaling Assumptions:** - COGS as % of revenue - S&M as % of revenue (CAC payback) - R&D growth rate - G&A as % of total expenses ### Step 4: Create Hiring Plan Model headcount growth by role and department. **Inputs:** - Starting headcount - Hiring velocity by role - Fully-loaded compensation by role - Benefits and taxes (typically 1.3-1.4x salary) **Example:** ``` Engineer: $150K salary × 1.35 = $202K fully-loaded Sales Rep: $100K OTE × 1.30 = $130K fully-loaded ``` ### Step 5: Project Cash Flow Calculate monthly cash position and runway. **Monthly Cash Flow:** ``` Beginning Cash + Revenue Collected (consider payment terms) - Operating Expenses Paid - CapEx = Ending Cash ``` **Runway Calculation:** ``` If Ending Cash < 0: Funding Need = Negative Cash Balance Runway = 0 Else: Runway = Ending Cash / Average Monthly Burn ``` ### Step 6: Calculate Key Metrics Track metrics that matter for stage. **Revenue Metrics:** - MRR / ARR - Growth rate (MoM, YoY) - Revenue by segment or cohort **Unit Economics:** - CAC (Customer Acquisition Cost) - LTV (Lifetime Value) - CAC Payback Period - LTV / CAC Ratio **Efficiency Metrics:** - Burn multiple (Net Burn / Net New ARR) - Magic number (Net New ARR / S&M Spend) - Rule of 40 (Growth % + Profit Margin %) **Cash Metrics:** - Monthly burn rate - Runway (months) - Cash efficiency ### Step 7: Scenario Analysis Create three scenarios with different assumptions. **Variable Assumptions:** - Customer acquisition rate (±30%) - Churn rate (±20%) - Average contract value (±15%) - CAC (±25%) **Fixed Assumptions:** - Pricing structure - Core operating expenses - Hiring plan (adjust timing, not roles) ## Business Model Templates ### SaaS Financial Model **Revenue Drivers:** - New MRR (customers × ARPU) - Expansion MRR (upsells) - Contraction MRR (downgrades) - Churned MRR (lost customers) **Key Ratios:** - Gross margin: 75-85% - S&M as % revenue: 40-60% (early stage) - CAC payback: < 12 months - Net retention: 100-120% **Example Projection:** ``` Year 1: $500K ARR, 50 customers, $100K MRR by Dec Year 2: $2.5M ARR, 200 customers, $208K MRR by Dec Year 3: $8M ARR, 600 customers, $667K MRR by Dec ``` ### Marketplace Financial Model **Revenue Drivers:** - GMV (Gross Merchandise Value) - Take rate (% of GMV) - Net revenue = GMV × Take rate **Key Ratios:** - Take rate: 10-30% depending on category - CAC for buyers vs. sellers - Contribution margin: 60-70% **Example Projection:** ``` Year 1: $5M GMV, 15% take rate = $750K revenue Year 2: $20M GMV, 15% take rate = $3M revenue Year 3: $60M GMV, 15% take rate = $9M revenue ``` ### E-Commerce Financial Model **Revenue Drivers:** - Traffic (visitors) - Conversion rate - Average order value (AOV) - Purchase frequency **Key Ratios:** - Gross margin: 40-60% - Contribution margin: 20-35% - CAC payback: 3-6 months ### Services / Agency Financial Model **Revenue Drivers:** - Billable hours or projects - Hourly rate or project fee - Utilization rate - Team capacity **Key Ratios:** - Gross margin: 50-70% - Utilization: 70-85% - Revenue per employee ## Fundraising Integration ### Funding Scenario Modeling **Pre-Money Valuation:** Based on metrics and comparables. **Dilution:** ``` Post-Money = Pre-Money + Investment Dilution % = Investment / Post-Money ``` **Use of Funds:** Allocate funding to extend runway and achieve milestones. **Example:** ``` Raise: $5M at $20M pre-money Post-Money: $25M Dilution: 20% Use of Funds: - Product Development: $2M (40%) - Sales & Marketing: $2M (40%) - G&A and Operations: $0.5M (10%) - Working Capital: $0.5M (10%) ``` ### Milestone-Based Planning **Identify Key Milestones:** - Product launch - First $1M ARR - Break-even on CAC - Series A fundraise **Funding Amount:** Ensure runway to achieve next milestone + 6 months buffer. ## Common Pitfalls **Pitfall 1: Overly Optimistic Revenue** - New startups rarely hit aggressive projections - Use conservative customer acquisition assumptions - Model realistic churn rates **Pitfall 2: Underestimating Costs** - Add 20% buffer to expense estimates - Include fully-loaded compensation - Account for software and tools **Pitfall 3: Ignoring Cash Flow Timing** - Revenue ≠ cash (payment terms) - Expenses paid before revenue collected - Model cash conversion carefully **Pitfall 4: Static Headcount** - Hiring takes time (3-6 months to fill roles) - Ramp time for productivity (3-6 months) - Account for attrition (10-15% annually) **Pitfall 5: Not Scenario Planning** - Single scenario is never accurate - Always model conservative case - Plan for what you'll do if base case fails ## Model Validation **Sanity Checks:** - [ ] Revenue growth rate is achievable (3x in Year 2, 2x in Year 3) - [ ] Unit economics are realistic (LTV/CAC > 3, payback < 18 months) - [ ] Burn multiple is reasonable (< 2.0 in Year 2-3) - [ ] Headcount scales with revenue (revenue per employee growing) - [ ] Gross margin is appropriate for business model - [ ] S&M spending aligns with CAC and growth targets **Benchmark Against Peers:** Compare key metrics to similar companies at similar stage. **Investor Feedback:** Share model with advisors or investors for feedback on assumptions. ## Additional Resources ### Reference Files For detailed model structures and advanced techniques: - **`references/model-templates.md`** - Complete financial model templates by business model - **`references/unit-economics.md`** - Deep dive on CAC, LTV, payback, and efficiency metrics - **`references/fundraising-scenarios.md`** - Modeling funding rounds and dilution ### Example Files Working financial models with formulas: - **`examples/saas-financial-model.md`** - Complete 3-year SaaS model with cohort analysis - **`examples/marketplace-model.md`** - Marketplace GMV and take rate projections - **`examples/scenario-analysis.md`** - Three-scenario framework with sensitivities ## Quick Start To create a startup financial model: 1. **Define business model** - Revenue drivers and pricing 2. **Project revenue** - Cohort-based with retention 3. **Model costs** - COGS, S&M, R&D, G&A by month 4. **Plan headcount** - Hiring by role and department 5. **Calculate cash flow** - Revenue - expenses = burn/runway 6. **Compute metrics** - CAC, LTV, burn multiple, runway 7. **Create scenarios** - Conservative, base, optimistic 8. **Validate assumptions** - Sanity check and benchmark 9. **Integrate fundraising** - Model funding rounds and milestones For complete templates and formulas, reference the `references/` and `examples/` files.
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🤖 Auto-discovered
🤖system prompt•7 months ago

startup-metrics-framework

This skill should be used when the user asks about "key startup

business
⭐1
# Startup Metrics Framework Comprehensive guide to tracking, calculating, and optimizing key performance metrics for different startup business models from seed through Series A. ## Overview Track the right metrics at the right stage. Focus on unit economics, growth efficiency, and cash management metrics that matter for fundraising and operational excellence. ## Universal Startup Metrics ### Revenue Metrics **MRR (Monthly Recurring Revenue)** ``` MRR = Σ (Active Subscriptions × Monthly Price) ``` **ARR (Annual Recurring Revenue)** ``` ARR = MRR × 12 ``` **Growth Rate** ``` MoM Growth = (This Month MRR - Last Month MRR) / Last Month MRR YoY Growth = (This Year ARR - Last Year ARR) / Last Year ARR ``` **Target Benchmarks:** - Seed stage: 15-20% MoM growth - Series A: 10-15% MoM growth, 3-5x YoY - Series B+: 100%+ YoY (Rule of 40) ### Unit Economics **CAC (Customer Acquisition Cost)** ``` CAC = Total S&M Spend / New Customers Acquired ``` Include: Sales salaries, marketing spend, tools, overhead **LTV (Lifetime Value)** ``` LTV = ARPU × Gross Margin% × (1 / Churn Rate) ``` Simplified: ``` LTV = ARPU × Average Customer Lifetime × Gross Margin% ``` **LTV:CAC Ratio** ``` LTV:CAC = LTV / CAC ``` **Benchmarks:** - LTV:CAC > 3.0 = Healthy - LTV:CAC 1.0-3.0 = Needs improvement - LTV:CAC < 1.0 = Unsustainable **CAC Payback Period** ``` CAC Payback = CAC / (ARPU × Gross Margin%) ``` **Benchmarks:** - < 12 months = Excellent - 12-18 months = Good - > 24 months = Concerning ### Cash Efficiency Metrics **Burn Rate** ``` Monthly Burn = Monthly Revenue - Monthly Expenses ``` Negative burn = losing money (typical early-stage) **Runway** ``` Runway (months) = Cash Balance / Monthly Burn Rate ``` **Target:** Always maintain 12-18 months runway **Burn Multiple** ``` Burn Multiple = Net Burn / Net New ARR ``` **Benchmarks:** - < 1.0 = Exceptional efficiency - 1.0-1.5 = Good - 1.5-2.0 = Acceptable - > 2.0 = Inefficient Lower is better (spending less to generate ARR) ## SaaS Metrics ### Revenue Composition **New MRR** New customers × ARPU **Expansion MRR** Upsells and cross-sells from existing customers **Contraction MRR** Downgrades from existing customers **Churned MRR** Lost customers **Net New MRR Formula:** ``` Net New MRR = New MRR + Expansion MRR - Contraction MRR - Churned MRR ``` ### Retention Metrics **Logo Retention** ``` Logo Retention = (Customers End - New Customers) / Customers Start ``` **Dollar Retention (NDR - Net Dollar Retention)** ``` NDR = (ARR Start + Expansion - Contraction - Churn) / ARR Start ``` **Benchmarks:** - NDR > 120% = Best-in-class - NDR 100-120% = Good - NDR < 100% = Needs work **Gross Retention** ``` Gross Retention = (ARR Start - Churn - Contraction) / ARR Start ``` **Benchmarks:** - > 90% = Excellent - 85-90% = Good - < 85% = Concerning ### SaaS-Specific Metrics **Magic Number** ``` Magic Number = Net New ARR (quarter) / S&M Spend (prior quarter) ``` **Benchmarks:** - > 0.75 = Efficient, ready to scale - 0.5-0.75 = Moderate efficiency - < 0.5 = Inefficient, don't scale yet **Rule of 40** ``` Rule of 40 = Revenue Growth Rate% + Profit Margin% ``` **Benchmarks:** - > 40% = Excellent - 20-40% = Acceptable - < 20% = Needs improvement **Example:** 50% growth + (10%) margin = 40% ✓ **Quick Ratio** ``` Quick Ratio = (New MRR + Expansion MRR) / (Churned MRR + Contraction MRR) ``` **Benchmarks:** - > 4.0 = Healthy growth - 2.0-4.0 = Moderate - < 2.0 = Churn problem ## Marketplace Metrics ### GMV (Gross Merchandise Value) **Total Transaction Volume:** ``` GMV = Σ (Transaction Value) ``` **Growth Rate:** ``` GMV Growth Rate = (Current Period GMV - Prior Period GMV) / Prior Period GMV ``` **Target:** 20%+ MoM early-stage ### Take Rate ``` Take Rate = Net Revenue / GMV ``` **Typical Ranges:** - Payment processors: 2-3% - E-commerce marketplaces: 10-20% - Service marketplaces: 15-25% - High-value B2B: 5-15% ### Marketplace Liquidity **Time to Transaction** How long from listing to sale/match? **Fill Rate** % of requests that result in transaction **Repeat Rate** % of users who transact multiple times **Benchmarks:** - Fill rate > 80% = Strong liquidity - Repeat rate > 60% = Strong retention ### Marketplace Balance **Supply/Demand Ratio:** Track relative growth of supply and demand sides. **Warning Signs:** - Too much supply: Low fill rates, frustrated suppliers - Too much demand: Long wait times, frustrated customers **Goal:** Balanced growth (1:1 ratio ideal, but varies by model) ## Consumer/Mobile Metrics ### Engagement Metrics **DAU (Daily Active Users)** Unique users active each day **MAU (Monthly Active Users)** Unique users active each month **DAU/MAU Ratio** ``` DAU/MAU = DAU / MAU ``` **Benchmarks:** - > 50% = Exceptional (daily habit) - 20-50% = Good - < 20% = Weak engagement **Session Frequency** Average sessions per user per day/week **Session Duration** Average time spent per session ### Retention Curves **Day 1 Retention:** % users who return next day **Day 7 Retention:** % users active 7 days after signup **Day 30 Retention:** % users active 30 days after signup **Benchmarks (Day 30):** - > 40% = Excellent - 25-40% = Good - < 25% = Weak **Retention Curve Shape:** - Flattening curve = good (users becoming habitual) - Steep decline = poor product-market fit ### Viral Coefficient (K-Factor) ``` K-Factor = Invites per User × Invite Conversion Rate ``` **Example:** 10 invites/user × 20% conversion = 2.0 K-factor **Benchmarks:** - K > 1.0 = Viral growth - K = 0.5-1.0 = Strong referrals - K < 0.5 = Weak virality ## B2B Metrics ### Sales Efficiency **Win Rate** ``` Win Rate = Deals Won / Total Opportunities ``` **Target:** 20-30% for new sales team, 30-40% mature **Sales Cycle Length** Average days from opportunity to close **Shorter is better:** - SMB: 30-60 days - Mid-market: 60-120 days - Enterprise: 120-270 days **Average Contract Value (ACV)** ``` ACV = Total Contract Value / Contract Length (years) ``` ### Pipeline Metrics **Pipeline Coverage** ``` Pipeline Coverage = Total Pipeline Value / Quota ``` **Target:** 3-5x coverage (3-5x pipeline needed to hit quota) **Conversion Rates by Stage:** - Lead → Opportunity: 10-20% - Opportunity → Demo: 50-70% - Demo → Proposal: 30-50% - Proposal → Close: 20-40% ## Metrics by Stage ### Pre-Seed (Product-Market Fit) **Focus Metrics:** 1. Active users growth 2. User retention (Day 7, Day 30) 3. Core engagement (sessions, features used) 4. Qualitative feedback (NPS, interviews) **Don't worry about:** - Revenue (may be zero) - CAC (not optimizing yet) - Unit economics ### Seed ($500K-$2M ARR) **Focus Metrics:** 1. MRR growth rate (15-20% MoM) 2. CAC and LTV (establish baseline) 3. Gross retention (> 85%) 4. Core product engagement **Start tracking:** - Sales efficiency - Burn rate and runway ### Series A ($2M-$10M ARR) **Focus Metrics:** 1. ARR growth (3-5x YoY) 2. Unit economics (LTV:CAC > 3, payback < 18 months) 3. Net dollar retention (> 100%) 4. Burn multiple (< 2.0) 5. Magic number (> 0.5) **Mature tracking:** - Rule of 40 - Sales efficiency - Pipeline coverage ## Metric Tracking Best Practices ### Data Infrastructure **Requirements:** - Single source of truth (analytics platform) - Real-time or daily updates - Automated calculations - Historical tracking **Tools:** - Mixpanel, Amplitude (product analytics) - ChartMogul, Baremetrics (SaaS metrics) - Looker, Tableau (BI dashboards) ### Reporting Cadence **Daily:** - MRR, active users - Sign-ups, conversions **Weekly:** - Growth rates - Retention cohorts - Sales pipeline **Monthly:** - Full metric suite - Board reporting - Investor updates **Quarterly:** - Trend analysis - Benchmarking - Strategy review ### Common Mistakes **Mistake 1: Vanity Metrics** Don't focus on: - Total users (without retention) - Page views (without engagement) - Downloads (without activation) Focus on actionable metrics tied to value. **Mistake 2: Too Many Metrics** Track 5-7 core metrics intensely, not 50 loosely. **Mistake 3: Ignoring Unit Economics** CAC and LTV are critical even at seed stage. **Mistake 4: Not Segmenting** Break down metrics by customer segment, channel, cohort. **Mistake 5: Gaming Metrics** Optimize for real business outcomes, not dashboard numbers. ## Investor Metrics ### What VCs Want to See **Seed Round:** - MRR growth rate - User retention - Early unit economics - Product engagement **Series A:** - ARR and growth rate - CAC payback < 18 months - LTV:CAC > 3.0 - Net dollar retention > 100% - Burn multiple < 2.0 **Series B+:** - Rule of 40 > 40% - Efficient growth (magic number) - Path to profitability - Market leadership metrics ### Metric Presentation **Dashboard Format:** ``` Current MRR: $250K (↑ 18% MoM) ARR: $3.0M (↑ 280% YoY) CAC: $1,200 | LTV: $4,800 | LTV:CAC = 4.0x NDR: 112% | Logo Retention: 92% Burn: $180K/mo | Runway: 18 months ``` **Include:** - Current value - Growth rate or trend - Context (target, benchmark) ## Additional Resources ### Reference Files - **`references/metric-definitions.md`** - Complete definitions and formulas for 50+ metrics - **`references/benchmarks-by-stage.md`** - Target ranges for each metric by company stage - **`references/calculation-examples.md`** - Step-by-step calculation examples ### Example Files - **`examples/saas-metrics-dashboard.md`** - Complete metrics suite for B2B SaaS company - **`examples/marketplace-metrics.md`** - Marketplace-specific metrics with examples - **`examples/investor-metrics-deck.md`** - How to present metrics for fundraising ## Quick Start To implement startup metrics framework: 1. **Identify business model** - SaaS, marketplace, consumer, B2B 2. **Choose 5-7 core metrics** - Based on stage and model 3. **Establish tracking** - Set up analytics and dashboards 4. **Calculate unit economics** - CAC, LTV, payback 5. **Set targets** - Use benchmarks for goals 6. **Review regularly** - Weekly for core metrics 7. **Share with team** - Align on goals and progress 8. **Update investors** - Monthly/quarterly reporting For detailed definitions, benchmarks, and examples, see `references/` and `examples/`.
👍0
👁️0
🤖 Auto-discovered
🤖system prompt•7 months ago

team-composition-analysis

This skill should be used when the user asks to "plan team

business
⭐1
# Team Composition Analysis Design optimal team structures, hiring plans, compensation strategies, and equity allocation for early-stage startups from pre-seed through Series A. ## Overview Build the right team at the right time with appropriate compensation and equity. Plan role-by-role hiring aligned with revenue milestones, budget constraints, and market benchmarks. ## Team Structure by Stage ### Pre-Seed (0-$500K ARR) **Team Size: 2-5 people** **Core Roles:** - Founders (2-3): Product, engineering, business - First engineer (if needed) - Contract roles: Design, marketing **Focus:** Build and validate product-market fit ### Seed ($500K-$2M ARR) **Team Size: 5-15 people** **Key Hires:** - Engineering lead + 2-3 engineers - First sales/business development - Product manager - Marketing/growth lead **Focus:** Scale product and prove repeatable sales ### Series A ($2M-$10M ARR) **Team Size: 15-50 people** **Department Build-Out:** - Engineering (40%): 6-20 people - Sales & Marketing (30%): 5-15 people - Customer Success (10%): 2-5 people - G&A (10%): 2-5 people - Product (10%): 2-5 people **Focus:** Scale revenue and build repeatable processes ## Role-by-Role Planning ### Engineering Team **Pre-Seed:** - Founders write code - 0-1 contract developers **Seed:** - Engineering Lead (first $150K-$180K) - 2-3 Full-Stack Engineers ($120K-$150K) - 1 Frontend or Backend Specialist ($130K-$160K) **Series A:** - VP Engineering ($180K-$250K + equity) - 2-3 Senior Engineers ($150K-$180K) - 3-5 Mid-Level Engineers ($120K-$150K) - 1-2 Junior Engineers ($90K-$120K) - 1 DevOps/Infrastructure ($140K-$170K) ### Sales & Marketing **Pre-Seed:** - Founders do sales - Contract marketing help **Seed:** - First Sales Hire / Head of Sales ($120K-$150K + commission) - Marketing/Growth Lead ($100K-$140K) - SDR or BDR (if B2B) ($50K-$70K + commission) **Series A:** - VP Sales ($150K-$200K + commission + equity) - 3-5 Account Executives ($80K-$120K + commission) - 2-3 SDRs/BDRs ($50K-$70K + commission) - Marketing Manager ($90K-$130K) - Content/Demand Gen ($70K-$100K) ### Product Team **Pre-Seed:** - Founder as product lead **Seed:** - First Product Manager ($120K-$150K) - Contract designer **Series A:** - Head of Product ($150K-$180K) - 1-2 Product Managers ($120K-$150K) - Product Designer ($100K-$140K) - UX Researcher (optional) ($90K-$130K) ### Customer Success **Pre-Seed:** - Founders handle support **Seed:** - First CS hire (optional) ($60K-$90K) **Series A:** - CS Manager ($100K-$130K) - 2-4 CS Representatives ($60K-$90K) - Support Engineer (technical) ($80K-$120K) ### G&A (General & Administrative) **Pre-Seed:** - Contractors (accounting, legal) **Seed:** - Operations/Office Manager ($70K-$100K) - Contract CFO **Series A:** - CFO or Finance Lead ($150K-$200K) - Recruiter ($80K-$120K) - Office Manager / EA ($60K-$90K) ## Compensation Strategy ### Base Salary Benchmarks (US, 2024) **Engineering:** - Junior: $90K-$120K - Mid-Level: $120K-$150K - Senior: $150K-$180K - Staff/Principal: $180K-$220K - Engineering Manager: $160K-$200K - VP Engineering: $180K-$250K **Sales:** - SDR/BDR: $50K-$70K base + $50K-$70K commission - Account Executive: $80K-$120K base + $80K-$120K commission - Sales Manager: $120K-$160K base + $80K-$120K commission - VP Sales: $150K-$200K base + $150K-$200K commission **Product:** - Product Manager: $120K-$150K - Senior PM: $150K-$180K - Head of Product: $150K-$180K - VP Product: $180K-$220K **Marketing:** - Marketing Manager: $90K-$130K - Content/Demand Gen: $70K-$100K - Head of Marketing: $130K-$170K - VP Marketing: $150K-$200K **Customer Success:** - CS Representative: $60K-$90K - CS Manager: $100K-$130K - VP Customer Success: $140K-$180K ### Total Compensation Formula ``` Total Comp = Base Salary × 1.30 (benefits & taxes) + Equity Value ``` **Fully-Loaded Cost:** - Base salary - Payroll taxes (7.65% FICA) - Benefits (health insurance, 401k): $10K-$15K per employee - Other (workspace, equipment, software): $5K-$10K per employee **Rule of Thumb:** Multiply base salary by 1.3-1.4 for fully-loaded cost ### Geographic Adjustments **San Francisco / New York:** +20-30% above benchmarks **Seattle / Boston / Los Angeles:** +10-20% **Austin / Denver / Chicago:** +0-10% **Remote / Other US Cities:** -10-20% **International:** Varies widely by country ## Equity Allocation ### Equity by Role and Stage **Founders:** - First founder: 40-60% - Second founder: 20-40% - Third founder: 10-20% - Vesting: 4 years with 1-year cliff **Early Employees (Pre-Seed):** - First engineer: 0.5-2.0% - First 5 employees: 0.25-1.0% each **Seed Stage Hires:** - VP/Head level: 0.5-1.5% - Senior IC: 0.1-0.5% - Mid-level: 0.05-0.25% - Junior: 0.01-0.1% **Series A Hires:** - C-level (CTO, CFO): 1.0-3.0% - VP level: 0.3-1.0% - Director level: 0.1-0.5% - Senior IC: 0.05-0.2% - Mid-level: 0.01-0.1% - Junior: 0.005-0.05% ### Equity Pool Sizing **Option Pool by Round:** - Pre-Seed: 10-15% reserved - Seed: 10-15% top-up - Series A: 10-15% top-up - Series B+: 5-10% per round **Pre-Funding Dilution:** Investors often require option pool creation before investment, diluting founders. **Example:** ``` Pre-money: $10M Investors want 15% option pool post-money Calculation: Post-money: $15M ($10M + $5M investment) Option pool: $2.25M (15% × $15M) Founders diluted by pool creation before new money ``` ## Organizational Design ### Reporting Structure **Pre-Seed:** ``` Founders (flat structure) ├── Contractors └── First hires (report to founders) ``` **Seed:** ``` CEO ├── Engineering Lead (2-4 engineers) ├── Sales/Growth Lead (1-2 reps) ├── Product Manager └── Operations ``` **Series A:** ``` CEO ├── CTO / VP Engineering (6-20 people) │ ├── Engineering Manager(s) │ └── Individual Contributors ├── VP Sales (5-15 people) │ ├── Sales Manager │ ├── Account Executives │ └── SDRs ├── Head of Product (2-5 people) │ ├── Product Managers │ └── Designers ├── Head of Customer Success (2-5 people) └── CFO / Finance Lead (2-5 people) ├── Recruiter └── Operations ``` ### Span of Control **Manager Ratios:** - First-line managers: 4-8 direct reports - Directors: 3-5 direct reports (managers) - VPs: 3-5 direct reports (directors) - CEO: 5-8 direct reports (executive team) ## Full-Time vs. Contract ### Use Full-Time for: - Core product development - Sales (revenue-generating roles) - Mission-critical operations - Institutional knowledge roles ### Use Contractors for: - Specialized short-term needs (legal, accounting) - Variable workload (design, marketing campaigns) - Skills outside core competency - Testing role before FTE hire - Geographic expansion before permanent presence ### Cost Comparison **Full-Time:** - Lower hourly cost - Benefits and overhead - Long-term commitment - Cultural fit matters **Contract:** - Higher hourly rate ($75-$200/hour vs. $40-$100/hour FTE equivalent) - No benefits or overhead - Flexible engagement - Easier to scale up/down ## Hiring Velocity ### Realistic Timeline **Role Opening to Hire:** - Junior: 6-8 weeks - Mid-Level: 8-12 weeks - Senior: 12-16 weeks - Executive: 16-24 weeks **Time to Productivity:** - Junior: 4-6 months - Mid-Level: 2-4 months - Senior: 1-3 months - Executive: 3-6 months ### Planning Buffer Always add 2-3 months buffer to hiring plans. **Example:** If need engineer by July 1: - Start recruiting: April 1 (12 weeks) - Productivity: September 1 (2 months ramp) ## Budget Planning ### Compensation as % of Revenue **Early Stage (Seed):** - Total comp: 120-150% of revenue (burning cash to grow) - Engineering: 50-60% - Sales: 30-40% - Other: 20-30% **Growth Stage (Series A):** - Total comp: 70-100% of revenue - Engineering: 35-45% - Sales: 25-35% - Other: 20-30% ### Headcount Budget Formula ``` Total Comp Budget = Σ (Role Count × Fully-Loaded Cost × % of Year) Example: 3 Engineers × $202K × 100% = $606K 2 AEs × $230K × 75% (mid-year start) = $345K 1 PM × $162K × 100% = $162K Total: $1.1M ``` ## Additional Resources ### Reference Files - **`references/compensation-benchmarks.md`** - Detailed salary data by role, level, and location - **`references/equity-calculator.md`** - Equity sizing formulas and dilution scenarios ### Example Files - **`examples/seed-stage-hiring-plan.md`** - Complete hiring plan for seed-stage SaaS company - **`examples/org-chart-evolution.md`** - Organizational design from 5 to 50 people ## Quick Start To plan team composition: 1. **Identify stage** - Pre-seed, seed, or Series A 2. **Define roles** - What functions are needed now 3. **Prioritize hires** - Critical path for business goals 4. **Set compensation** - Base salary + equity by level 5. **Plan timeline** - Account for recruiting and ramp time 6. **Calculate budget** - Fully-loaded cost × headcount 7. **Design org chart** - Reporting structure and span of control 8. **Allocate equity** - Fair allocation that preserves pool For detailed compensation benchmarks and hiring plan templates, see `references/` and `examples/`.
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