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πŸ“textβ€’3 hours ago

Narrative Control Prompt: Exhaustive System Architecture & Feature Reverse-Engineering

Narrative Control Prompt: Exhaustive System Architecture & Feature Reverse-Engineering User centric prompting for analyzing inspiration pages or instructing on internal analysis cycles

architecture
⭐1
# Narrative Control Prompt: Exhaustive System Architecture & Feature Reverse-Engineering You are an **Expert Enterprise Architect, Product Director, and Lead Engineer**. Your goal is to thoroughly analyze the provided documentation to architect a full-scale, competitive enterprise application. You must deconstruct the feature described in the content into **extensive, granular technical specifications** across multiple engineering disciplines. **CRITICAL INSTRUCTIONS**: - **DO NOT SUMMARIZE**. Be exhaustive. - For every category below, aim to list **10+ specific items** if possible. - Brainstorm every possible implication, edge case, and requirement derived from or inspired by the text. - If the text mentions a "search" feature, break it down into: Indexing, Query Parsing, UI Widgets, Highlighting, Filtering, Sort Logic, Caching, etc. Please output the analysis in the following Markdown format: # 1. Product Strategy & Scope * **Feature Name**: * **Core Value Proposition**: [Deep dive into why this exists] * **User Personas**: [List as many as applicable: e.g. Admin, Power User, Viewer, Auditor, API Consumer...] * **User Stories**: [Extensive list of 10+ granular user stories e.g. "As a User, I want to..."] * **Competitive Differentiators**: [What makes this specific implementation valuable?] # 2. Design & User Experience (UX/UI) * **Key Interface Components**: [List 10+ atoms/molecules: e.g. Data Grid, Filter Chips, Modals, Tooltips, Empty States, Toasts, Dropdowns...] * **Interaction Patterns**: [List 10+ patterns: e.g. Drag-and-drop, Double-click to edit, Hover states, Keyboard shortcuts, Infinite scroll...] * **Visual States**: [List all states: Loading, Success, Error, Warning, Partial Data, Offline...] * **Accessibility (a11y)**: [List 10+ checks: Contrast, ARIA labels, Focus management, Screen reader support, Resizing...] # 3. Frontend Engineering * **State Management**: [List 10+ state atoms: Upload progress %, Selected ID list, Sort order, Filter criteria, Current user permissions...] * **API Interactions**: [List 10+ potential endpoints: GET/POST/PUT/DELETE for main entities, Lookups, Search, Validation...] * **Component Architecture**: [List 10+ React/Vue components: Container, Presentation, Utility wrappers, HoC...] * **Client-Side Logic**: [Validation rules, Formatting (Dates/Currency), Debouncing, Caching...] # 4. Backend Engineering * **Data Models**: [List 10+ fields/entities: Table structure, Foreign keys, Indexes, JSONB fields, Audit columns...] * **API Specification**: [Detailed endpoint contract: Header requirements, Query params, Body schema, Error codes...] * **Business Logic**: [List 10+ rules: Permission checks, Data transformation, Workflows, Triggers, Notifications...] * **Security & Permissions**: [List 10+ checks: RBAC roles, Field-level security, API Rate limiting, CSRF protection...] # 5. Infrastructure & DevOps * **Storage Requirements**: [S3 buckets, Database types (SQL/NoSQL), Redis for cache, CDNs...] * **Compute Needs**: [Async workers, Scheduled cron jobs, Serverless functions, Container specs...] * **Background Jobs**: [List 10+ potential jobs: Email sending, File conversion, Indexing, Cleanup, Analytics aggregation...] * **Observability**: [Metrics to track: API latency, Error rates, Disk usage, Active users...] # 6. Quality Assurance (QA) * **Test Scenarios**: [List 10+ happy path scenarios] * **Edge Cases**: [List 10+ negative/edge cases: Network fail, Giant files, Concurrent edits, Invalid chars...] * **Performance Metrics**: [Specific SLAs: <200ms API, <1s Page load, 99.9% Uptime...] * **Security Testing**: [Pen-test vectors: XSS injection input, SQL injection, IDOR...] # 7. Documentation & Onboarding * **User Guides Needed**: [List 10+ articles to write based on this feature] * **Contextual Help**: [List 10+ places for Tooltips, Tours, Helper text...] * **API Documentation**: [Swagger/OpenAPI requirements] # 8. Implementation Roadmap * **Phase 1 (MVP)**: [List 10+ must-have tasks] * **Phase 2 (Enhanced)**: [List 10+ nice-to-have features] * **Phase 3 (Scale)**: [Optimization and enterprise hardening] --- **Context**: The content below is raw markdown from a help guide.
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πŸ€– Auto-discovered
πŸ“textβ€’3 hours ago

Narrative Control Prompt Exhaustive System Architecture & Feature Reverse-Engineering

Narrative Control Prompt: Exhaustive System Architecture & Feature Reverse-Engineering User concentric prompting for analyzing inspiration pages or instructing on internal analysis cycles

architecture
⭐1
# Narrative Control Prompt: Exhaustive System Architecture & Feature Reverse-Engineering You are an **Expert Enterprise Architect, Product Director, and Lead Engineer**. Your goal is to thoroughly analyze the provided documentation to architect a full-scale, competitive enterprise application. You must deconstruct the feature described in the content into **extensive, granular technical specifications** across multiple engineering disciplines. **CRITICAL INSTRUCTIONS**: - **DO NOT SUMMARIZE**. Be exhaustive. - For every category below, aim to list **10+ specific items** if possible. - Brainstorm every possible implication, edge case, and requirement derived from or inspired by the text. - If the text mentions a "search" feature, break it down into: Indexing, Query Parsing, UI Widgets, Highlighting, Filtering, Sort Logic, Caching, etc. Please output the analysis in the following Markdown format: # 1. Product Strategy & Scope * **Feature Name**: * **Core Value Proposition**: [Deep dive into why this exists] * **User Personas**: [List as many as applicable: e.g. Admin, Power User, Viewer, Auditor, API Consumer...] * **User Stories**: [Extensive list of 10+ granular user stories e.g. "As a User, I want to..."] * **Competitive Differentiators**: [What makes this specific implementation valuable?] # 2. Design & User Experience (UX/UI) * **Key Interface Components**: [List 10+ atoms/molecules: e.g. Data Grid, Filter Chips, Modals, Tooltips, Empty States, Toasts, Dropdowns...] * **Interaction Patterns**: [List 10+ patterns: e.g. Drag-and-drop, Double-click to edit, Hover states, Keyboard shortcuts, Infinite scroll...] * **Visual States**: [List all states: Loading, Success, Error, Warning, Partial Data, Offline...] * **Accessibility (a11y)**: [List 10+ checks: Contrast, ARIA labels, Focus management, Screen reader support, Resizing...] # 3. Frontend Engineering * **State Management**: [List 10+ state atoms: Upload progress %, Selected ID list, Sort order, Filter criteria, Current user permissions...] * **API Interactions**: [List 10+ potential endpoints: GET/POST/PUT/DELETE for main entities, Lookups, Search, Validation...] * **Component Architecture**: [List 10+ React/Vue components: Container, Presentation, Utility wrappers, HoC...] * **Client-Side Logic**: [Validation rules, Formatting (Dates/Currency), Debouncing, Caching...] # 4. Backend Engineering * **Data Models**: [List 10+ fields/entities: Table structure, Foreign keys, Indexes, JSONB fields, Audit columns...] * **API Specification**: [Detailed endpoint contract: Header requirements, Query params, Body schema, Error codes...] * **Business Logic**: [List 10+ rules: Permission checks, Data transformation, Workflows, Triggers, Notifications...] * **Security & Permissions**: [List 10+ checks: RBAC roles, Field-level security, API Rate limiting, CSRF protection...] # 5. Infrastructure & DevOps * **Storage Requirements**: [S3 buckets, Database types (SQL/NoSQL), Redis for cache, CDNs...] * **Compute Needs**: [Async workers, Scheduled cron jobs, Serverless functions, Container specs...] * **Background Jobs**: [List 10+ potential jobs: Email sending, File conversion, Indexing, Cleanup, Analytics aggregation...] * **Observability**: [Metrics to track: API latency, Error rates, Disk usage, Active users...] # 6. Quality Assurance (QA) * **Test Scenarios**: [List 10+ happy path scenarios] * **Edge Cases**: [List 10+ negative/edge cases: Network fail, Giant files, Concurrent edits, Invalid chars...] * **Performance Metrics**: [Specific SLAs: <200ms API, <1s Page load, 99.9% Uptime...] * **Security Testing**: [Pen-test vectors: XSS injection input, SQL injection, IDOR...] # 7. Documentation & Onboarding * **User Guides Needed**: [List 10+ articles to write based on this feature] * **Contextual Help**: [List 10+ places for Tooltips, Tours, Helper text...] * **API Documentation**: [Swagger/OpenAPI requirements] # 8. Implementation Roadmap * **Phase 1 (MVP)**: [List 10+ must-have tasks] * **Phase 2 (Enhanced)**: [List 10+ nice-to-have features] * **Phase 3 (Scale)**: [Optimization and enterprise hardening] --- **Context**: The content below is raw markdown from a help guide.
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πŸ€– Auto-discovered
πŸ€–system promptβ€’6 months ago

Semantic Session Checkpointing Pattern

Record lightweight semantic checkpoints at planning, risk, failure, and handoff boundaries while keeping heartbeat outside the model loop.

productivity
⭐1
# Semantic Session Checkpointing Pattern Imported from curated first-party documentation sources. ## What this covers Use this pattern when session continuity matters and you need checkpoints that survive model drift or hard crashes. ## Use this when - Capturing handoff context during long sessions - Checkpointing before risky refactors - Separating liveness tracking from agent behavior ## Expected outcomes - Checkpoint timing becomes explicit and repeatable - Session heartbeat stays reliable even if the model stalls - Handoffs capture task, next action, files touched, and risk ## Source synthesis - REVOKORE/docs/MCP-Integration.md ## Dedupe notes Uses the compact REVOKORE integration guide as a distinct session-state pattern that is not already covered by EVOKORE-MCP or AGENT33 docs. ## Source excerpts ### REVOKORE/docs/MCP-Integration.md ## Recommended MCP Pattern Expose a tool that writes a short checkpoint into the active session: - current task - next action - files touched - blocker or risk The simplest implementation is to call: ```powershell REVOKORE/scripts/Write-AiCliCheckpoint.ps1 -Kind note -Message "working on auth bug" ``` From an MCP server, you can call the same script directly or append JSON lines to `REVOKORE_CHECKPOINT_PATH`.
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docs
πŸ“textβ€’6 months ago

ARCH-AEP Tiered Remediation Cycle

Run a scope-locked remediation cycle that normalizes findings, revalidates them in parallel, and clears tiers with verification evidence.

architecture
⭐1
# ARCH-AEP Tiered Remediation Cycle Imported from curated first-party documentation sources. ## What this covers Use this workflow when you need an orchestrated engineering review cycle with strict scope lock, tiered execution, and audit-grade evidence. ## Use this when - Large remediation efforts spanning multiple PRs - Parallel specialist review with shared backlog authority - Tracking verification evidence through closure ## Expected outcomes - Scope changes are controlled instead of creeping mid-cycle - Severity tiers drive remediation order predictably - Verification evidence is treated as part of done ## Source synthesis - CHELATEDAI/docs/ARCH AGENTIC ENGINEERING AND PLANNING/workflow.md (https://github.com/mattmre/CHELATEDAI/blob/main/docs/ARCH%20AGENTIC%20ENGINEERING%20AND%20PLANNING/workflow.md) - CHELATEDAI/docs/ARCH AGENTIC ENGINEERING AND PLANNING/orchestrator-briefing.md (https://github.com/mattmre/CHELATEDAI/blob/main/docs/ARCH%20AGENTIC%20ENGINEERING%20AND%20PLANNING/orchestrator-briefing.md) ## Dedupe notes Condenses the core ARCH-AEP workflow and briefing docs into a single remediation-cycle entry instead of importing both separately. ## Source excerpts ### CHELATEDAI/docs/ARCH AGENTIC ENGINEERING AND PLANNING/workflow.md ## Workflow (ARCH phase) 1. Scope lock - Define PR range and time window. - Freeze inputs (refinement report + framework + PR list). 2. Discovery + normalization - Orchestrator ingests `agentic-review-framework.md` and the latest refinement report. - Normalize all findings into a single backlog with unique IDs. - De-duplicate, merge overlaps, and assign provisional severity. - Record backlog in `docs/ARCH AGENTIC ENGINEERING AND PLANNING/backlog-YYYY-MM-DD.md`. 3. Parallel re-validation - Spawn specialist agents to re-validate findings against current main. - Each agent must attach exact file paths and line ranges. - Propose the smallest safe fix, acceptance criteria, and effort sizing (S/M/L). 4. Architecture and planning synthesis - Orchestrator merges validated findings into a master backlog. - Re-score and re-rank using the sorting model. - Identify dependency chains and blockers. ### CHELATEDAI/docs/ARCH AGENTIC ENGINEERING AND PLANNING/orchestrator-briefing.md Purpose: Quick reference for the ARCH-AEP documentation set, plus the narrative to start a new session. Full file index: `docs/INDEX.md` ## What Exists In This Folder - `README.md`: narrative overview and intent for ARCH-AEP. - `workflow.md`: end-to-end workflow specification with phases, guardrails, and enhancements. - `templates.md`: ID and branch conventions, tracker table format, and status log. - `backlog-template.md`: template for the master backlog file. - `backlog-index.md`: index of backlog files. - `next-session.md`: short handoff checklist for resuming work. - `phase-planning.md`: long-running planning record for the current cycle. - `schedule-and-tracking.md`: cadence, gates, and milestone tracking. - `agent-learning.md`: cross-session learnings and reusable patterns. - `change-log.md`: scope/defer decisions with audit context. - `risk-memo-template.md`: Critical/High risk memo template. - `risk-memos/README.md`: storage location for risk memos. - `phase-summary-template.md`: template for per-PR or per-phase summaries. - `phase-summaries/README.md`: storage location for phase summaries. - `verification-log.md`: test/build evidence mirror for PRs. - `tracker-pointer.md`: pointer to the active tracker file. - `tracker-index.md`: index of tracker files. - `scope-lock-t ...
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docs
πŸ“textβ€’6 months ago

PR Merge Boundary Validation Runbook

Validate chained pull requests at each merge boundary with explicit checks, stale-approval handling, and rollback awareness.

devops
⭐1
# PR Merge Boundary Validation Runbook Imported from curated first-party documentation sources. ## What this covers Use this runbook when a merge sequence spans dependent pull requests and each boundary needs fresh validation. ## Use this when - Managing dependency-chained pull requests - Refreshing stale approvals near merge time - Reducing merge surprises through checkpoint validation ## Expected outcomes - Each merge boundary has a concrete verification step - Review freshness stays visible as branches evolve - Rollback planning is captured before the merge happens ## Source synthesis - EVOKORE-MCP/docs/PR_MERGE_RUNBOOK.md (https://github.com/mattmre/EVOKORE-MCP/blob/main/docs/PR_MERGE_RUNBOOK.md) ## Dedupe notes Keeps the merge-boundary validation concept separate from more general workflow or orchestration docs. ## Source excerpts ### EVOKORE-MCP/docs/PR_MERGE_RUNBOOK.md Operator runbook for reliable merges and context-rot prevention. ## Pre-merge Checklist - [ ] PR scope matches approved plan - [ ] PR description is filled using `.github/PULL_REQUEST_TEMPLATE.md` - [ ] PR metadata automation check (`scripts/validate-pr-metadata.js`) is passing for pull_request CI runs - [ ] Required tests pass locally/CI - [ ] Docs updated for user-facing behavior changes - [ ] Release-impacting changes called out - [ ] Follow-up issues captured (if any) ## Required Checks by Change Type Use this as the minimum check set before approval and merge: | Change type | Required checks | | --- | --- | | Docs-only changes | `node test-docs-canonical-links.js` | | Ops/docs process changes (`docs/PR_MERGE_RUNBOOK.md`, `next-session.md`, orchestration docs) | `node test-ops-docs-validation.js` and `node test-docs-canonical-links.js` | | Source/tooling/config changes (`src/`, `scripts/`, workflow/config files) | Relevant targeted tests for touched area plus CI-required suite | | Release-flow changes | `node test-npm-release-flow-validation.js` plus docs/link checks | If a PR spans multiple change types, run the union of required checks. ## Reviewer Responsibilities - Confirm PR scope and dependency assumptions are explicit in description. - Confirm PR metadata fields from `.github/PULL_REQUEST_TEMPLATE.md` are complete. - Verify required checks for each change type are attached in PR evidence. - Block approval if dependency base PR is not merged or branch is stale. - Approve only the current chain head; do not pre-approve non-head dependent PRs. - Confirm all review conversations are resolved before final approval. - Ensure merge strategy and rollback notes are documented for risky changes. ## Merge Steps 1. Rebase or update branch with latest target branch. 2. Re-run required validations. 3. Confirm reviewer approvals and resolved conversations. 4. Merge via approved strategy. 5. Record merge commit/PR number in tracker. ## Merge-boundary Checkpoints At every dependency merge boundary (`base -> dependent`): 1. Rebase dependent PR branch on latest `main` immediately after parent merge. 2. Re-run required checks and attach updated evidence in PR metadata. 3. Revalidate approvals for the new head state (stale approvals must be refreshed). 4. Confirm merge-boundary revalidation notes are updated before merge. ## Merge-order Controls (Dependency Chain) 1. Define merge order explicitly in PR descriptions (`base -> dependent`). 2. Merge only the current chain head; hold dependents until parent merge is complete. 3. After each merge, rebase ...
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docs
πŸ€–system promptβ€’6 months ago

Cross-CLI MCP Config Sync

Keep Claude, Cursor, Gemini, and related CLI integrations aligned with a repeatable dry-run and apply workflow.

productivity
⭐1
# Cross-CLI MCP Config Sync Imported from curated first-party documentation sources. ## What this covers Use this skill when multiple AI clients need the same MCP configuration without drifting out of sync. ## Use this when - Rolling out a shared MCP config to multiple clients - Previewing config changes before applying them - Standardizing developer setup across tools ## Expected outcomes - Cross-client MCP setup becomes easier to repeat - Dry-run and apply modes reduce accidental changes - Environment-specific details stay documented near the workflow ## Source synthesis - EVOKORE-MCP/docs/CLI_INTEGRATION.md (https://github.com/mattmre/EVOKORE-MCP/blob/main/docs/CLI_INTEGRATION.md) ## Dedupe notes Uses the dedicated CLI integration guide as the canonical source for config sync instead of duplicating setup notes elsewhere. ## Source excerpts ### EVOKORE-MCP/docs/CLI_INTEGRATION.md EVOKORE-MCP isn't just an MCP Server-Ò€—it also ships with natively integrated UI hooks designed to make your AI CLI experience (like Gemini CLI or Claude Code) significantly more powerful and transparent. ## 🍨 The Interactive Status Line When you connect EVOKORE-MCP to your AI Assistant, you can optionally enable the **EVOKORE Status Line**. Every time the AI finishes a thought or a tool execution, this hook intercepts the internal JSON payload and renders a beautiful, color-coded ASCII status bar at the bottom of your terminal showing: - **Location**: Your current working directory. - **Model Identity**: The exact LLM model currently loaded. - **Skill Count**: A live count of the MCP Agent Skills currently indexed in your library. - **Context Window Health**: A dynamic, color-coded progress bar showing exactly how many tokens you have consumed. --- ### 💜 Enabling in Gemini CLI Gemini CLI features a robust native hook engine. You can configure it to execute the EVOKORE Status Line immediately after every model response (`AfterModel`). **Step 1:** Locate your global settings file (`~/.gemini/settings.json`). **Step 2:** Ensure hooks are enabled, and add the `AfterModel` event array to the root of the JSON object: ```json { "enableHooks": true, "hooks": { "AfterModel": [ { "type": "command", "command": "node /absolute/path/to/EVOKORE-MCP/scripts/status.js" } ] } } ``` **Step 3:** Restart your Gemini CLI! --- ### 💜 Enabling in Claude Code Claude Code features an undocumented internal hook architecture that natively supports this status line. *(Note: Because this feature is currently undocumented by Anthropic, Claude Code's `doctor` command will display "Found 1 settings issue". This is perfectly normal and the status line will still execute successfully)*. **Step 1:** Locate your Claude settings file (`~/.claude/settings.json`). **Step 2:** Add the `statusLine` block to the root of the JSON object: ```json { "statusLine": { "type": "command", "command": "node /absolute/path/to/EVOKORE-MCP/scripts/status.js" } } ``` **Step 3:** Restart Claude Code. --- ### Òő ï¸ A Note on GitHub Copilot and Codex Microsoft's GitHub Copilot CLI and OpenAI's Codex CLI **do not natively support** these JSON hook configurations. If you want the EVOKORE Status Line to appear after commands in these tools, you must configure a native PowerShell/Bash alias wrapper around the CLI execution. **Example (PowerShell Profile):** ```powershell function copilot-evokore { gh copilot $args node "/absolute/path/to/EV ...
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docs
πŸ€–system promptβ€’6 months ago

Human-in-the-Loop Approval Token Workflow

Gate risky tool execution behind approval tokens so agents can retry safely after a human reviewer signs off.

security
⭐1
# Human-in-the-Loop Approval Token Workflow Imported from curated first-party documentation sources. ## What this covers Use this workflow when autonomous execution needs a durable handoff from human review back into the agent loop. ## Use this when - Retrying a blocked tool call after approval - Adding a human checkpoint to sensitive actions - Reducing insecure workarounds around approval flows ## Expected outcomes - Approval becomes a reusable tokenized workflow - Agents resume work without losing execution context - Security controls stay explicit instead of being implied ## Source synthesis - EVOKORE-MCP/docs/V2_MULTI_AGENT_WORKFLOWS.md (https://github.com/mattmre/EVOKORE-MCP/blob/main/docs/V2_MULTI_AGENT_WORKFLOWS.md) - EVOKORE-MCP/docs/AGENT33_IMPROVEMENT_INSTRUCTIONS.md (https://github.com/mattmre/EVOKORE-MCP/blob/main/docs/AGENT33_IMPROVEMENT_INSTRUCTIONS.md) ## Dedupe notes Synthesizes the approval-token concept from the main workflow doc and the Agent33 improvement transfer notes. ## Source excerpts ### EVOKORE-MCP/docs/V2_MULTI_AGENT_WORKFLOWS.md With EVOKORE-MCP v2.0 fully operational, we can now leverage 40+ proxied GitHub and Filesystem tools seamlessly within complex multi-agent workflows. The core architectural advancements include: ## 1. Dynamic Tool Prefixing & Indexing Instead of loading 40+ tools into an LLM's context window statically (which causes massive bloat), EVOKORE's `ProxyManager` boots child servers (like `@modelcontextprotocol/server-github` and `@modelcontextprotocol/server-filesystem`) and dynamically prefixes their tools (`github_create_issue`, `fs_write_file`). This prevents namespace collisions while keeping the tools accessible to native skills. ## 2. Human-in-the-Loop (HITL) Security Interceptor Automated multi-agent workflows involving sensitive endpoints (like GitHub write access or file deletion) are governed by EVOKORE's stateless `_evokore_approval_token` architecture. - When an agent attempts a restricted action, the tool call is intercepted and blocked. - The server returns an error explicitly commanding the agent to prompt the human for approval. - Upon approval, the agent retries the exact tool call with the injected token, securely fulfilling the workflow without severing the conversational context. ## 3. Active Skill Orchestration (Native Harnessing) Unlike v1.0 where skills merel ... ### EVOKORE-MCP/docs/AGENT33_IMPROVEMENT_INSTRUCTIONS.md > **Purpose**: Feed this file into Claude Code CLI when working on the Agent33 repo. It contains patterns, architectures, and capabilities proven in EVOKORE-MCP that Agent33 should adopt. --- ## 1. Multi-Server MCP Aggregation Pattern **What Agent33 lacks**: Agent33's MCP server (Phase 43) is a single-endpoint bridge. It doesn't aggregate multiple child MCP servers behind a unified namespace. **What to build**: A proxy layer that spawns and manages multiple child MCP servers from a single config file, presenting them as one unified tool surface. ### Implementation spec: ``` mcp.config.json { "servers": { "github": { "command": "npx", "args": ["-y", "@modelcontextprotocol/server-github"], "env": { "GITHUB_TOKEN": "${GITHUB_TOKEN}" } }, "fs": { "command": "npx", "args": ["-y", "@modelcontextprotocol/server-filesystem", "./"] }, "elevenlabs": { "command": "uvx", "args": ["elevenlabs-mcp"], "env": { "ELEVENLABS_API_KEY": "${ELEVENLABS_API_KEY}" } } } } ``` **Key patterns from EVOKORE**: - **Tool name prefixing**: Every proxied tool gets renamed `{serverId}_{originalName}` to prevent namespace collisions (e.g., `github_create_issue`, `fs_read_file`). First-registration-wins for duplicates. - **Environment interpolation**: `${VAR}` syntax in `env` blocks resolved ...
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docs
πŸ€–system promptβ€’7 months ago

github-actions-templates

Create production-ready GitHub Actions workflows for automated

coding
⭐1
# GitHub Actions Templates Production-ready GitHub Actions workflow patterns for testing, building, and deploying applications. ## Purpose Create efficient, secure GitHub Actions workflows for continuous integration and deployment across various tech stacks. ## When to Use - Automate testing and deployment - Build Docker images and push to registries - Deploy to Kubernetes clusters - Run security scans - Implement matrix builds for multiple environments ## Common Workflow Patterns ### Pattern 1: Test Workflow ```yaml name: Test on: push: branches: [main, develop] pull_request: branches: [main] jobs: test: runs-on: ubuntu-latest strategy: matrix: node-version: [18.x, 20.x] steps: - uses: actions/checkout@v4 - name: Use Node.js ${{ matrix.node-version }} uses: actions/setup-node@v4 with: node-version: ${{ matrix.node-version }} cache: "npm" - name: Install dependencies run: npm ci - name: Run linter run: npm run lint - name: Run tests run: npm test - name: Upload coverage uses: codecov/codecov-action@v3 with: files: ./coverage/lcov.info ``` **Reference:** See `assets/test-workflow.yml` ### Pattern 2: Build and Push Docker Image ```yaml name: Build and Push on: push: branches: [main] tags: ["v*"] env: REGISTRY: ghcr.io IMAGE_NAME: ${{ github.repository }} jobs: build: runs-on: ubuntu-latest permissions: contents: read packages: write steps: - uses: actions/checkout@v4 - name: Log in to Container Registry uses: docker/login-action@v3 with: registry: ${{ env.REGISTRY }} username: ${{ github.actor }} password: ${{ secrets.GITHUB_TOKEN }} - name: Extract metadata id: meta uses: docker/metadata-action@v5 with: images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }} tags: | type=ref,event=branch type=ref,event=pr type=semver,pattern={{version}} type=semver,pattern={{major}}.{{minor}} - name: Build and push uses: docker/build-push-action@v5 with: context: . push: true tags: ${{ steps.meta.outputs.tags }} labels: ${{ steps.meta.outputs.labels }} cache-from: type=gha cache-to: type=gha,mode=max ``` **Reference:** See `assets/deploy-workflow.yml` ### Pattern 3: Deploy to Kubernetes ```yaml name: Deploy to Kubernetes on: push: branches: [main] jobs: deploy: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 - name: Configure AWS credentials uses: aws-actions/configure-aws-credentials@v4 with: aws-access-key-id: ${{ secrets.AWS_ACCESS_KEY_ID }} aws-secret-access-key: ${{ secrets.AWS_SECRET_ACCESS_KEY }} aws-region: us-west-2 - name: Update kubeconfig run: | aws eks update-kubeconfig --name production-cluster --region us-west-2 - name: Deploy to Kubernetes run: | kubectl apply -f k8s/ kubectl rollout status deployment/my-app -n production kubectl get services -n production - name: Verify deployment run: | kubectl get pods -n production kubectl describe deployment my-app -n production ``` ### Pattern 4: Matrix Build ```yaml name: Matrix Build on: [push, pull_request] jobs: build: runs-on: ${{ matrix.os }} strategy: matrix: os: [ubuntu-latest, macos-latest, windows-latest] python-version: ["3.9", "3.10", "3.11", "3.12"] steps: - uses: actions/checkout@v4 - name: Set up Python uses: actions/setup-python@v5 with: python-version: ${{ matrix.python-version }} - name: Install dependencies run: | python -m pip install --upgrade pip pip install -r requirements.txt - name: Run tests run: pytest ``` **Reference:** See `assets/matrix-build.yml` ## Workflow Best Practices 1. **Use specific action versions** (@v4, not @latest) 2. **Cache dependencies** to speed up builds 3. **Use secrets** for sensitive data 4. **Implement status checks** on PRs 5. **Use matrix builds** for multi-version testing 6. **Set appropriate permissions** 7. **Use reusable workflows** for common patterns 8. **Implement approval gates** for production 9. **Add notification steps** for failures 10. **Use self-hosted runners** for sensitive workloads ## Reusable Workflows ```yaml # .github/workflows/reusable-test.yml name: Reusable Test Workflow on: workflow_call: inputs: node-version: required: true type: string secrets: NPM_TOKEN: required: true jobs: test: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 - uses: actions/setup-node@v4 with: node-version: ${{ inputs.node-version }} - run: npm ci - run: npm test ``` **Use reusable workflow:** ```yaml jobs: call-test: uses: ./.github/workflows/reusable-test.yml with: node-version: "20.x" secrets: NPM_TOKEN: ${{ secrets.NPM_TOKEN }} ``` ## Security Scanning ```yaml name: Security Scan on: push: branches: [main] pull_request: branches: [main] jobs: security: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 - name: Run Trivy vulnerability scanner uses: aquasecurity/trivy-action@master with: scan-type: "fs" scan-ref: "." format: "sarif" output: "trivy-results.sarif" - name: Upload Trivy results to GitHub Security uses: github/codeql-action/upload-sarif@v2 with: sarif_file: "trivy-results.sarif" - name: Run Snyk Security Scan uses: snyk/actions/node@master env: SNYK_TOKEN: ${{ secrets.SNYK_TOKEN }} ``` ## Deployment with Approvals ```yaml name: Deploy to Production on: push: tags: ["v*"] jobs: deploy: runs-on: ubuntu-latest environment: name: production url: https://app.example.com steps: - uses: actions/checkout@v4 - name: Deploy application run: | echo "Deploying to production..." # Deployment commands here - name: Notify Slack if: success() uses: slackapi/slack-github-action@v1 with: webhook-url: ${{ secrets.SLACK_WEBHOOK }} payload: | { "text": "Deployment to production completed successfully!" } ``` ## Reference Files - `assets/test-workflow.yml` - Testing workflow template - `assets/deploy-workflow.yml` - Deployment workflow template - `assets/matrix-build.yml` - Matrix build template - `references/common-workflows.md` - Common workflow patterns ## Related Skills - `gitlab-ci-patterns` - For GitLab CI workflows - `deployment-pipeline-design` - For pipeline architecture - `secrets-management` - For secrets handling
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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 ``` β”œβ”€β”€ Executive Summary (1 page) β”‚ β”œβ”€β”€ 4-6 headline KPIs β”‚ β”œβ”€β”€ Trend indicators β”‚ └── Key alerts β”œβ”€β”€ Department Views β”‚ β”œβ”€β”€ Sales Dashboard β”‚ β”œβ”€β”€ Marketing Dashboard β”‚ β”œβ”€β”€ Operations Dashboard β”‚ └── Finance Dashboard └── Detailed Drilldowns β”œβ”€β”€ 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 ``` β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”‚ EXECUTIVE DASHBOARD [Date Range β–Ό] β”‚ β”œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€ β”‚ REVENUE β”‚ PROFIT β”‚ CUSTOMERS β”‚ NPS SCORE β”‚ β”‚ $2.4M β”‚ $450K β”‚ 12,450 β”‚ 72 β”‚ β”‚ β–² 12% β”‚ β–² 8% β”‚ β–² 15% β”‚ β–² 5pts β”‚ β”œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€ β”‚ β”‚ β”‚ Revenue Trend β”‚ Revenue by Product β”‚ β”‚ β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”‚ β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”‚ β”‚ β”‚ /\ /\ β”‚ β”‚ β”‚ β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ 45% β”‚ β”‚ β”‚ β”‚ / \ / \ /\ β”‚ β”‚ β”‚ β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ 32% β”‚ β”‚ β”‚ β”‚ / \/ \ / \ β”‚ β”‚ β”‚ β–ˆβ–ˆβ–ˆβ–ˆ 18% β”‚ β”‚ β”‚ β”‚ / \/ \ β”‚ β”‚ β”‚ β–ˆβ–ˆ 5% β”‚ β”‚ β”‚ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ β”‚ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ β”‚ β”‚ β”‚ β”œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€ β”‚ πŸ”΄ Alert: Churn rate exceeded threshold (>5%) β”‚ β”‚ 🟑 Warning: Support ticket volume 20% above average β”‚ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ ``` ### Pattern 2: SaaS Metrics Dashboard ``` β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”‚ SAAS METRICS Jan 2024 [Monthly β–Ό] β”‚ β”œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€ β”‚ β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”‚ MRR GROWTH β”‚ β”‚ β”‚ MRR β”‚ β”‚ β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”‚ β”‚ β”‚ $125,000 β”‚ β”‚ β”‚ /── β”‚ β”‚ β”‚ β”‚ β–² 8% β”‚ β”‚ β”‚ /────/ β”‚ β”‚ β”‚ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ β”‚ β”‚ /────/ β”‚ β”‚ β”‚ β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”‚ β”‚ /────/ β”‚ β”‚ β”‚ β”‚ ARR β”‚ β”‚ β”‚ /────/ β”‚ β”‚ β”‚ β”‚ $1,500,000 β”‚ β”‚ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ β”‚ β”‚ β”‚ β–² 15% β”‚ β”‚ J F M A M J J A S O N D β”‚ β”‚ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ β”‚ β”‚ β”œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€ β”‚ UNIT ECONOMICS β”‚ COHORT RETENTION β”‚ β”‚ β”‚ β”‚ β”‚ CAC: $450 β”‚ Month 1: β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ 100% β”‚ β”‚ LTV: $2,700 β”‚ Month 3: β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ 85% β”‚ β”‚ LTV/CAC: 6.0x β”‚ Month 6: β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ 80% β”‚ β”‚ β”‚ Month 12: β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ 72% β”‚ β”‚ Payback: 4 months β”‚ β”‚ β”œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€ β”‚ CHURN ANALYSIS β”‚ β”‚ β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”‚ β”‚ β”‚ Gross β”‚ Net β”‚ Logo β”‚ Expansion β”‚ β”‚ β”‚ β”‚ 4.2% β”‚ 1.8% β”‚ 3.1% β”‚ 2.4% β”‚ β”‚ β”‚ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ β”‚ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ ``` ### Pattern 3: Real-time Operations ``` β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”‚ OPERATIONS CENTER Live ● Last: 10:42:15 β”‚ β”œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€ β”‚ SYSTEM HEALTH β”‚ SERVICE STATUS β”‚ β”‚ β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”‚ β”‚ β”‚ β”‚ CPU MEM DISK β”‚ β”‚ ● API Gateway Healthy β”‚ β”‚ β”‚ 45% 72% 58% β”‚ β”‚ ● User Service Healthy β”‚ β”‚ β”‚ β–ˆβ–ˆβ–ˆ β–ˆβ–ˆβ–ˆβ–ˆ β–ˆβ–ˆβ–ˆ β”‚ β”‚ ● Payment Service Degraded β”‚ β”‚ β”‚ β–ˆβ–ˆβ–ˆ β–ˆβ–ˆβ–ˆβ–ˆ β–ˆβ–ˆβ–ˆ β”‚ β”‚ ● Database Healthy β”‚ β”‚ β”‚ β–ˆβ–ˆβ–ˆ β–ˆβ–ˆβ–ˆβ–ˆ β–ˆβ–ˆβ–ˆ β”‚ β”‚ ● Cache Healthy β”‚ β”‚ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ β”‚ β”‚ β”œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€ β”‚ REQUEST THROUGHPUT β”‚ ERROR RATE β”‚ β”‚ β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”‚ β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”‚ β”‚ β”‚ β–β–‚β–ƒβ–„β–…β–†β–‡β–ˆβ–‡β–†β–…β–„β–ƒβ–‚β–β–‚β–ƒβ–„β–… β”‚ β”‚ β”‚ ▁▁▁▁▁▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁ β”‚ β”‚ β”‚ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ β”‚ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ β”‚ β”‚ Current: 12,450 req/s β”‚ Current: 0.02% β”‚ β”‚ Peak: 18,200 req/s β”‚ Threshold: 1.0% β”‚ β”œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€ β”‚ RECENT ALERTS β”‚ β”‚ 10:40 🟑 High latency on payment-service (p99 > 500ms) β”‚ β”‚ 10:35 🟒 Resolved: Database connection pool recovered β”‚ β”‚ 10:22 πŸ”΄ Payment service circuit breaker tripped β”‚ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ ``` ## 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 = "β–²" if delta >= 0 else "β–Ό" 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"πŸ”΄ {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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πŸ‘οΈ0
πŸ€– Auto-discovered
πŸ€–system promptβ€’7 months ago

architecture-patterns

Implement proven backend architecture patterns including Clean

coding
⭐1
# Architecture Patterns Master proven backend architecture patterns including Clean Architecture, Hexagonal Architecture, and Domain-Driven Design to build maintainable, testable, and scalable systems. ## When to Use This Skill - Designing new backend systems from scratch - Refactoring monolithic applications for better maintainability - Establishing architecture standards for your team - Migrating from tightly coupled to loosely coupled architectures - Implementing domain-driven design principles - Creating testable and mockable codebases - Planning microservices decomposition ## Core Concepts ### 1. Clean Architecture (Uncle Bob) **Layers (dependency flows inward):** - **Entities**: Core business models - **Use Cases**: Application business rules - **Interface Adapters**: Controllers, presenters, gateways - **Frameworks & Drivers**: UI, database, external services **Key Principles:** - Dependencies point inward - Inner layers know nothing about outer layers - Business logic independent of frameworks - Testable without UI, database, or external services ### 2. Hexagonal Architecture (Ports and Adapters) **Components:** - **Domain Core**: Business logic - **Ports**: Interfaces defining interactions - **Adapters**: Implementations of ports (database, REST, message queue) **Benefits:** - Swap implementations easily (mock for testing) - Technology-agnostic core - Clear separation of concerns ### 3. Domain-Driven Design (DDD) **Strategic Patterns:** - **Bounded Contexts**: Separate models for different domains - **Context Mapping**: How contexts relate - **Ubiquitous Language**: Shared terminology **Tactical Patterns:** - **Entities**: Objects with identity - **Value Objects**: Immutable objects defined by attributes - **Aggregates**: Consistency boundaries - **Repositories**: Data access abstraction - **Domain Events**: Things that happened ## Clean Architecture Pattern ### Directory Structure ``` app/ β”œβ”€β”€ domain/ # Entities & business rules β”‚ β”œβ”€β”€ entities/ β”‚ β”‚ β”œβ”€β”€ user.py β”‚ β”‚ └── order.py β”‚ β”œβ”€β”€ value_objects/ β”‚ β”‚ β”œβ”€β”€ email.py β”‚ β”‚ └── money.py β”‚ └── interfaces/ # Abstract interfaces β”‚ β”œβ”€β”€ user_repository.py β”‚ └── payment_gateway.py β”œβ”€β”€ use_cases/ # Application business rules β”‚ β”œβ”€β”€ create_user.py β”‚ β”œβ”€β”€ process_order.py β”‚ └── send_notification.py β”œβ”€β”€ adapters/ # Interface implementations β”‚ β”œβ”€β”€ repositories/ β”‚ β”‚ β”œβ”€β”€ postgres_user_repository.py β”‚ β”‚ └── redis_cache_repository.py β”‚ β”œβ”€β”€ controllers/ β”‚ β”‚ └── user_controller.py β”‚ └── gateways/ β”‚ β”œβ”€β”€ stripe_payment_gateway.py β”‚ └── sendgrid_email_gateway.py └── infrastructure/ # Framework & external concerns β”œβ”€β”€ database.py β”œβ”€β”€ config.py └── logging.py ``` ### Implementation Example ```python # domain/entities/user.py from dataclasses import dataclass from datetime import datetime from typing import Optional @dataclass class User: """Core user entity - no framework dependencies.""" id: str email: str name: str created_at: datetime is_active: bool = True def deactivate(self): """Business rule: deactivating user.""" self.is_active = False def can_place_order(self) -> bool: """Business rule: active users can order.""" return self.is_active # domain/interfaces/user_repository.py from abc import ABC, abstractmethod from typing import Optional, List from domain.entities.user import User class IUserRepository(ABC): """Port: defines contract, no implementation.""" @abstractmethod async def find_by_id(self, user_id: str) -> Optional[User]: pass @abstractmethod async def find_by_email(self, email: str) -> Optional[User]: pass @abstractmethod async def save(self, user: User) -> User: pass @abstractmethod async def delete(self, user_id: str) -> bool: pass # use_cases/create_user.py from domain.entities.user import User from domain.interfaces.user_repository import IUserRepository from dataclasses import dataclass from datetime import datetime import uuid @dataclass class CreateUserRequest: email: str name: str @dataclass class CreateUserResponse: user: User success: bool error: Optional[str] = None class CreateUserUseCase: """Use case: orchestrates business logic.""" def __init__(self, user_repository: IUserRepository): self.user_repository = user_repository async def execute(self, request: CreateUserRequest) -> CreateUserResponse: # Business validation existing = await self.user_repository.find_by_email(request.email) if existing: return CreateUserResponse( user=None, success=False, error="Email already exists" ) # Create entity user = User( id=str(uuid.uuid4()), email=request.email, name=request.name, created_at=datetime.now(), is_active=True ) # Persist saved_user = await self.user_repository.save(user) return CreateUserResponse( user=saved_user, success=True ) # adapters/repositories/postgres_user_repository.py from domain.interfaces.user_repository import IUserRepository from domain.entities.user import User from typing import Optional import asyncpg class PostgresUserRepository(IUserRepository): """Adapter: PostgreSQL implementation.""" def __init__(self, pool: asyncpg.Pool): self.pool = pool async def find_by_id(self, user_id: str) -> Optional[User]: async with self.pool.acquire() as conn: row = await conn.fetchrow( "SELECT * FROM users WHERE id = $1", user_id ) return self._to_entity(row) if row else None async def find_by_email(self, email: str) -> Optional[User]: async with self.pool.acquire() as conn: row = await conn.fetchrow( "SELECT * FROM users WHERE email = $1", email ) return self._to_entity(row) if row else None async def save(self, user: User) -> User: async with self.pool.acquire() as conn: await conn.execute( """ INSERT INTO users (id, email, name, created_at, is_active) VALUES ($1, $2, $3, $4, $5) ON CONFLICT (id) DO UPDATE SET email = $2, name = $3, is_active = $5 """, user.id, user.email, user.name, user.created_at, user.is_active ) return user async def delete(self, user_id: str) -> bool: async with self.pool.acquire() as conn: result = await conn.execute( "DELETE FROM users WHERE id = $1", user_id ) return result == "DELETE 1" def _to_entity(self, row) -> User: """Map database row to entity.""" return User( id=row["id"], email=row["email"], name=row["name"], created_at=row["created_at"], is_active=row["is_active"] ) # adapters/controllers/user_controller.py from fastapi import APIRouter, Depends, HTTPException from use_cases.create_user import CreateUserUseCase, CreateUserRequest from pydantic import BaseModel router = APIRouter() class CreateUserDTO(BaseModel): email: str name: str @router.post("/users") async def create_user( dto: CreateUserDTO, use_case: CreateUserUseCase = Depends(get_create_user_use_case) ): """Controller: handles HTTP concerns only.""" request = CreateUserRequest(email=dto.email, name=dto.name) response = await use_case.execute(request) if not response.success: raise HTTPException(status_code=400, detail=response.error) return {"user": response.user} ``` ## Hexagonal Architecture Pattern ```python # Core domain (hexagon center) class OrderService: """Domain service - no infrastructure dependencies.""" def __init__( self, order_repository: OrderRepositoryPort, payment_gateway: PaymentGatewayPort, notification_service: NotificationPort ): self.orders = order_repository self.payments = payment_gateway self.notifications = notification_service async def place_order(self, order: Order) -> OrderResult: # Business logic if not order.is_valid(): return OrderResult(success=False, error="Invalid order") # Use ports (interfaces) payment = await self.payments.charge( amount=order.total, customer=order.customer_id ) if not payment.success: return OrderResult(success=False, error="Payment failed") order.mark_as_paid() saved_order = await self.orders.save(order) await self.notifications.send( to=order.customer_email, subject="Order confirmed", body=f"Order {order.id} confirmed" ) return OrderResult(success=True, order=saved_order) # Ports (interfaces) class OrderRepositoryPort(ABC): @abstractmethod async def save(self, order: Order) -> Order: pass class PaymentGatewayPort(ABC): @abstractmethod async def charge(self, amount: Money, customer: str) -> PaymentResult: pass class NotificationPort(ABC): @abstractmethod async def send(self, to: str, subject: str, body: str): pass # Adapters (implementations) class StripePaymentAdapter(PaymentGatewayPort): """Primary adapter: connects to Stripe API.""" def __init__(self, api_key: str): self.stripe = stripe self.stripe.api_key = api_key async def charge(self, amount: Money, customer: str) -> PaymentResult: try: charge = self.stripe.Charge.create( amount=amount.cents, currency=amount.currency, customer=customer ) return PaymentResult(success=True, transaction_id=charge.id) except stripe.error.CardError as e: return PaymentResult(success=False, error=str(e)) class MockPaymentAdapter(PaymentGatewayPort): """Test adapter: no external dependencies.""" async def charge(self, amount: Money, customer: str) -> PaymentResult: return PaymentResult(success=True, transaction_id="mock-123") ``` ## Domain-Driven Design Pattern ```python # Value Objects (immutable) from dataclasses import dataclass from typing import Optional @dataclass(frozen=True) class Email: """Value object: validated email.""" value: str def __post_init__(self): if "@" not in self.value: raise ValueError("Invalid email") @dataclass(frozen=True) class Money: """Value object: amount with currency.""" amount: int # cents currency: str def add(self, other: "Money") -> "Money": if self.currency != other.currency: raise ValueError("Currency mismatch") return Money(self.amount + other.amount, self.currency) # Entities (with identity) class Order: """Entity: has identity, mutable state.""" def __init__(self, id: str, customer: Customer): self.id = id self.customer = customer self.items: List[OrderItem] = [] self.status = OrderStatus.PENDING self._events: List[DomainEvent] = [] def add_item(self, product: Product, quantity: int): """Business logic in entity.""" item = OrderItem(product, quantity) self.items.append(item) self._events.append(ItemAddedEvent(self.id, item)) def total(self) -> Money: """Calculated property.""" return sum(item.subtotal() for item in self.items) def submit(self): """State transition with business rules.""" if not self.items: raise ValueError("Cannot submit empty order") if self.status != OrderStatus.PENDING: raise ValueError("Order already submitted") self.status = OrderStatus.SUBMITTED self._events.append(OrderSubmittedEvent(self.id)) # Aggregates (consistency boundary) class Customer: """Aggregate root: controls access to entities.""" def __init__(self, id: str, email: Email): self.id = id self.email = email self._addresses: List[Address] = [] self._orders: List[str] = [] # Order IDs, not full objects def add_address(self, address: Address): """Aggregate enforces invariants.""" if len(self._addresses) >= 5: raise ValueError("Maximum 5 addresses allowed") self._addresses.append(address) @property def primary_address(self) -> Optional[Address]: return next((a for a in self._addresses if a.is_primary), None) # Domain Events @dataclass class OrderSubmittedEvent: order_id: str occurred_at: datetime = field(default_factory=datetime.now) # Repository (aggregate persistence) class OrderRepository: """Repository: persist/retrieve aggregates.""" async def find_by_id(self, order_id: str) -> Optional[Order]: """Reconstitute aggregate from storage.""" pass async def save(self, order: Order): """Persist aggregate and publish events.""" await self._persist(order) await self._publish_events(order._events) order._events.clear() ``` ## Resources - **references/clean-architecture-guide.md**: Detailed layer breakdown - **references/hexagonal-architecture-guide.md**: Ports and adapters patterns - **references/ddd-tactical-patterns.md**: Entities, value objects, aggregates - **assets/clean-architecture-template/**: Complete project structure - **assets/ddd-examples/**: Domain modeling examples ## Best Practices 1. **Dependency Rule**: Dependencies always point inward 2. **Interface Segregation**: Small, focused interfaces 3. **Business Logic in Domain**: Keep frameworks out of core 4. **Test Independence**: Core testable without infrastructure 5. **Bounded Contexts**: Clear domain boundaries 6. **Ubiquitous Language**: Consistent terminology 7. **Thin Controllers**: Delegate to use cases 8. **Rich Domain Models**: Behavior with data ## Common Pitfalls - **Anemic Domain**: Entities with only data, no behavior - **Framework Coupling**: Business logic depends on frameworks - **Fat Controllers**: Business logic in controllers - **Repository Leakage**: Exposing ORM objects - **Missing Abstractions**: Concrete dependencies in core - **Over-Engineering**: Clean architecture for simple CRUD
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πŸ‘οΈ0
πŸ€– Auto-discovered
πŸ€–system promptβ€’7 months ago

track-management

Use this skill when creating, managing, or working with Conductor

coding
⭐1
# Track Management Guide for creating, managing, and completing Conductor tracks - the logical work units that organize features, bugs, and refactors through specification, planning, and implementation phases. ## When to Use This Skill - Creating new feature, bug, or refactor tracks - Writing or reviewing spec.md files - Creating or updating plan.md files - Managing track lifecycle from creation to completion - Understanding track status markers and conventions - Working with the tracks.md registry - Interpreting or updating track metadata ## Track Concept A track is a logical work unit that encapsulates a complete piece of work. Each track has: - A unique identifier - A specification defining requirements - A phased plan breaking work into tasks - Metadata tracking status and progress Tracks provide semantic organization for work, enabling: - Clear scope boundaries - Progress tracking - Git-aware operations (revert by track) - Team coordination ## Track Types ### feature New functionality or capabilities. Use for: - New user-facing features - New API endpoints - New integrations - Significant enhancements ### bug Defect fixes. Use for: - Incorrect behavior - Error conditions - Performance regressions - Security vulnerabilities ### chore Maintenance and housekeeping. Use for: - Dependency updates - Configuration changes - Documentation updates - Cleanup tasks ### refactor Code improvement without behavior change. Use for: - Code restructuring - Pattern adoption - Technical debt reduction - Performance optimization (same behavior, better performance) ## Track ID Format Track IDs follow the pattern: `{shortname}_{YYYYMMDD}` - **shortname**: 2-4 word kebab-case description (e.g., `user-auth`, `api-rate-limit`) - **YYYYMMDD**: Creation date in ISO format Examples: - `user-auth_20250115` - `fix-login-error_20250115` - `upgrade-deps_20250115` - `refactor-api-client_20250115` ## Track Lifecycle ### 1. Creation (newTrack) **Define Requirements** 1. Gather requirements through interactive Q&A 2. Identify acceptance criteria 3. Determine scope boundaries 4. Identify dependencies **Generate Specification** 1. Create `spec.md` with structured requirements 2. Document functional and non-functional requirements 3. Define acceptance criteria 4. List dependencies and constraints **Generate Plan** 1. Create `plan.md` with phased task breakdown 2. Organize tasks into logical phases 3. Add verification tasks after phases 4. Estimate effort and complexity **Register Track** 1. Add entry to `tracks.md` registry 2. Create track directory structure 3. Generate `metadata.json` 4. Create track `index.md` ### 2. Implementation **Execute Tasks** 1. Select next pending task from plan 2. Mark task as in-progress 3. Implement following workflow (TDD) 4. Mark task complete with commit SHA **Update Status** 1. Update task markers in plan.md 2. Record commit SHAs for traceability 3. Update phase progress 4. Update track status in tracks.md **Verify Progress** 1. Complete verification tasks 2. Wait for checkpoint approval 3. Record checkpoint commits ### 3. Completion **Sync Documentation** 1. Update product.md if features added 2. Update tech-stack.md if dependencies changed 3. Verify all acceptance criteria met **Archive or Delete** 1. Mark track as completed in tracks.md 2. Record completion date 3. Archive or retain track directory ## Specification (spec.md) Structure ```markdown # {Track Title} ## Overview Brief description of what this track accomplishes and why. ## Functional Requirements ### FR-1: {Requirement Name} Description of the functional requirement. - Acceptance: How to verify this requirement is met ### FR-2: {Requirement Name} ... ## Non-Functional Requirements ### NFR-1: {Requirement Name} Description of the non-functional requirement (performance, security, etc.) - Target: Specific measurable target - Verification: How to test ## Acceptance Criteria - [ ] Criterion 1: Specific, testable condition - [ ] Criterion 2: Specific, testable condition - [ ] Criterion 3: Specific, testable condition ## Scope ### In Scope - Explicitly included items - Features to implement - Components to modify ### Out of Scope - Explicitly excluded items - Future considerations - Related but separate work ## Dependencies ### Internal - Other tracks or components this depends on - Required context artifacts ### External - Third-party services or APIs - External dependencies ## Risks and Mitigations | Risk | Impact | Mitigation | | ---------------- | --------------- | ------------------- | | Risk description | High/Medium/Low | Mitigation strategy | ## Open Questions - [ ] Question that needs resolution - [x] Resolved question - Answer ``` ## Plan (plan.md) Structure ```markdown # Implementation Plan: {Track Title} Track ID: `{track-id}` Created: YYYY-MM-DD Status: pending | in-progress | completed ## Overview Brief description of implementation approach. ## Phase 1: {Phase Name} ### Tasks - [ ] **Task 1.1**: Task description - Sub-task or detail - Sub-task or detail - [ ] **Task 1.2**: Task description - [ ] **Task 1.3**: Task description ### Verification - [ ] **Verify 1.1**: Verification step for phase ## Phase 2: {Phase Name} ### Tasks - [ ] **Task 2.1**: Task description - [ ] **Task 2.2**: Task description ### Verification - [ ] **Verify 2.1**: Verification step for phase ## Phase 3: Finalization ### Tasks - [ ] **Task 3.1**: Update documentation - [ ] **Task 3.2**: Final integration test ### Verification - [ ] **Verify 3.1**: All acceptance criteria met ## Checkpoints | Phase | Checkpoint SHA | Date | Status | | ------- | -------------- | ---- | ------- | | Phase 1 | | | pending | | Phase 2 | | | pending | | Phase 3 | | | pending | ``` ## Status Marker Conventions Use consistent markers in plan.md: | Marker | Meaning | Usage | | ------ | ----------- | --------------------------- | | `[ ]` | Pending | Task not started | | `[~]` | In Progress | Currently being worked | | `[x]` | Complete | Task finished (include SHA) | | `[-]` | Skipped | Intentionally not done | | `[!]` | Blocked | Waiting on dependency | Example: ```markdown - [x] **Task 1.1**: Set up database schema `abc1234` - [~] **Task 1.2**: Implement user model - [ ] **Task 1.3**: Add validation logic - [!] **Task 1.4**: Integrate auth service (blocked: waiting for API key) - [-] **Task 1.5**: Legacy migration (skipped: not needed) ``` ## Track Registry (tracks.md) Format ```markdown # Track Registry ## Active Tracks | Track ID | Type | Status | Phase | Started | Assignee | | ------------------------------------------------ | ------- | ----------- | ----- | ---------- | ---------- | | [user-auth_20250115](tracks/user-auth_20250115/) | feature | in-progress | 2/3 | 2025-01-15 | @developer | | [fix-login_20250114](tracks/fix-login_20250114/) | bug | pending | 0/2 | 2025-01-14 | - | ## Completed Tracks | Track ID | Type | Completed | Duration | | ---------------------------------------------- | ----- | ---------- | -------- | | [setup-ci_20250110](tracks/setup-ci_20250110/) | chore | 2025-01-12 | 2 days | ## Archived Tracks | Track ID | Reason | Archived | | ---------------------------------------------------- | ---------- | ---------- | | [old-feature_20241201](tracks/old-feature_20241201/) | Superseded | 2025-01-05 | ``` ## Metadata (metadata.json) Fields ```json { "id": "user-auth_20250115", "title": "User Authentication System", "type": "feature", "status": "in-progress", "priority": "high", "created": "2025-01-15T10:30:00Z", "updated": "2025-01-15T14:45:00Z", "started": "2025-01-15T11:00:00Z", "completed": null, "assignee": "@developer", "phases": { "total": 3, "current": 2, "completed": 1 }, "tasks": { "total": 12, "completed": 5, "in_progress": 1, "pending": 6 }, "checkpoints": [ { "phase": 1, "sha": "abc1234", "date": "2025-01-15T13:00:00Z" } ], "dependencies": [], "tags": ["auth", "security"] } ``` ## Track Operations ### Creating a Track 1. Run `/conductor:new-track` 2. Answer interactive questions 3. Review generated spec.md 4. Review generated plan.md 5. Confirm track creation ### Starting Implementation 1. Read spec.md and plan.md 2. Verify context artifacts are current 3. Mark first task as `[~]` 4. Begin TDD workflow ### Completing a Phase 1. Ensure all phase tasks are `[x]` 2. Complete verification tasks 3. Wait for checkpoint approval 4. Record checkpoint SHA 5. Proceed to next phase ### Completing a Track 1. Verify all phases complete 2. Verify all acceptance criteria met 3. Update product.md if needed 4. Mark track completed in tracks.md 5. Update metadata.json ### Reverting a Track 1. Run `/conductor:revert` 2. Select track to revert 3. Choose granularity (track/phase/task) 4. Confirm revert operation 5. Update status markers ## Handling Track Dependencies ### Identifying Dependencies During track creation, identify: - **Hard dependencies**: Must complete before this track can start - **Soft dependencies**: Can proceed in parallel but may affect integration - **External dependencies**: Third-party services, APIs, or team decisions ### Documenting Dependencies In spec.md, list dependencies with: - Dependency type (hard/soft/external) - Current status (available/pending/blocked) - Resolution path (what needs to happen) ### Managing Blocked Tracks When a track is blocked: 1. Mark blocked tasks with `[!]` and reason 2. Update tracks.md status 3. Document blocker in metadata.json 4. Consider creating dependency track if needed ## Track Sizing Guidelines ### Right-Sized Tracks Aim for tracks that: - Complete in 1-5 days of work - Have 2-4 phases - Contain 8-20 tasks total - Deliver a coherent, testable unit ### Too Large Signs a track is too large: - More than 5 phases - More than 25 tasks - Multiple unrelated features - Estimated duration > 1 week Solution: Split into multiple tracks with clear boundaries. ### Too Small Signs a track is too small: - Single phase with 1-2 tasks - No meaningful verification needed - Could be a sub-task of another track - Less than a few hours of work Solution: Combine with related work or handle as part of existing track. ## Specification Quality Checklist Before finalizing spec.md, verify: ### Requirements Quality - [ ] Each requirement has clear acceptance criteria - [ ] Requirements are testable - [ ] Requirements are independent (can verify separately) - [ ] No ambiguous language ("should be fast" β†’ "response < 200ms") ### Scope Clarity - [ ] In-scope items are specific - [ ] Out-of-scope items prevent scope creep - [ ] Boundaries are clear to implementer ### Dependencies Identified - [ ] All internal dependencies listed - [ ] External dependencies have owners/contacts - [ ] Dependency status is current ### Risks Addressed - [ ] Major risks identified - [ ] Impact assessment realistic - [ ] Mitigations are actionable ## Plan Quality Checklist Before starting implementation, verify plan.md: ### Task Quality - [ ] Tasks are atomic (one logical action) - [ ] Tasks are independently verifiable - [ ] Task descriptions are clear - [ ] Sub-tasks provide helpful detail ### Phase Organization - [ ] Phases group related tasks - [ ] Each phase delivers something testable - [ ] Verification tasks after each phase - [ ] Phases build on each other logically ### Completeness - [ ] All spec requirements have corresponding tasks - [ ] Documentation tasks included - [ ] Testing tasks included - [ ] Integration tasks included ## Common Track Patterns ### Feature Track Pattern ``` Phase 1: Foundation - Data models - Database migrations - Basic API structure Phase 2: Core Logic - Business logic implementation - Input validation - Error handling Phase 3: Integration - UI integration - API documentation - End-to-end tests ``` ### Bug Fix Track Pattern ``` Phase 1: Reproduction - Write failing test capturing bug - Document reproduction steps Phase 2: Fix - Implement fix - Verify test passes - Check for regressions Phase 3: Verification - Manual verification - Update documentation if needed ``` ### Refactor Track Pattern ``` Phase 1: Preparation - Add characterization tests - Document current behavior Phase 2: Refactoring - Apply changes incrementally - Maintain green tests throughout Phase 3: Cleanup - Remove dead code - Update documentation ``` ## Best Practices 1. **One track, one concern**: Keep tracks focused on a single logical change 2. **Small phases**: Break work into phases of 3-5 tasks maximum 3. **Verification after phases**: Always include verification tasks 4. **Update markers immediately**: Mark task status as you work 5. **Record SHAs**: Always note commit SHAs for completed tasks 6. **Review specs before planning**: Ensure spec is complete before creating plan 7. **Link dependencies**: Explicitly note track dependencies 8. **Archive, don't delete**: Preserve completed tracks for reference 9. **Size appropriately**: Keep tracks between 1-5 days of work 10. **Clear acceptance criteria**: Every requirement must be testable
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πŸ€– Auto-discovered
πŸ€–system promptβ€’7 months ago

workflow-patterns

Use this skill when implementing tasks according to Conductor's TDD

coding
⭐1
# Workflow Patterns Guide for implementing tasks using Conductor's TDD workflow, managing phase checkpoints, handling git commits, and executing the verification protocol that ensures quality throughout implementation. ## When to Use This Skill - Implementing tasks from a track's plan.md - Following TDD red-green-refactor cycle - Completing phase checkpoints - Managing git commits and notes - Understanding quality assurance gates - Handling verification protocols - Recording progress in plan files ## TDD Task Lifecycle Follow these 11 steps for each task: ### Step 1: Select Next Task Read plan.md and identify the next pending `[ ]` task. Select tasks in order within the current phase. Do not skip ahead to later phases. ### Step 2: Mark as In Progress Update plan.md to mark the task as `[~]`: ```markdown - [~] **Task 2.1**: Implement user validation ``` Commit this status change separately from implementation. ### Step 3: RED - Write Failing Tests Write tests that define the expected behavior before writing implementation: - Create test file if needed - Write test cases covering happy path - Write test cases covering edge cases - Write test cases covering error conditions - Run tests - they should FAIL Example: ```python def test_validate_user_email_valid(): user = User(email="test@example.com") assert user.validate_email() is True def test_validate_user_email_invalid(): user = User(email="invalid") assert user.validate_email() is False ``` ### Step 4: GREEN - Implement Minimum Code Write the minimum code necessary to make tests pass: - Focus on making tests green, not perfection - Avoid premature optimization - Keep implementation simple - Run tests - they should PASS ### Step 5: REFACTOR - Improve Clarity With green tests, improve the code: - Extract common patterns - Improve naming - Remove duplication - Simplify logic - Run tests after each change - they should remain GREEN ### Step 6: Verify Coverage Check test coverage meets the 80% target: ```bash pytest --cov=module --cov-report=term-missing ``` If coverage is below 80%: - Identify uncovered lines - Add tests for missing paths - Re-run coverage check ### Step 7: Document Deviations If implementation deviated from plan or introduced new dependencies: - Update tech-stack.md with new dependencies - Note deviations in plan.md task comments - Update spec.md if requirements changed ### Step 8: Commit Implementation Create a focused commit for the task: ```bash git add -A git commit -m "feat(user): implement email validation - Add validate_email method to User class - Handle empty and malformed emails - Add comprehensive test coverage Task: 2.1 Track: user-auth_20250115" ``` Commit message format: - Type: feat, fix, refactor, test, docs, chore - Scope: affected module or component - Summary: imperative, present tense - Body: bullet points of changes - Footer: task and track references ### Step 9: Attach Git Notes Add rich task summary as git note: ```bash git notes add -m "Task 2.1: Implement user validation Summary: - Added email validation using regex pattern - Handles edge cases: empty, no @, no domain - Coverage: 94% on validation module Files changed: - src/models/user.py (modified) - tests/test_user.py (modified) Decisions: - Used simple regex over email-validator library - Reason: No external dependency for basic validation" ``` ### Step 10: Update Plan with SHA Update plan.md to mark task complete with commit SHA: ```markdown - [x] **Task 2.1**: Implement user validation `abc1234` ``` ### Step 11: Commit Plan Update Commit the plan status update: ```bash git add conductor/tracks/*/plan.md git commit -m "docs: update plan - task 2.1 complete Track: user-auth_20250115" ``` ## Phase Completion Protocol When all tasks in a phase are complete, execute the verification protocol: ### Identify Changed Files List all files modified since the last checkpoint: ```bash git diff --name-only <last-checkpoint-sha>..HEAD ``` ### Ensure Test Coverage For each modified file: 1. Identify corresponding test file 2. Verify tests exist for new/changed code 3. Run coverage for modified modules 4. Add tests if coverage < 80% ### Run Full Test Suite Execute complete test suite: ```bash pytest -v --tb=short ``` All tests must pass before proceeding. ### Generate Manual Verification Steps Create checklist of manual verifications: ```markdown ## Phase 1 Verification Checklist - [ ] User can register with valid email - [ ] Invalid email shows appropriate error - [ ] Database stores user correctly - [ ] API returns expected response codes ``` ### WAIT for User Approval Present verification checklist to user: ``` Phase 1 complete. Please verify: 1. [ ] Test suite passes (automated) 2. [ ] Coverage meets target (automated) 3. [ ] Manual verification items (requires human) Respond with 'approved' to continue, or note issues. ``` Do NOT proceed without explicit approval. ### Create Checkpoint Commit After approval, create checkpoint commit: ```bash git add -A git commit -m "checkpoint: phase 1 complete - user-auth_20250115 Verified: - All tests passing - Coverage: 87% - Manual verification approved Phase 1 tasks: - [x] Task 1.1: Setup database schema - [x] Task 1.2: Implement user model - [x] Task 1.3: Add validation logic" ``` ### Record Checkpoint SHA Update plan.md checkpoints table: ```markdown ## Checkpoints | Phase | Checkpoint SHA | Date | Status | | ------- | -------------- | ---------- | -------- | | Phase 1 | def5678 | 2025-01-15 | verified | | Phase 2 | | | pending | ``` ## Quality Assurance Gates Before marking any task complete, verify these gates: ### Passing Tests - All existing tests pass - New tests pass - No test regressions ### Coverage >= 80% - New code has 80%+ coverage - Overall project coverage maintained - Critical paths fully covered ### Style Compliance - Code follows style guides - Linting passes - Formatting correct ### Documentation - Public APIs documented - Complex logic explained - README updated if needed ### Type Safety - Type hints present (if applicable) - Type checker passes - No type: ignore without reason ### No Linting Errors - Zero linter errors - Warnings addressed or justified - Static analysis clean ### Mobile Compatibility If applicable: - Responsive design verified - Touch interactions work - Performance acceptable ### Security Audit - No secrets in code - Input validation present - Authentication/authorization correct - Dependencies vulnerability-free ## Git Integration ### Commit Message Format ``` <type>(<scope>): <subject> <body> <footer> ``` Types: - `feat`: New feature - `fix`: Bug fix - `refactor`: Code change without feature/fix - `test`: Adding tests - `docs`: Documentation - `chore`: Maintenance ### Git Notes for Rich Summaries Attach detailed notes to commits: ```bash git notes add -m "<detailed summary>" ``` View notes: ```bash git log --show-notes ``` Benefits: - Preserves context without cluttering commit message - Enables semantic queries across commits - Supports track-based operations ### SHA Recording in plan.md Always record the commit SHA when completing tasks: ```markdown - [x] **Task 1.1**: Setup schema `abc1234` - [x] **Task 1.2**: Add model `def5678` ``` This enables: - Traceability from plan to code - Semantic revert operations - Progress auditing ## Verification Checkpoints ### Why Checkpoints Matter Checkpoints create restore points for semantic reversion: - Revert to end of any phase - Maintain logical code state - Enable safe experimentation ### When to Create Checkpoints Create checkpoint after: - All phase tasks complete - All phase verifications pass - User approval received ### Checkpoint Commit Content Include in checkpoint commit: - All uncommitted changes - Updated plan.md - Updated metadata.json - Any documentation updates ### How to Use Checkpoints For reverting: ```bash # Revert to end of Phase 1 git revert --no-commit <phase-2-commits>... git commit -m "revert: rollback to phase 1 checkpoint" ``` For review: ```bash # See what changed in Phase 2 git diff <phase-1-sha>..<phase-2-sha> ``` ## Handling Deviations During implementation, deviations from the plan may occur. Handle them systematically: ### Types of Deviations **Scope Addition** Discovered requirement not in original spec. - Document in spec.md as new requirement - Add tasks to plan.md - Note addition in task comments **Scope Reduction** Feature deemed unnecessary during implementation. - Mark tasks as `[-]` (skipped) with reason - Update spec.md scope section - Document decision rationale **Technical Deviation** Different implementation approach than planned. - Note deviation in task completion comment - Update tech-stack.md if dependencies changed - Document why original approach was unsuitable **Requirement Change** Understanding of requirement changes during work. - Update spec.md with corrected requirement - Adjust plan.md tasks if needed - Re-verify acceptance criteria ### Deviation Documentation Format When completing a task with deviation: ```markdown - [x] **Task 2.1**: Implement validation `abc1234` - DEVIATION: Used library instead of custom code - Reason: Better edge case handling - Impact: Added email-validator to dependencies ``` ## Error Recovery ### Failed Tests After GREEN If tests fail after reaching GREEN: 1. Do NOT proceed to REFACTOR 2. Identify which test started failing 3. Check if refactoring broke something 4. Revert to last known GREEN state 5. Re-approach the implementation ### Checkpoint Rejection If user rejects a checkpoint: 1. Note rejection reason in plan.md 2. Create tasks to address issues 3. Complete remediation tasks 4. Request checkpoint approval again ### Blocked by Dependency If task cannot proceed: 1. Mark task as `[!]` with blocker description 2. Check if other tasks can proceed 3. Document expected resolution timeline 4. Consider creating dependency resolution track ## TDD Variations by Task Type ### Data Model Tasks ``` RED: Write test for model creation and validation GREEN: Implement model class with fields REFACTOR: Add computed properties, improve types ``` ### API Endpoint Tasks ``` RED: Write test for request/response contract GREEN: Implement endpoint handler REFACTOR: Extract validation, improve error handling ``` ### Integration Tasks ``` RED: Write test for component interaction GREEN: Wire components together REFACTOR: Improve error propagation, add logging ``` ### Refactoring Tasks ``` RED: Add characterization tests for current behavior GREEN: Apply refactoring (tests should stay green) REFACTOR: Clean up any introduced complexity ``` ## Working with Existing Tests When modifying code with existing tests: ### Extend, Don't Replace - Keep existing tests passing - Add new tests for new behavior - Update tests only when requirements change ### Test Migration When refactoring changes test structure: 1. Run existing tests (should pass) 2. Add new tests for refactored code 3. Migrate test cases to new structure 4. Remove old tests only after new tests pass ### Regression Prevention After any change: 1. Run full test suite 2. Check for unexpected failures 3. Investigate any new failures 4. Fix regressions before proceeding ## Checkpoint Verification Details ### Automated Verification Run before requesting approval: ```bash # Test suite pytest -v --tb=short # Coverage pytest --cov=src --cov-report=term-missing # Linting ruff check src/ tests/ # Type checking (if applicable) mypy src/ ``` ### Manual Verification Guidance For manual items, provide specific instructions: ```markdown ## Manual Verification Steps ### User Registration 1. Navigate to /register 2. Enter valid email: test@example.com 3. Enter password meeting requirements 4. Click Submit 5. Verify success message appears 6. Verify user appears in database ### Error Handling 1. Enter invalid email: "notanemail" 2. Verify error message shows 3. Verify form retains other entered data ``` ## Performance Considerations ### Test Suite Performance Keep test suite fast: - Use fixtures to avoid redundant setup - Mock slow external calls - Run subset during development, full suite at checkpoints ### Commit Performance Keep commits atomic: - One logical change per commit - Complete thought, not work-in-progress - Tests should pass after every commit ## Best Practices 1. **Never skip RED**: Always write failing tests first 2. **Small commits**: One logical change per commit 3. **Immediate updates**: Update plan.md right after task completion 4. **Wait for approval**: Never skip checkpoint verification 5. **Rich git notes**: Include context that helps future understanding 6. **Coverage discipline**: Don't accept coverage below target 7. **Quality gates**: Check all gates before marking complete 8. **Sequential phases**: Complete phases in order 9. **Document deviations**: Note any changes from original plan 10. **Clean state**: Each commit should leave code in working state 11. **Fast feedback**: Run relevant tests frequently during development 12. **Clear blockers**: Address blockers promptly, don't work around them
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πŸ€– Auto-discovered
πŸ€–system promptβ€’7 months ago

airflow-dag-patterns

Build production Apache Airflow DAGs with best practices for

data
⭐1
# Apache Airflow DAG Patterns Production-ready patterns for Apache Airflow including DAG design, operators, sensors, testing, and deployment strategies. ## When to Use This Skill - Creating data pipeline orchestration with Airflow - Designing DAG structures and dependencies - Implementing custom operators and sensors - Testing Airflow DAGs locally - Setting up Airflow in production - Debugging failed DAG runs ## Core Concepts ### 1. DAG Design Principles | Principle | Description | | --------------- | ----------------------------------- | | **Idempotent** | Running twice produces same result | | **Atomic** | Tasks succeed or fail completely | | **Incremental** | Process only new/changed data | | **Observable** | Logs, metrics, alerts at every step | ### 2. Task Dependencies ```python # Linear task1 >> task2 >> task3 # Fan-out task1 >> [task2, task3, task4] # Fan-in [task1, task2, task3] >> task4 # Complex task1 >> task2 >> task4 task1 >> task3 >> task4 ``` ## Quick Start ```python # dags/example_dag.py from datetime import datetime, timedelta from airflow import DAG from airflow.operators.python import PythonOperator from airflow.operators.empty import EmptyOperator default_args = { 'owner': 'data-team', 'depends_on_past': False, 'email_on_failure': True, 'email_on_retry': False, 'retries': 3, 'retry_delay': timedelta(minutes=5), 'retry_exponential_backoff': True, 'max_retry_delay': timedelta(hours=1), } with DAG( dag_id='example_etl', default_args=default_args, description='Example ETL pipeline', schedule='0 6 * * *', # Daily at 6 AM start_date=datetime(2024, 1, 1), catchup=False, tags=['etl', 'example'], max_active_runs=1, ) as dag: start = EmptyOperator(task_id='start') def extract_data(**context): execution_date = context['ds'] # Extract logic here return {'records': 1000} extract = PythonOperator( task_id='extract', python_callable=extract_data, ) end = EmptyOperator(task_id='end') start >> extract >> end ``` ## Patterns ### Pattern 1: TaskFlow API (Airflow 2.0+) ```python # dags/taskflow_example.py from datetime import datetime from airflow.decorators import dag, task from airflow.models import Variable @dag( dag_id='taskflow_etl', schedule='@daily', start_date=datetime(2024, 1, 1), catchup=False, tags=['etl', 'taskflow'], ) def taskflow_etl(): """ETL pipeline using TaskFlow API""" @task() def extract(source: str) -> dict: """Extract data from source""" import pandas as pd df = pd.read_csv(f's3://bucket/{source}/{{ ds }}.csv') return {'data': df.to_dict(), 'rows': len(df)} @task() def transform(extracted: dict) -> dict: """Transform extracted data""" import pandas as pd df = pd.DataFrame(extracted['data']) df['processed_at'] = datetime.now() df = df.dropna() return {'data': df.to_dict(), 'rows': len(df)} @task() def load(transformed: dict, target: str): """Load data to target""" import pandas as pd df = pd.DataFrame(transformed['data']) df.to_parquet(f's3://bucket/{target}/{{ ds }}.parquet') return transformed['rows'] @task() def notify(rows_loaded: int): """Send notification""" print(f'Loaded {rows_loaded} rows') # Define dependencies with XCom passing extracted = extract(source='raw_data') transformed = transform(extracted) loaded = load(transformed, target='processed_data') notify(loaded) # Instantiate the DAG taskflow_etl() ``` ### Pattern 2: Dynamic DAG Generation ```python # dags/dynamic_dag_factory.py from datetime import datetime, timedelta from airflow import DAG from airflow.operators.python import PythonOperator from airflow.models import Variable import json # Configuration for multiple similar pipelines PIPELINE_CONFIGS = [ {'name': 'customers', 'schedule': '@daily', 'source': 's3://raw/customers'}, {'name': 'orders', 'schedule': '@hourly', 'source': 's3://raw/orders'}, {'name': 'products', 'schedule': '@weekly', 'source': 's3://raw/products'}, ] def create_dag(config: dict) -> DAG: """Factory function to create DAGs from config""" dag_id = f"etl_{config['name']}" default_args = { 'owner': 'data-team', 'retries': 3, 'retry_delay': timedelta(minutes=5), } dag = DAG( dag_id=dag_id, default_args=default_args, schedule=config['schedule'], start_date=datetime(2024, 1, 1), catchup=False, tags=['etl', 'dynamic', config['name']], ) with dag: def extract_fn(source, **context): print(f"Extracting from {source} for {context['ds']}") def transform_fn(**context): print(f"Transforming data for {context['ds']}") def load_fn(table_name, **context): print(f"Loading to {table_name} for {context['ds']}") extract = PythonOperator( task_id='extract', python_callable=extract_fn, op_kwargs={'source': config['source']}, ) transform = PythonOperator( task_id='transform', python_callable=transform_fn, ) load = PythonOperator( task_id='load', python_callable=load_fn, op_kwargs={'table_name': config['name']}, ) extract >> transform >> load return dag # Generate DAGs for config in PIPELINE_CONFIGS: globals()[f"dag_{config['name']}"] = create_dag(config) ``` ### Pattern 3: Branching and Conditional Logic ```python # dags/branching_example.py from airflow.decorators import dag, task from airflow.operators.python import BranchPythonOperator from airflow.operators.empty import EmptyOperator from airflow.utils.trigger_rule import TriggerRule @dag( dag_id='branching_pipeline', schedule='@daily', start_date=datetime(2024, 1, 1), catchup=False, ) def branching_pipeline(): @task() def check_data_quality() -> dict: """Check data quality and return metrics""" quality_score = 0.95 # Simulated return {'score': quality_score, 'rows': 10000} def choose_branch(**context) -> str: """Determine which branch to execute""" ti = context['ti'] metrics = ti.xcom_pull(task_ids='check_data_quality') if metrics['score'] >= 0.9: return 'high_quality_path' elif metrics['score'] >= 0.7: return 'medium_quality_path' else: return 'low_quality_path' quality_check = check_data_quality() branch = BranchPythonOperator( task_id='branch', python_callable=choose_branch, ) high_quality = EmptyOperator(task_id='high_quality_path') medium_quality = EmptyOperator(task_id='medium_quality_path') low_quality = EmptyOperator(task_id='low_quality_path') # Join point - runs after any branch completes join = EmptyOperator( task_id='join', trigger_rule=TriggerRule.NONE_FAILED_MIN_ONE_SUCCESS, ) quality_check >> branch >> [high_quality, medium_quality, low_quality] >> join branching_pipeline() ``` ### Pattern 4: Sensors and External Dependencies ```python # dags/sensor_patterns.py from datetime import datetime, timedelta from airflow import DAG from airflow.sensors.filesystem import FileSensor from airflow.providers.amazon.aws.sensors.s3 import S3KeySensor from airflow.sensors.external_task import ExternalTaskSensor from airflow.operators.python import PythonOperator with DAG( dag_id='sensor_example', schedule='@daily', start_date=datetime(2024, 1, 1), catchup=False, ) as dag: # Wait for file on S3 wait_for_file = S3KeySensor( task_id='wait_for_s3_file', bucket_name='data-lake', bucket_key='raw/{{ ds }}/data.parquet', aws_conn_id='aws_default', timeout=60 * 60 * 2, # 2 hours poke_interval=60 * 5, # Check every 5 minutes mode='reschedule', # Free up worker slot while waiting ) # Wait for another DAG to complete wait_for_upstream = ExternalTaskSensor( task_id='wait_for_upstream_dag', external_dag_id='upstream_etl', external_task_id='final_task', execution_date_fn=lambda dt: dt, # Same execution date timeout=60 * 60 * 3, mode='reschedule', ) # Custom sensor using @task.sensor decorator @task.sensor(poke_interval=60, timeout=3600, mode='reschedule') def wait_for_api() -> PokeReturnValue: """Custom sensor for API availability""" import requests response = requests.get('https://api.example.com/health') is_done = response.status_code == 200 return PokeReturnValue(is_done=is_done, xcom_value=response.json()) api_ready = wait_for_api() def process_data(**context): api_result = context['ti'].xcom_pull(task_ids='wait_for_api') print(f"API returned: {api_result}") process = PythonOperator( task_id='process', python_callable=process_data, ) [wait_for_file, wait_for_upstream, api_ready] >> process ``` ### Pattern 5: Error Handling and Alerts ```python # dags/error_handling.py from datetime import datetime, timedelta from airflow import DAG from airflow.operators.python import PythonOperator from airflow.utils.trigger_rule import TriggerRule from airflow.models import Variable def task_failure_callback(context): """Callback on task failure""" task_instance = context['task_instance'] exception = context.get('exception') # Send to Slack/PagerDuty/etc message = f""" Task Failed! DAG: {task_instance.dag_id} Task: {task_instance.task_id} Execution Date: {context['ds']} Error: {exception} Log URL: {task_instance.log_url} """ # send_slack_alert(message) print(message) def dag_failure_callback(context): """Callback on DAG failure""" # Aggregate failures, send summary pass with DAG( dag_id='error_handling_example', schedule='@daily', start_date=datetime(2024, 1, 1), catchup=False, on_failure_callback=dag_failure_callback, default_args={ 'on_failure_callback': task_failure_callback, 'retries': 3, 'retry_delay': timedelta(minutes=5), }, ) as dag: def might_fail(**context): import random if random.random() < 0.3: raise ValueError("Random failure!") return "Success" risky_task = PythonOperator( task_id='risky_task', python_callable=might_fail, ) def cleanup(**context): """Cleanup runs regardless of upstream failures""" print("Cleaning up...") cleanup_task = PythonOperator( task_id='cleanup', python_callable=cleanup, trigger_rule=TriggerRule.ALL_DONE, # Run even if upstream fails ) def notify_success(**context): """Only runs if all upstream succeeded""" print("All tasks succeeded!") success_notification = PythonOperator( task_id='notify_success', python_callable=notify_success, trigger_rule=TriggerRule.ALL_SUCCESS, ) risky_task >> [cleanup_task, success_notification] ``` ### Pattern 6: Testing DAGs ```python # tests/test_dags.py import pytest from datetime import datetime from airflow.models import DagBag @pytest.fixture def dagbag(): return DagBag(dag_folder='dags/', include_examples=False) def test_dag_loaded(dagbag): """Test that all DAGs load without errors""" assert len(dagbag.import_errors) == 0, f"DAG import errors: {dagbag.import_errors}" def test_dag_structure(dagbag): """Test specific DAG structure""" dag = dagbag.get_dag('example_etl') assert dag is not None assert len(dag.tasks) == 3 assert dag.schedule_interval == '0 6 * * *' def test_task_dependencies(dagbag): """Test task dependencies are correct""" dag = dagbag.get_dag('example_etl') extract_task = dag.get_task('extract') assert 'start' in [t.task_id for t in extract_task.upstream_list] assert 'end' in [t.task_id for t in extract_task.downstream_list] def test_dag_integrity(dagbag): """Test DAG has no cycles and is valid""" for dag_id, dag in dagbag.dags.items(): assert dag.test_cycle() is None, f"Cycle detected in {dag_id}" # Test individual task logic def test_extract_function(): """Unit test for extract function""" from dags.example_dag import extract_data result = extract_data(ds='2024-01-01') assert 'records' in result assert isinstance(result['records'], int) ``` ## Project Structure ``` airflow/ β”œβ”€β”€ dags/ β”‚ β”œβ”€β”€ __init__.py β”‚ β”œβ”€β”€ common/ β”‚ β”‚ β”œβ”€β”€ __init__.py β”‚ β”‚ β”œβ”€β”€ operators.py # Custom operators β”‚ β”‚ β”œβ”€β”€ sensors.py # Custom sensors β”‚ β”‚ └── callbacks.py # Alert callbacks β”‚ β”œβ”€β”€ etl/ β”‚ β”‚ β”œβ”€β”€ customers.py β”‚ β”‚ └── orders.py β”‚ └── ml/ β”‚ └── training.py β”œβ”€β”€ plugins/ β”‚ └── custom_plugin.py β”œβ”€β”€ tests/ β”‚ β”œβ”€β”€ __init__.py β”‚ β”œβ”€β”€ test_dags.py β”‚ └── test_operators.py β”œβ”€β”€ docker-compose.yml └── requirements.txt ``` ## Best Practices ### Do's - **Use TaskFlow API** - Cleaner code, automatic XCom - **Set timeouts** - Prevent zombie tasks - **Use `mode='reschedule'`** - For sensors, free up workers - **Test DAGs** - Unit tests and integration tests - **Idempotent tasks** - Safe to retry ### Don'ts - **Don't use `depends_on_past=True`** - Creates bottlenecks - **Don't hardcode dates** - Use `{{ ds }}` macros - **Don't use global state** - Tasks should be stateless - **Don't skip catchup blindly** - Understand implications - **Don't put heavy logic in DAG file** - Import from modules ## Resources - [Airflow Documentation](https://airflow.apache.org/docs/) - [Astronomer Guides](https://docs.astronomer.io/learn) - [TaskFlow API](https://airflow.apache.org/docs/apache-airflow/stable/tutorial/taskflow.html)
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data-quality-frameworks

Implement data quality validation with Great Expectations, dbt

data
⭐1
# Data Quality Frameworks Production patterns for implementing data quality with Great Expectations, dbt tests, and data contracts to ensure reliable data pipelines. ## When to Use This Skill - Implementing data quality checks in pipelines - Setting up Great Expectations validation - Building comprehensive dbt test suites - Establishing data contracts between teams - Monitoring data quality metrics - Automating data validation in CI/CD ## Core Concepts ### 1. Data Quality Dimensions | Dimension | Description | Example Check | | ---------------- | ------------------------ | -------------------------------------------------- | | **Completeness** | No missing values | `expect_column_values_to_not_be_null` | | **Uniqueness** | No duplicates | `expect_column_values_to_be_unique` | | **Validity** | Values in expected range | `expect_column_values_to_be_in_set` | | **Accuracy** | Data matches reality | Cross-reference validation | | **Consistency** | No contradictions | `expect_column_pair_values_A_to_be_greater_than_B` | | **Timeliness** | Data is recent | `expect_column_max_to_be_between` | ### 2. Testing Pyramid for Data ``` /\ / \ Integration Tests (cross-table) /────\ / \ Unit Tests (single column) /────────\ / \ Schema Tests (structure) /────────────\ ``` ## Quick Start ### Great Expectations Setup ```bash # Install pip install great_expectations # Initialize project great_expectations init # Create datasource great_expectations datasource new ``` ```python # great_expectations/checkpoints/daily_validation.yml import great_expectations as gx # Create context context = gx.get_context() # Create expectation suite suite = context.add_expectation_suite("orders_suite") # Add expectations suite.add_expectation( gx.expectations.ExpectColumnValuesToNotBeNull(column="order_id") ) suite.add_expectation( gx.expectations.ExpectColumnValuesToBeUnique(column="order_id") ) # Validate results = context.run_checkpoint(checkpoint_name="daily_orders") ``` ## Patterns ### Pattern 1: Great Expectations Suite ```python # expectations/orders_suite.py import great_expectations as gx from great_expectations.core import ExpectationSuite from great_expectations.core.expectation_configuration import ExpectationConfiguration def build_orders_suite() -> ExpectationSuite: """Build comprehensive orders expectation suite""" suite = ExpectationSuite(expectation_suite_name="orders_suite") # Schema expectations suite.add_expectation(ExpectationConfiguration( expectation_type="expect_table_columns_to_match_set", kwargs={ "column_set": ["order_id", "customer_id", "amount", "status", "created_at"], "exact_match": False # Allow additional columns } )) # Primary key suite.add_expectation(ExpectationConfiguration( expectation_type="expect_column_values_to_not_be_null", kwargs={"column": "order_id"} )) suite.add_expectation(ExpectationConfiguration( expectation_type="expect_column_values_to_be_unique", kwargs={"column": "order_id"} )) # Foreign key suite.add_expectation(ExpectationConfiguration( expectation_type="expect_column_values_to_not_be_null", kwargs={"column": "customer_id"} )) # Categorical values suite.add_expectation(ExpectationConfiguration( expectation_type="expect_column_values_to_be_in_set", kwargs={ "column": "status", "value_set": ["pending", "processing", "shipped", "delivered", "cancelled"] } )) # Numeric ranges suite.add_expectation(ExpectationConfiguration( expectation_type="expect_column_values_to_be_between", kwargs={ "column": "amount", "min_value": 0, "max_value": 100000, "strict_min": True # amount > 0 } )) # Date validity suite.add_expectation(ExpectationConfiguration( expectation_type="expect_column_values_to_be_dateutil_parseable", kwargs={"column": "created_at"} )) # Freshness - data should be recent suite.add_expectation(ExpectationConfiguration( expectation_type="expect_column_max_to_be_between", kwargs={ "column": "created_at", "min_value": {"$PARAMETER": "now - timedelta(days=1)"}, "max_value": {"$PARAMETER": "now"} } )) # Row count sanity suite.add_expectation(ExpectationConfiguration( expectation_type="expect_table_row_count_to_be_between", kwargs={ "min_value": 1000, # Expect at least 1000 rows "max_value": 10000000 } )) # Statistical expectations suite.add_expectation(ExpectationConfiguration( expectation_type="expect_column_mean_to_be_between", kwargs={ "column": "amount", "min_value": 50, "max_value": 500 } )) return suite ``` ### Pattern 2: Great Expectations Checkpoint ```yaml # great_expectations/checkpoints/orders_checkpoint.yml name: orders_checkpoint config_version: 1.0 class_name: Checkpoint run_name_template: "%Y%m%d-%H%M%S-orders-validation" validations: - batch_request: datasource_name: warehouse data_connector_name: default_inferred_data_connector_name data_asset_name: orders data_connector_query: index: -1 # Latest batch expectation_suite_name: orders_suite action_list: - name: store_validation_result action: class_name: StoreValidationResultAction - name: store_evaluation_parameters action: class_name: StoreEvaluationParametersAction - name: update_data_docs action: class_name: UpdateDataDocsAction # Slack notification on failure - name: send_slack_notification action: class_name: SlackNotificationAction slack_webhook: ${SLACK_WEBHOOK} notify_on: failure renderer: module_name: great_expectations.render.renderer.slack_renderer class_name: SlackRenderer ``` ```python # Run checkpoint import great_expectations as gx context = gx.get_context() result = context.run_checkpoint(checkpoint_name="orders_checkpoint") if not result.success: failed_expectations = [ r for r in result.run_results.values() if not r.success ] raise ValueError(f"Data quality check failed: {failed_expectations}") ``` ### Pattern 3: dbt Data Tests ```yaml # models/marts/core/_core__models.yml version: 2 models: - name: fct_orders description: Order fact table tests: # Table-level tests - dbt_utils.recency: datepart: day field: created_at interval: 1 - dbt_utils.at_least_one - dbt_utils.expression_is_true: expression: "total_amount >= 0" columns: - name: order_id description: Primary key tests: - unique - not_null - name: customer_id description: Foreign key to dim_customers tests: - not_null - relationships: to: ref('dim_customers') field: customer_id - name: order_status tests: - accepted_values: values: ["pending", "processing", "shipped", "delivered", "cancelled"] - name: total_amount tests: - not_null - dbt_utils.expression_is_true: expression: ">= 0" - name: created_at tests: - not_null - dbt_utils.expression_is_true: expression: "<= current_timestamp" - name: dim_customers columns: - name: customer_id tests: - unique - not_null - name: email tests: - unique - not_null # Custom regex test - dbt_utils.expression_is_true: expression: "email ~ '^[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\\.[A-Za-z]{2,}$'" ``` ### Pattern 4: Custom dbt Tests ```sql -- tests/generic/test_row_count_in_range.sql {% test row_count_in_range(model, min_count, max_count) %} with row_count as ( select count(*) as cnt from {{ model }} ) select cnt from row_count where cnt < {{ min_count }} or cnt > {{ max_count }} {% endtest %} -- Usage in schema.yml: -- tests: -- - row_count_in_range: -- min_count: 1000 -- max_count: 10000000 ``` ```sql -- tests/generic/test_sequential_values.sql {% test sequential_values(model, column_name, interval=1) %} with lagged as ( select {{ column_name }}, lag({{ column_name }}) over (order by {{ column_name }}) as prev_value from {{ model }} ) select * from lagged where {{ column_name }} - prev_value != {{ interval }} and prev_value is not null {% endtest %} ``` ```sql -- tests/singular/assert_orders_customers_match.sql -- Singular test: specific business rule with orders_customers as ( select distinct customer_id from {{ ref('fct_orders') }} ), dim_customers as ( select customer_id from {{ ref('dim_customers') }} ), orphaned_orders as ( select o.customer_id from orders_customers o left join dim_customers c using (customer_id) where c.customer_id is null ) select * from orphaned_orders -- Test passes if this returns 0 rows ``` ### Pattern 5: Data Contracts ```yaml # contracts/orders_contract.yaml apiVersion: datacontract.com/v1.0.0 kind: DataContract metadata: name: orders version: 1.0.0 owner: data-platform-team contact: data-team@company.com info: title: Orders Data Contract description: Contract for order event data from the ecommerce platform purpose: Analytics, reporting, and ML features servers: production: type: snowflake account: company.us-east-1 database: ANALYTICS schema: CORE terms: usage: Internal analytics only limitations: PII must not be exposed in downstream marts billing: Charged per query TB scanned schema: type: object properties: order_id: type: string format: uuid description: Unique order identifier required: true unique: true pii: false customer_id: type: string format: uuid description: Customer identifier required: true pii: true piiClassification: indirect total_amount: type: number minimum: 0 maximum: 100000 description: Order total in USD created_at: type: string format: date-time description: Order creation timestamp required: true status: type: string enum: [pending, processing, shipped, delivered, cancelled] description: Current order status quality: type: SodaCL specification: checks for orders: - row_count > 0 - missing_count(order_id) = 0 - duplicate_count(order_id) = 0 - invalid_count(status) = 0: valid values: [pending, processing, shipped, delivered, cancelled] - freshness(created_at) < 24h sla: availability: 99.9% freshness: 1 hour latency: 5 minutes ``` ### Pattern 6: Automated Quality Pipeline ```python # quality_pipeline.py from dataclasses import dataclass from typing import List, Dict, Any import great_expectations as gx from datetime import datetime @dataclass class QualityResult: table: str passed: bool total_expectations: int failed_expectations: int details: List[Dict[str, Any]] timestamp: datetime class DataQualityPipeline: """Orchestrate data quality checks across tables""" def __init__(self, context: gx.DataContext): self.context = context self.results: List[QualityResult] = [] def validate_table(self, table: str, suite: str) -> QualityResult: """Validate a single table against expectation suite""" checkpoint_config = { "name": f"{table}_validation", "config_version": 1.0, "class_name": "Checkpoint", "validations": [{ "batch_request": { "datasource_name": "warehouse", "data_asset_name": table, }, "expectation_suite_name": suite, }], } result = self.context.run_checkpoint(**checkpoint_config) # Parse results validation_result = list(result.run_results.values())[0] results = validation_result.results failed = [r for r in results if not r.success] return QualityResult( table=table, passed=result.success, total_expectations=len(results), failed_expectations=len(failed), details=[{ "expectation": r.expectation_config.expectation_type, "success": r.success, "observed_value": r.result.get("observed_value"), } for r in results], timestamp=datetime.now() ) def run_all(self, tables: Dict[str, str]) -> Dict[str, QualityResult]: """Run validation for all tables""" results = {} for table, suite in tables.items(): print(f"Validating {table}...") results[table] = self.validate_table(table, suite) return results def generate_report(self, results: Dict[str, QualityResult]) -> str: """Generate quality report""" report = ["# Data Quality Report", f"Generated: {datetime.now()}", ""] total_passed = sum(1 for r in results.values() if r.passed) total_tables = len(results) report.append(f"## Summary: {total_passed}/{total_tables} tables passed") report.append("") for table, result in results.items(): status = "βœ…" if result.passed else "❌" report.append(f"### {status} {table}") report.append(f"- Expectations: {result.total_expectations}") report.append(f"- Failed: {result.failed_expectations}") if not result.passed: report.append("- Failed checks:") for detail in result.details: if not detail["success"]: report.append(f" - {detail['expectation']}: {detail['observed_value']}") report.append("") return "\n".join(report) # Usage context = gx.get_context() pipeline = DataQualityPipeline(context) tables_to_validate = { "orders": "orders_suite", "customers": "customers_suite", "products": "products_suite", } results = pipeline.run_all(tables_to_validate) report = pipeline.generate_report(results) # Fail pipeline if any table failed if not all(r.passed for r in results.values()): print(report) raise ValueError("Data quality checks failed!") ``` ## Best Practices ### Do's - **Test early** - Validate source data before transformations - **Test incrementally** - Add tests as you find issues - **Document expectations** - Clear descriptions for each test - **Alert on failures** - Integrate with monitoring - **Version contracts** - Track schema changes ### Don'ts - **Don't test everything** - Focus on critical columns - **Don't ignore warnings** - They often precede failures - **Don't skip freshness** - Stale data is bad data - **Don't hardcode thresholds** - Use dynamic baselines - **Don't test in isolation** - Test relationships too ## Resources - [Great Expectations Documentation](https://docs.greatexpectations.io/) - [dbt Testing Documentation](https://docs.getdbt.com/docs/build/tests) - [Data Contract Specification](https://datacontract.com/) - [Soda Core](https://docs.soda.io/soda-core/overview.html)
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context-driven-development

Creates and maintains project context artifacts (product.md,

coding
⭐1
# Context-Driven Development Guide for implementing and maintaining context as a managed artifact alongside code, enabling consistent AI interactions and team alignment through structured project documentation. ## When to Use This Skill - Setting up new projects with Conductor - Understanding the relationship between context artifacts - Maintaining consistency across AI-assisted development sessions - Onboarding team members to an existing Conductor project - Deciding when to update context documents - Managing greenfield vs brownfield project contexts ## Core Philosophy Context-Driven Development treats project context as a first-class artifact managed alongside code. Instead of relying on ad-hoc prompts or scattered documentation, establish a persistent, structured foundation that informs all AI interactions. Key principles: 1. **Context precedes code**: Define what you're building and how before implementation 2. **Living documentation**: Context artifacts evolve with the project 3. **Single source of truth**: One canonical location for each type of information 4. **AI alignment**: Consistent context produces consistent AI behavior ## The Workflow Follow the **Context β†’ Spec & Plan β†’ Implement** workflow: 1. **Context Phase**: Establish or verify project context artifacts exist and are current 2. **Specification Phase**: Define requirements and acceptance criteria for work units 3. **Planning Phase**: Break specifications into phased, actionable tasks 4. **Implementation Phase**: Execute tasks following established workflow patterns ## Artifact Relationships ### product.md - Defines WHAT and WHY Purpose: Captures product vision, goals, target users, and business context. Contents: - Product name and one-line description - Problem statement and solution approach - Target user personas - Core features and capabilities - Success metrics and KPIs - Product roadmap (high-level) Update when: - Product vision or goals change - New major features are planned - Target audience shifts - Business priorities evolve ### product-guidelines.md - Defines HOW to Communicate Purpose: Establishes brand voice, messaging standards, and communication patterns. Contents: - Brand voice and tone guidelines - Terminology and glossary - Error message conventions - User-facing copy standards - Documentation style Update when: - Brand guidelines change - New terminology is introduced - Communication patterns need refinement ### tech-stack.md - Defines WITH WHAT Purpose: Documents technology choices, dependencies, and architectural decisions. Contents: - Primary languages and frameworks - Key dependencies with versions - Infrastructure and deployment targets - Development tools and environment - Testing frameworks - Code quality tools Update when: - Adding new dependencies - Upgrading major versions - Changing infrastructure - Adopting new tools or patterns ### workflow.md - Defines HOW to Work Purpose: Establishes development practices, quality gates, and team workflows. Contents: - Development methodology (TDD, etc.) - Git workflow and commit conventions - Code review requirements - Testing requirements and coverage targets - Quality assurance gates - Deployment procedures Update when: - Team practices evolve - Quality standards change - New workflow patterns are adopted ### tracks.md - Tracks WHAT'S HAPPENING Purpose: Registry of all work units with status and metadata. Contents: - Active tracks with current status - Completed tracks with completion dates - Track metadata (type, priority, assignee) - Links to individual track directories Update when: - New tracks are created - Track status changes - Tracks are completed or archived See [references/artifact-templates.md](references/artifact-templates.md) for copy-paste starter templates. ## Context Maintenance Principles ### Keep Artifacts Synchronized Ensure changes in one artifact reflect in related documents: - New feature in product.md β†’ Update tech-stack.md if new dependencies needed - Completed track β†’ Update product.md to reflect new capabilities - Workflow change β†’ Update all affected track plans ### Update tech-stack.md When Adding Dependencies Before adding any new dependency: 1. Check if existing dependencies solve the need 2. Document the rationale for new dependencies 3. Add version constraints 4. Note any configuration requirements ### Update product.md When Features Complete After completing a feature track: 1. Move feature from "planned" to "implemented" in product.md 2. Update any affected success metrics 3. Document any scope changes from original plan ### Verify Context Before Implementation Before starting any track: 1. Read all context artifacts 2. Flag any outdated information 3. Propose updates before proceeding 4. Confirm context accuracy with stakeholders ## Greenfield vs Brownfield Handling ### Greenfield Projects (New) For new projects: 1. Run `/conductor:setup` to create all artifacts interactively 2. Answer questions about product vision, tech preferences, and workflow 3. Generate initial style guides for chosen languages 4. Create empty tracks registry Characteristics: - Full control over context structure - Define standards before code exists - Establish patterns early ### Brownfield Projects (Existing) For existing codebases: 1. Run `/conductor:setup` with existing codebase detection 2. System analyzes existing code, configs, and documentation 3. Pre-populate artifacts based on discovered patterns 4. Review and refine generated context Characteristics: - Extract implicit context from existing code - Reconcile existing patterns with desired patterns - Document technical debt and modernization plans - Preserve working patterns while establishing standards ## Benefits ### Team Alignment - New team members onboard faster with explicit context - Consistent terminology and conventions across the team - Shared understanding of product goals and technical decisions ### AI Consistency - AI assistants produce aligned outputs across sessions - Reduced need to re-explain context in each interaction - Predictable behavior based on documented standards ### Institutional Memory - Decisions and rationale are preserved - Context survives team changes - Historical context informs future decisions ### Quality Assurance - Standards are explicit and verifiable - Deviations from context are detectable - Quality gates are documented and enforceable ## Directory Structure ``` conductor/ β”œβ”€β”€ index.md # Navigation hub linking all artifacts β”œβ”€β”€ product.md # Product vision and goals β”œβ”€β”€ product-guidelines.md # Communication standards β”œβ”€β”€ tech-stack.md # Technology preferences β”œβ”€β”€ workflow.md # Development practices β”œβ”€β”€ tracks.md # Work unit registry β”œβ”€β”€ setup_state.json # Resumable setup state β”œβ”€β”€ code_styleguides/ # Language-specific conventions β”‚ β”œβ”€β”€ python.md β”‚ β”œβ”€β”€ typescript.md β”‚ └── ... └── tracks/ └── <track-id>/ β”œβ”€β”€ spec.md β”œβ”€β”€ plan.md β”œβ”€β”€ metadata.json └── index.md ``` ## Context Lifecycle 1. **Creation**: Initial setup via `/conductor:setup` 2. **Validation**: Verify before each track 3. **Evolution**: Update as project grows 4. **Synchronization**: Keep artifacts aligned 5. **Archival**: Document historical decisions ## Context Validation Checklist Before starting implementation on any track, validate context: ### Product Context - [ ] product.md reflects current product vision - [ ] Target users are accurately described - [ ] Feature list is up to date - [ ] Success metrics are defined ### Technical Context - [ ] tech-stack.md lists all current dependencies - [ ] Version numbers are accurate - [ ] Infrastructure targets are correct - [ ] Development tools are documented ### Workflow Context - [ ] workflow.md describes current practices - [ ] Quality gates are defined - [ ] Coverage targets are specified - [ ] Commit conventions are documented ### Track Context - [ ] tracks.md shows all active work - [ ] No stale or abandoned tracks - [ ] Dependencies between tracks are noted ## Common Anti-Patterns Avoid these context management mistakes: ### Stale Context Problem: Context documents become outdated and misleading. Solution: Update context as part of each track's completion process. ### Context Sprawl Problem: Information scattered across multiple locations. Solution: Use the defined artifact structure; resist creating new document types. ### Implicit Context Problem: Relying on knowledge not captured in artifacts. Solution: If you reference something repeatedly, add it to the appropriate artifact. ### Context Hoarding Problem: One person maintains context without team input. Solution: Review context artifacts in pull requests; make updates collaborative. ### Over-Specification Problem: Context becomes so detailed it's impossible to maintain. Solution: Keep artifacts focused on decisions that affect AI behavior and team alignment. ## Integration with Development Tools ### IDE Integration Configure your IDE to display context files prominently: - Pin conductor/product.md for quick reference - Add tech-stack.md to project notes - Create snippets for common patterns from style guides ### Git Hooks Consider pre-commit hooks that: - Warn when dependencies change without tech-stack.md update - Remind to update product.md when feature branches merge - Validate context artifact syntax ### CI/CD Integration Include context validation in pipelines: - Check tech-stack.md matches actual dependencies - Verify links in context documents resolve - Ensure tracks.md status matches git branch state ## Session Continuity Conductor supports multi-session development through context persistence: ### Starting a New Session 1. Read index.md to orient yourself 2. Check tracks.md for active work 3. Review relevant track's plan.md for current task 4. Verify context artifacts are current ### Ending a Session 1. Update plan.md with current progress 2. Note any blockers or decisions made 3. Commit in-progress work with clear status 4. Update tracks.md if status changed ### Handling Interruptions If interrupted mid-task: 1. Mark task as `[~]` with note about stopping point 2. Commit work-in-progress to feature branch 3. Document any uncommitted decisions in plan.md ## Best Practices 1. **Read context first**: Always read relevant artifacts before starting work 2. **Small updates**: Make incremental context changes, not massive rewrites 3. **Link decisions**: Reference context when making implementation choices 4. **Version context**: Commit context changes alongside code changes 5. **Review context**: Include context artifact reviews in code reviews 6. **Validate regularly**: Run context validation checklist before major work 7. **Communicate changes**: Notify team when context artifacts change significantly 8. **Preserve history**: Use git to track context evolution over time 9. **Question staleness**: If context feels wrong, investigate and update 10. **Keep it actionable**: Every context item should inform a decision or behavior
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πŸ€– Auto-discovered
πŸ€–system promptβ€’7 months ago

service-mesh-observability

Implement comprehensive observability for service meshes including

architecture
⭐1
# Service Mesh Observability Complete guide to observability patterns for Istio, Linkerd, and service mesh deployments. ## When to Use This Skill - Setting up distributed tracing across services - Implementing service mesh metrics and dashboards - Debugging latency and error issues - Defining SLOs for service communication - Visualizing service dependencies - Troubleshooting mesh connectivity ## Core Concepts ### 1. Three Pillars of Observability ``` β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”‚ Observability β”‚ β”œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€ β”‚ Metrics β”‚ Traces β”‚ Logs β”‚ β”‚ β”‚ β”‚ β”‚ β”‚ β€’ Request rate β”‚ β€’ Span context β”‚ β€’ Access logs β”‚ β”‚ β€’ Error rate β”‚ β€’ Latency β”‚ β€’ Error details β”‚ β”‚ β€’ Latency P50 β”‚ β€’ Dependencies β”‚ β€’ Debug info β”‚ β”‚ β€’ Saturation β”‚ β€’ Bottlenecks β”‚ β€’ Audit trail β”‚ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ ``` ### 2. Golden Signals for Mesh | Signal | Description | Alert Threshold | | -------------- | ------------------------- | ----------------- | | **Latency** | Request duration P50, P99 | P99 > 500ms | | **Traffic** | Requests per second | Anomaly detection | | **Errors** | 5xx error rate | > 1% | | **Saturation** | Resource utilization | > 80% | ## Templates ### Template 1: Istio with Prometheus & Grafana ```yaml # Install Prometheus apiVersion: v1 kind: ConfigMap metadata: name: prometheus namespace: istio-system data: prometheus.yml: | global: scrape_interval: 15s scrape_configs: - job_name: 'istio-mesh' kubernetes_sd_configs: - role: endpoints namespaces: names: - istio-system relabel_configs: - source_labels: [__meta_kubernetes_service_name] action: keep regex: istio-telemetry --- # ServiceMonitor for Prometheus Operator apiVersion: monitoring.coreos.com/v1 kind: ServiceMonitor metadata: name: istio-mesh namespace: istio-system spec: selector: matchLabels: app: istiod endpoints: - port: http-monitoring interval: 15s ``` ### Template 2: Key Istio Metrics Queries ```promql # Request rate by service sum(rate(istio_requests_total{reporter="destination"}[5m])) by (destination_service_name) # Error rate (5xx) sum(rate(istio_requests_total{reporter="destination", response_code=~"5.."}[5m])) / sum(rate(istio_requests_total{reporter="destination"}[5m])) * 100 # P99 latency histogram_quantile(0.99, sum(rate(istio_request_duration_milliseconds_bucket{reporter="destination"}[5m])) by (le, destination_service_name)) # TCP connections sum(istio_tcp_connections_opened_total{reporter="destination"}) by (destination_service_name) # Request size histogram_quantile(0.99, sum(rate(istio_request_bytes_bucket{reporter="destination"}[5m])) by (le, destination_service_name)) ``` ### Template 3: Jaeger Distributed Tracing ```yaml # Jaeger installation for Istio apiVersion: install.istio.io/v1alpha1 kind: IstioOperator spec: meshConfig: enableTracing: true defaultConfig: tracing: sampling: 100.0 # 100% in dev, lower in prod zipkin: address: jaeger-collector.istio-system:9411 --- # Jaeger deployment apiVersion: apps/v1 kind: Deployment metadata: name: jaeger namespace: istio-system spec: selector: matchLabels: app: jaeger template: metadata: labels: app: jaeger spec: containers: - name: jaeger image: jaegertracing/all-in-one:1.50 ports: - containerPort: 5775 # UDP - containerPort: 6831 # Thrift - containerPort: 6832 # Thrift - containerPort: 5778 # Config - containerPort: 16686 # UI - containerPort: 14268 # HTTP - containerPort: 14250 # gRPC - containerPort: 9411 # Zipkin env: - name: COLLECTOR_ZIPKIN_HOST_PORT value: ":9411" ``` ### Template 4: Linkerd Viz Dashboard ```bash # Install Linkerd viz extension linkerd viz install | kubectl apply -f - # Access dashboard linkerd viz dashboard # CLI commands for observability # Top requests linkerd viz top deploy/my-app # Per-route metrics linkerd viz routes deploy/my-app --to deploy/backend # Live traffic inspection linkerd viz tap deploy/my-app --to deploy/backend # Service edges (dependencies) linkerd viz edges deployment -n my-namespace ``` ### Template 5: Grafana Dashboard JSON ```json { "dashboard": { "title": "Service Mesh Overview", "panels": [ { "title": "Request Rate", "type": "graph", "targets": [ { "expr": "sum(rate(istio_requests_total{reporter=\"destination\"}[5m])) by (destination_service_name)", "legendFormat": "{{destination_service_name}}" } ] }, { "title": "Error Rate", "type": "gauge", "targets": [ { "expr": "sum(rate(istio_requests_total{response_code=~\"5..\"}[5m])) / sum(rate(istio_requests_total[5m])) * 100" } ], "fieldConfig": { "defaults": { "thresholds": { "steps": [ { "value": 0, "color": "green" }, { "value": 1, "color": "yellow" }, { "value": 5, "color": "red" } ] } } } }, { "title": "P99 Latency", "type": "graph", "targets": [ { "expr": "histogram_quantile(0.99, sum(rate(istio_request_duration_milliseconds_bucket{reporter=\"destination\"}[5m])) by (le, destination_service_name))", "legendFormat": "{{destination_service_name}}" } ] }, { "title": "Service Topology", "type": "nodeGraph", "targets": [ { "expr": "sum(rate(istio_requests_total{reporter=\"destination\"}[5m])) by (source_workload, destination_service_name)" } ] } ] } } ``` ### Template 6: Kiali Service Mesh Visualization ```yaml # Kiali installation apiVersion: kiali.io/v1alpha1 kind: Kiali metadata: name: kiali namespace: istio-system spec: auth: strategy: anonymous # or openid, token deployment: accessible_namespaces: - "**" external_services: prometheus: url: http://prometheus.istio-system:9090 tracing: url: http://jaeger-query.istio-system:16686 grafana: url: http://grafana.istio-system:3000 ``` ### Template 7: OpenTelemetry Integration ```yaml # OpenTelemetry Collector for mesh apiVersion: v1 kind: ConfigMap metadata: name: otel-collector-config data: config.yaml: | receivers: otlp: protocols: grpc: endpoint: 0.0.0.0:4317 http: endpoint: 0.0.0.0:4318 zipkin: endpoint: 0.0.0.0:9411 processors: batch: timeout: 10s exporters: jaeger: endpoint: jaeger-collector:14250 tls: insecure: true prometheus: endpoint: 0.0.0.0:8889 service: pipelines: traces: receivers: [otlp, zipkin] processors: [batch] exporters: [jaeger] metrics: receivers: [otlp] processors: [batch] exporters: [prometheus] --- # Istio Telemetry v2 with OTel apiVersion: telemetry.istio.io/v1alpha1 kind: Telemetry metadata: name: mesh-default namespace: istio-system spec: tracing: - providers: - name: otel randomSamplingPercentage: 10 ``` ## Alerting Rules ```yaml apiVersion: monitoring.coreos.com/v1 kind: PrometheusRule metadata: name: mesh-alerts namespace: istio-system spec: groups: - name: mesh.rules rules: - alert: HighErrorRate expr: | sum(rate(istio_requests_total{response_code=~"5.."}[5m])) by (destination_service_name) / sum(rate(istio_requests_total[5m])) by (destination_service_name) > 0.05 for: 5m labels: severity: critical annotations: summary: "High error rate for {{ $labels.destination_service_name }}" - alert: HighLatency expr: | histogram_quantile(0.99, sum(rate(istio_request_duration_milliseconds_bucket[5m])) by (le, destination_service_name)) > 1000 for: 5m labels: severity: warning annotations: summary: "High P99 latency for {{ $labels.destination_service_name }}" - alert: MeshCertExpiring expr: | (certmanager_certificate_expiration_timestamp_seconds - time()) / 86400 < 7 labels: severity: warning annotations: summary: "Mesh certificate expiring in less than 7 days" ``` ## Best Practices ### Do's - **Sample appropriately** - 100% in dev, 1-10% in prod - **Use trace context** - Propagate headers consistently - **Set up alerts** - For golden signals - **Correlate metrics/traces** - Use exemplars - **Retain strategically** - Hot/cold storage tiers ### Don'ts - **Don't over-sample** - Storage costs add up - **Don't ignore cardinality** - Limit label values - **Don't skip dashboards** - Visualize dependencies - **Don't forget costs** - Monitor observability costs ## Resources - [Istio Observability](https://istio.io/latest/docs/tasks/observability/) - [Linkerd Observability](https://linkerd.io/2.14/features/dashboard/) - [OpenTelemetry](https://opentelemetry.io/) - [Kiali](https://kiali.io/)
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πŸ‘οΈ0
πŸ€– Auto-discovered
πŸ€–system promptβ€’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 β†’ Conflict β†’ 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 β†’ 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: β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”‚ BEFORE β”‚ AFTER β”‚ β”‚ β”‚ β”‚ β”‚ Process: 5 daysβ”‚ Process: 1 day β”‚ β”‚ Errors: 15% β”‚ Errors: 2% β”‚ β”‚ Cost: $50/unit β”‚ Cost: $20/unit β”‚ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ This/That (emphasize difference): β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”‚ CUSTOMER A vs B β”‚ β”‚ β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”‚ β”‚ β”‚ β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ β”‚ β”‚ β–ˆβ–ˆ β”‚ β”‚ β”‚ β”‚ $45,000 β”‚ β”‚ $8,000 β”‚ β”‚ β”‚ β”‚ LTV β”‚ β”‚ LTV β”‚ β”‚ β”‚ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ β”‚ β”‚ Onboarded No onboarding β”‚ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ ``` ### 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 ``` β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”‚ KEY INSIGHT β”‚ β”‚ ══════════════════════════════════════════════════════════│ β”‚ β”‚ β”‚ "Customers who complete onboarding in week 1 β”‚ β”‚ have 3x higher lifetime value" β”‚ β”‚ β”‚ β”œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€ β”‚ β”‚ β”‚ β”‚ THE DATA β”‚ THE IMPLICATION β”‚ β”‚ β”‚ β”‚ β”‚ Week 1 completers: β”‚ βœ“ Prioritize onboarding UX β”‚ β”‚ β€’ LTV: $4,500 β”‚ βœ“ Add day-1 success milestones β”‚ β”‚ β€’ Retention: 85% β”‚ βœ“ Proactive week-1 outreach β”‚ β”‚ β€’ NPS: 72 β”‚ β”‚ β”‚ β”‚ Investment: $75K β”‚ β”‚ Others: β”‚ Expected ROI: 8x β”‚ β”‚ β€’ LTV: $1,500 β”‚ β”‚ β”‚ β€’ Retention: 45% β”‚ β”‚ β”‚ β€’ NPS: 34 β”‚ β”‚ β”‚ β”‚ β”‚ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ ``` ### 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 β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β” β”‚ MRR β”‚ NRR β”‚ CAC β”‚ LTV β”‚ β”‚ $125K β”‚ 108% β”‚ $450 β”‚ $2,200 β”‚ β”‚ β–²15% β”‚ β–²3% β”‚ β–²22% β”‚ β–²8% β”‚ β””β”€β”€β”€β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”€β”€β”€β”˜ ## WHAT'S WORKING βœ“ Enterprise segment growing 25% MoM βœ“ Referral program driving 30% of new logos βœ“ Support satisfaction at all-time high (94%) ## WHAT NEEDS ATTENTION βœ— SMB acquisition cost up 40% βœ— Trial conversion down 5 points βœ— 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: β€’ "This leads us to ask..." β€’ "When we dig deeper..." β€’ "The pattern becomes clear when..." β€’ "Contrast this with..." Introducing insights: β€’ "The data reveals..." β€’ "What surprised us was..." β€’ "The inflection point came when..." β€’ "The key finding is..." Moving to action: β€’ "This insight suggests..." β€’ "Based on this analysis..." β€’ "The implication is clear..." β€’ "Our recommendation is..." ``` ### Handling Uncertainty ```markdown Acknowledge limitations: β€’ "With 95% confidence, we can say..." β€’ "The sample size of 500 shows..." β€’ "While correlation is strong, causation requires..." β€’ "This trend holds for [segment], though [caveat]..." Present ranges: β€’ "Impact estimate: $400K-$600K" β€’ "Confidence interval: 15-20% improvement" β€’ "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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react-modernization

Upgrade React applications to latest versions, migrate from class

coding
⭐1
# React Modernization Master React version upgrades, class to hooks migration, concurrent features adoption, and codemods for automated transformation. ## When to Use This Skill - Upgrading React applications to latest versions - Migrating class components to functional components with hooks - Adopting concurrent React features (Suspense, transitions) - Applying codemods for automated refactoring - Modernizing state management patterns - Updating to TypeScript - Improving performance with React 18+ features ## Version Upgrade Path ### React 16 β†’ 17 β†’ 18 **Breaking Changes by Version:** **React 17:** - Event delegation changes - No event pooling - Effect cleanup timing - JSX transform (no React import needed) **React 18:** - Automatic batching - Concurrent rendering - Strict Mode changes (double invocation) - New root API - Suspense on server ## Class to Hooks Migration ### State Management ```javascript // Before: Class component class Counter extends React.Component { constructor(props) { super(props); this.state = { count: 0, name: "", }; } increment = () => { this.setState({ count: this.state.count + 1 }); }; render() { return ( <div> <p>Count: {this.state.count}</p> <button onClick={this.increment}>Increment</button> </div> ); } } // After: Functional component with hooks function Counter() { const [count, setCount] = useState(0); const [name, setName] = useState(""); const increment = () => { setCount(count + 1); }; return ( <div> <p>Count: {count}</p> <button onClick={increment}>Increment</button> </div> ); } ``` ### Lifecycle Methods to Hooks ```javascript // Before: Lifecycle methods class DataFetcher extends React.Component { state = { data: null, loading: true }; componentDidMount() { this.fetchData(); } componentDidUpdate(prevProps) { if (prevProps.id !== this.props.id) { this.fetchData(); } } componentWillUnmount() { this.cancelRequest(); } fetchData = async () => { const data = await fetch(`/api/${this.props.id}`); this.setState({ data, loading: false }); }; cancelRequest = () => { // Cleanup }; render() { if (this.state.loading) return <div>Loading...</div>; return <div>{this.state.data}</div>; } } // After: useEffect hook function DataFetcher({ id }) { const [data, setData] = useState(null); const [loading, setLoading] = useState(true); useEffect(() => { let cancelled = false; const fetchData = async () => { try { const response = await fetch(`/api/${id}`); const result = await response.json(); if (!cancelled) { setData(result); setLoading(false); } } catch (error) { if (!cancelled) { console.error(error); } } }; fetchData(); // Cleanup function return () => { cancelled = true; }; }, [id]); // Re-run when id changes if (loading) return <div>Loading...</div>; return <div>{data}</div>; } ``` ### Context and HOCs to Hooks ```javascript // Before: Context consumer and HOC const ThemeContext = React.createContext(); class ThemedButton extends React.Component { static contextType = ThemeContext; render() { return ( <button style={{ background: this.context.theme }}> {this.props.children} </button> ); } } // After: useContext hook function ThemedButton({ children }) { const { theme } = useContext(ThemeContext); return <button style={{ background: theme }}>{children}</button>; } // Before: HOC for data fetching function withUser(Component) { return class extends React.Component { state = { user: null }; componentDidMount() { fetchUser().then((user) => this.setState({ user })); } render() { return <Component {...this.props} user={this.state.user} />; } }; } // After: Custom hook function useUser() { const [user, setUser] = useState(null); useEffect(() => { fetchUser().then(setUser); }, []); return user; } function UserProfile() { const user = useUser(); if (!user) return <div>Loading...</div>; return <div>{user.name}</div>; } ``` ## React 18 Concurrent Features ### New Root API ```javascript // Before: React 17 import ReactDOM from "react-dom"; ReactDOM.render(<App />, document.getElementById("root")); // After: React 18 import { createRoot } from "react-dom/client"; const root = createRoot(document.getElementById("root")); root.render(<App />); ``` ### Automatic Batching ```javascript // React 18: All updates are batched function handleClick() { setCount((c) => c + 1); setFlag((f) => !f); // Only one re-render (batched) } // Even in async: setTimeout(() => { setCount((c) => c + 1); setFlag((f) => !f); // Still batched in React 18! }, 1000); // Opt out if needed import { flushSync } from "react-dom"; flushSync(() => { setCount((c) => c + 1); }); // Re-render happens here setFlag((f) => !f); // Another re-render ``` ### Transitions ```javascript import { useState, useTransition } from "react"; function SearchResults() { const [query, setQuery] = useState(""); const [results, setResults] = useState([]); const [isPending, startTransition] = useTransition(); const handleChange = (e) => { // Urgent: Update input immediately setQuery(e.target.value); // Non-urgent: Update results (can be interrupted) startTransition(() => { setResults(searchResults(e.target.value)); }); }; return ( <> <input value={query} onChange={handleChange} /> {isPending && <Spinner />} <Results data={results} /> </> ); } ``` ### Suspense for Data Fetching ```javascript import { Suspense } from "react"; // Resource-based data fetching (with React 18) const resource = fetchProfileData(); function ProfilePage() { return ( <Suspense fallback={<Loading />}> <ProfileDetails /> <Suspense fallback={<Loading />}> <ProfileTimeline /> </Suspense> </Suspense> ); } function ProfileDetails() { // This will suspend if data not ready const user = resource.user.read(); return <h1>{user.name}</h1>; } function ProfileTimeline() { const posts = resource.posts.read(); return <Timeline posts={posts} />; } ``` ## Codemods for Automation ### Run React Codemods ```bash # Rename unsafe lifecycle methods npx jscodeshift -t https://raw.githubusercontent.com/reactjs/react-codemod/master/transforms/rename-unsafe-lifecycles.js src/ # Update React imports (React 17+) npx jscodeshift -t https://raw.githubusercontent.com/reactjs/react-codemod/master/transforms/update-react-imports.js src/ # Add error boundaries npx jscodeshift -t https://raw.githubusercontent.com/reactjs/react-codemod/master/transforms/error-boundaries.js src/ # For TypeScript files npx jscodeshift -t https://raw.githubusercontent.com/reactjs/react-codemod/master/transforms/rename-unsafe-lifecycles.js --parser=tsx src/ # Dry run to preview changes npx jscodeshift -t https://raw.githubusercontent.com/reactjs/react-codemod/master/transforms/rename-unsafe-lifecycles.js --dry --print src/ # Class to Hooks (third-party) npx codemod react/hooks/convert-class-to-function src/ ``` ### Custom Codemod Example ```javascript // custom-codemod.js module.exports = function (file, api) { const j = api.jscodeshift; const root = j(file.source); // Find setState calls root .find(j.CallExpression, { callee: { type: "MemberExpression", property: { name: "setState" }, }, }) .forEach((path) => { // Transform to useState // ... transformation logic }); return root.toSource(); }; // Run: jscodeshift -t custom-codemod.js src/ ``` ## Performance Optimization ### useMemo and useCallback ```javascript function ExpensiveComponent({ items, filter }) { // Memoize expensive calculation const filteredItems = useMemo(() => { return items.filter((item) => item.category === filter); }, [items, filter]); // Memoize callback to prevent child re-renders const handleClick = useCallback((id) => { console.log("Clicked:", id); }, []); // No dependencies, never changes return <List items={filteredItems} onClick={handleClick} />; } // Child component with memo const List = React.memo(({ items, onClick }) => { return items.map((item) => ( <Item key={item.id} item={item} onClick={onClick} /> )); }); ``` ### Code Splitting ```javascript import { lazy, Suspense } from "react"; // Lazy load components const Dashboard = lazy(() => import("./Dashboard")); const Settings = lazy(() => import("./Settings")); function App() { return ( <Suspense fallback={<Loading />}> <Routes> <Route path="/dashboard" element={<Dashboard />} /> <Route path="/settings" element={<Settings />} /> </Routes> </Suspense> ); } ``` ## TypeScript Migration ```typescript // Before: JavaScript function Button({ onClick, children }) { return <button onClick={onClick}>{children}</button>; } // After: TypeScript interface ButtonProps { onClick: () => void; children: React.ReactNode; } function Button({ onClick, children }: ButtonProps) { return <button onClick={onClick}>{children}</button>; } // Generic components interface ListProps<T> { items: T[]; renderItem: (item: T) => React.ReactNode; } function List<T>({ items, renderItem }: ListProps<T>) { return <>{items.map(renderItem)}</>; } ``` ## Migration Checklist ```markdown ### Pre-Migration - [ ] Update dependencies incrementally (not all at once) - [ ] Review breaking changes in release notes - [ ] Set up testing suite - [ ] Create feature branch ### Class β†’ Hooks Migration - [ ] Identify class components to migrate - [ ] Start with leaf components (no children) - [ ] Convert state to useState - [ ] Convert lifecycle to useEffect - [ ] Convert context to useContext - [ ] Extract custom hooks - [ ] Test thoroughly ### React 18 Upgrade - [ ] Update to React 17 first (if needed) - [ ] Update react and react-dom to 18 - [ ] Update @types/react if using TypeScript - [ ] Change to createRoot API - [ ] Test with StrictMode (double invocation) - [ ] Address concurrent rendering issues - [ ] Adopt Suspense/Transitions where beneficial ### Performance - [ ] Identify performance bottlenecks - [ ] Add React.memo where appropriate - [ ] Use useMemo/useCallback for expensive operations - [ ] Implement code splitting - [ ] Optimize re-renders ### Testing - [ ] Update test utilities (React Testing Library) - [ ] Test with React 18 features - [ ] Check for warnings in console - [ ] Performance testing ``` ## Resources - **references/breaking-changes.md**: Version-specific breaking changes - **references/codemods.md**: Codemod usage guide - **references/hooks-migration.md**: Comprehensive hooks patterns - **references/concurrent-features.md**: React 18 concurrent features - **assets/codemod-config.json**: Codemod configurations - **assets/migration-checklist.md**: Step-by-step checklist - **scripts/apply-codemods.sh**: Automated codemod script ## Best Practices 1. **Incremental Migration**: Don't migrate everything at once 2. **Test Thoroughly**: Comprehensive testing at each step 3. **Use Codemods**: Automate repetitive transformations 4. **Start Simple**: Begin with leaf components 5. **Leverage StrictMode**: Catch issues early 6. **Monitor Performance**: Measure before and after 7. **Document Changes**: Keep migration log ## Common Pitfalls - Forgetting useEffect dependencies - Over-using useMemo/useCallback - Not handling cleanup in useEffect - Mixing class and functional patterns - Ignoring StrictMode warnings - Breaking change assumptions
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πŸ€– Auto-discovered
πŸ€–system promptβ€’7 months ago

react-native-architecture

Build production React Native apps with Expo, navigation, native

coding
⭐1
# React Native Architecture Production-ready patterns for React Native development with Expo, including navigation, state management, native modules, and offline-first architecture. ## When to Use This Skill - Starting a new React Native or Expo project - Implementing complex navigation patterns - Integrating native modules and platform APIs - Building offline-first mobile applications - Optimizing React Native performance - Setting up CI/CD for mobile releases ## Core Concepts ### 1. Project Structure ``` src/ β”œβ”€β”€ app/ # Expo Router screens β”‚ β”œβ”€β”€ (auth)/ # Auth group β”‚ β”œβ”€β”€ (tabs)/ # Tab navigation β”‚ └── _layout.tsx # Root layout β”œβ”€β”€ components/ β”‚ β”œβ”€β”€ ui/ # Reusable UI components β”‚ └── features/ # Feature-specific components β”œβ”€β”€ hooks/ # Custom hooks β”œβ”€β”€ services/ # API and native services β”œβ”€β”€ stores/ # State management β”œβ”€β”€ utils/ # Utilities └── types/ # TypeScript types ``` ### 2. Expo vs Bare React Native | Feature | Expo | Bare RN | | ------------------ | -------------- | -------------- | | Setup complexity | Low | High | | Native modules | EAS Build | Manual linking | | OTA updates | Built-in | Manual setup | | Build service | EAS | Custom CI | | Custom native code | Config plugins | Direct access | ## Quick Start ```bash # Create new Expo project npx create-expo-app@latest my-app -t expo-template-blank-typescript # Install essential dependencies npx expo install expo-router expo-status-bar react-native-safe-area-context npx expo install @react-native-async-storage/async-storage npx expo install expo-secure-store expo-haptics ``` ```typescript // app/_layout.tsx import { Stack } from 'expo-router' import { ThemeProvider } from '@/providers/ThemeProvider' import { QueryProvider } from '@/providers/QueryProvider' export default function RootLayout() { return ( <QueryProvider> <ThemeProvider> <Stack screenOptions={{ headerShown: false }}> <Stack.Screen name="(tabs)" /> <Stack.Screen name="(auth)" /> <Stack.Screen name="modal" options={{ presentation: 'modal' }} /> </Stack> </ThemeProvider> </QueryProvider> ) } ``` ## Patterns ### Pattern 1: Expo Router Navigation ```typescript // app/(tabs)/_layout.tsx import { Tabs } from 'expo-router' import { Home, Search, User, Settings } from 'lucide-react-native' import { useTheme } from '@/hooks/useTheme' export default function TabLayout() { const { colors } = useTheme() return ( <Tabs screenOptions={{ tabBarActiveTintColor: colors.primary, tabBarInactiveTintColor: colors.textMuted, tabBarStyle: { backgroundColor: colors.background }, headerShown: false, }} > <Tabs.Screen name="index" options={{ title: 'Home', tabBarIcon: ({ color, size }) => <Home size={size} color={color} />, }} /> <Tabs.Screen name="search" options={{ title: 'Search', tabBarIcon: ({ color, size }) => <Search size={size} color={color} />, }} /> <Tabs.Screen name="profile" options={{ title: 'Profile', tabBarIcon: ({ color, size }) => <User size={size} color={color} />, }} /> <Tabs.Screen name="settings" options={{ title: 'Settings', tabBarIcon: ({ color, size }) => <Settings size={size} color={color} />, }} /> </Tabs> ) } // app/(tabs)/profile/[id].tsx - Dynamic route import { useLocalSearchParams } from 'expo-router' export default function ProfileScreen() { const { id } = useLocalSearchParams<{ id: string }>() return <UserProfile userId={id} /> } // Navigation from anywhere import { router } from 'expo-router' // Programmatic navigation router.push('/profile/123') router.replace('/login') router.back() // With params router.push({ pathname: '/product/[id]', params: { id: '123', referrer: 'home' }, }) ``` ### Pattern 2: Authentication Flow ```typescript // providers/AuthProvider.tsx import { createContext, useContext, useEffect, useState } from 'react' import { useRouter, useSegments } from 'expo-router' import * as SecureStore from 'expo-secure-store' interface AuthContextType { user: User | null isLoading: boolean signIn: (credentials: Credentials) => Promise<void> signOut: () => Promise<void> } const AuthContext = createContext<AuthContextType | null>(null) export function AuthProvider({ children }: { children: React.ReactNode }) { const [user, setUser] = useState<User | null>(null) const [isLoading, setIsLoading] = useState(true) const segments = useSegments() const router = useRouter() // Check authentication on mount useEffect(() => { checkAuth() }, []) // Protect routes useEffect(() => { if (isLoading) return const inAuthGroup = segments[0] === '(auth)' if (!user && !inAuthGroup) { router.replace('/login') } else if (user && inAuthGroup) { router.replace('/(tabs)') } }, [user, segments, isLoading]) async function checkAuth() { try { const token = await SecureStore.getItemAsync('authToken') if (token) { const userData = await api.getUser(token) setUser(userData) } } catch (error) { await SecureStore.deleteItemAsync('authToken') } finally { setIsLoading(false) } } async function signIn(credentials: Credentials) { const { token, user } = await api.login(credentials) await SecureStore.setItemAsync('authToken', token) setUser(user) } async function signOut() { await SecureStore.deleteItemAsync('authToken') setUser(null) } if (isLoading) { return <SplashScreen /> } return ( <AuthContext.Provider value={{ user, isLoading, signIn, signOut }}> {children} </AuthContext.Provider> ) } export const useAuth = () => { const context = useContext(AuthContext) if (!context) throw new Error('useAuth must be used within AuthProvider') return context } ``` ### Pattern 3: Offline-First with React Query ```typescript // providers/QueryProvider.tsx import { QueryClient, QueryClientProvider } from '@tanstack/react-query' import { createAsyncStoragePersister } from '@tanstack/query-async-storage-persister' import { PersistQueryClientProvider } from '@tanstack/react-query-persist-client' import AsyncStorage from '@react-native-async-storage/async-storage' import NetInfo from '@react-native-community/netinfo' import { onlineManager } from '@tanstack/react-query' // Sync online status onlineManager.setEventListener((setOnline) => { return NetInfo.addEventListener((state) => { setOnline(!!state.isConnected) }) }) const queryClient = new QueryClient({ defaultOptions: { queries: { gcTime: 1000 * 60 * 60 * 24, // 24 hours staleTime: 1000 * 60 * 5, // 5 minutes retry: 2, networkMode: 'offlineFirst', }, mutations: { networkMode: 'offlineFirst', }, }, }) const asyncStoragePersister = createAsyncStoragePersister({ storage: AsyncStorage, key: 'REACT_QUERY_OFFLINE_CACHE', }) export function QueryProvider({ children }: { children: React.ReactNode }) { return ( <PersistQueryClientProvider client={queryClient} persistOptions={{ persister: asyncStoragePersister }} > {children} </PersistQueryClientProvider> ) } // hooks/useProducts.ts import { useQuery, useMutation, useQueryClient } from '@tanstack/react-query' export function useProducts() { return useQuery({ queryKey: ['products'], queryFn: api.getProducts, // Use stale data while revalidating placeholderData: (previousData) => previousData, }) } export function useCreateProduct() { const queryClient = useQueryClient() return useMutation({ mutationFn: api.createProduct, // Optimistic update onMutate: async (newProduct) => { await queryClient.cancelQueries({ queryKey: ['products'] }) const previous = queryClient.getQueryData(['products']) queryClient.setQueryData(['products'], (old: Product[]) => [ ...old, { ...newProduct, id: 'temp-' + Date.now() }, ]) return { previous } }, onError: (err, newProduct, context) => { queryClient.setQueryData(['products'], context?.previous) }, onSettled: () => { queryClient.invalidateQueries({ queryKey: ['products'] }) }, }) } ``` ### Pattern 4: Native Module Integration ```typescript // services/haptics.ts import * as Haptics from "expo-haptics"; import { Platform } from "react-native"; export const haptics = { light: () => { if (Platform.OS !== "web") { Haptics.impactAsync(Haptics.ImpactFeedbackStyle.Light); } }, medium: () => { if (Platform.OS !== "web") { Haptics.impactAsync(Haptics.ImpactFeedbackStyle.Medium); } }, heavy: () => { if (Platform.OS !== "web") { Haptics.impactAsync(Haptics.ImpactFeedbackStyle.Heavy); } }, success: () => { if (Platform.OS !== "web") { Haptics.notificationAsync(Haptics.NotificationFeedbackType.Success); } }, error: () => { if (Platform.OS !== "web") { Haptics.notificationAsync(Haptics.NotificationFeedbackType.Error); } }, }; // services/biometrics.ts import * as LocalAuthentication from "expo-local-authentication"; export async function authenticateWithBiometrics(): Promise<boolean> { const hasHardware = await LocalAuthentication.hasHardwareAsync(); if (!hasHardware) return false; const isEnrolled = await LocalAuthentication.isEnrolledAsync(); if (!isEnrolled) return false; const result = await LocalAuthentication.authenticateAsync({ promptMessage: "Authenticate to continue", fallbackLabel: "Use passcode", disableDeviceFallback: false, }); return result.success; } // services/notifications.ts import * as Notifications from "expo-notifications"; import { Platform } from "react-native"; import Constants from "expo-constants"; Notifications.setNotificationHandler({ handleNotification: async () => ({ shouldShowAlert: true, shouldPlaySound: true, shouldSetBadge: true, }), }); export async function registerForPushNotifications() { let token: string | undefined; if (Platform.OS === "android") { await Notifications.setNotificationChannelAsync("default", { name: "default", importance: Notifications.AndroidImportance.MAX, vibrationPattern: [0, 250, 250, 250], }); } const { status: existingStatus } = await Notifications.getPermissionsAsync(); let finalStatus = existingStatus; if (existingStatus !== "granted") { const { status } = await Notifications.requestPermissionsAsync(); finalStatus = status; } if (finalStatus !== "granted") { return null; } const projectId = Constants.expoConfig?.extra?.eas?.projectId; token = (await Notifications.getExpoPushTokenAsync({ projectId })).data; return token; } ``` ### Pattern 5: Platform-Specific Code ```typescript // components/ui/Button.tsx import { Platform, Pressable, StyleSheet, Text, ViewStyle } from 'react-native' import * as Haptics from 'expo-haptics' import Animated, { useAnimatedStyle, useSharedValue, withSpring, } from 'react-native-reanimated' const AnimatedPressable = Animated.createAnimatedComponent(Pressable) interface ButtonProps { title: string onPress: () => void variant?: 'primary' | 'secondary' | 'outline' disabled?: boolean } export function Button({ title, onPress, variant = 'primary', disabled = false, }: ButtonProps) { const scale = useSharedValue(1) const animatedStyle = useAnimatedStyle(() => ({ transform: [{ scale: scale.value }], })) const handlePressIn = () => { scale.value = withSpring(0.95) if (Platform.OS !== 'web') { Haptics.impactAsync(Haptics.ImpactFeedbackStyle.Light) } } const handlePressOut = () => { scale.value = withSpring(1) } return ( <AnimatedPressable onPress={onPress} onPressIn={handlePressIn} onPressOut={handlePressOut} disabled={disabled} style={[ styles.button, styles[variant], disabled && styles.disabled, animatedStyle, ]} > <Text style={[styles.text, styles[`${variant}Text`]]}>{title}</Text> </AnimatedPressable> ) } // Platform-specific files // Button.ios.tsx - iOS-specific implementation // Button.android.tsx - Android-specific implementation // Button.web.tsx - Web-specific implementation // Or use Platform.select const styles = StyleSheet.create({ button: { paddingVertical: 12, paddingHorizontal: 24, borderRadius: 8, alignItems: 'center', ...Platform.select({ ios: { shadowColor: '#000', shadowOffset: { width: 0, height: 2 }, shadowOpacity: 0.1, shadowRadius: 4, }, android: { elevation: 4, }, }), }, primary: { backgroundColor: '#007AFF', }, secondary: { backgroundColor: '#5856D6', }, outline: { backgroundColor: 'transparent', borderWidth: 1, borderColor: '#007AFF', }, disabled: { opacity: 0.5, }, text: { fontSize: 16, fontWeight: '600', }, primaryText: { color: '#FFFFFF', }, secondaryText: { color: '#FFFFFF', }, outlineText: { color: '#007AFF', }, }) ``` ### Pattern 6: Performance Optimization ```typescript // components/ProductList.tsx import { FlashList } from '@shopify/flash-list' import { memo, useCallback } from 'react' interface ProductListProps { products: Product[] onProductPress: (id: string) => void } // Memoize list item const ProductItem = memo(function ProductItem({ item, onPress, }: { item: Product onPress: (id: string) => void }) { const handlePress = useCallback(() => onPress(item.id), [item.id, onPress]) return ( <Pressable onPress={handlePress} style={styles.item}> <FastImage source={{ uri: item.image }} style={styles.image} resizeMode="cover" /> <Text style={styles.title}>{item.name}</Text> <Text style={styles.price}>${item.price}</Text> </Pressable> ) }) export function ProductList({ products, onProductPress }: ProductListProps) { const renderItem = useCallback( ({ item }: { item: Product }) => ( <ProductItem item={item} onPress={onProductPress} /> ), [onProductPress] ) const keyExtractor = useCallback((item: Product) => item.id, []) return ( <FlashList data={products} renderItem={renderItem} keyExtractor={keyExtractor} estimatedItemSize={100} // Performance optimizations removeClippedSubviews={true} maxToRenderPerBatch={10} windowSize={5} // Pull to refresh onRefresh={onRefresh} refreshing={isRefreshing} /> ) } ``` ## EAS Build & Submit ```json // eas.json { "cli": { "version": ">= 5.0.0" }, "build": { "development": { "developmentClient": true, "distribution": "internal", "ios": { "simulator": true } }, "preview": { "distribution": "internal", "android": { "buildType": "apk" } }, "production": { "autoIncrement": true } }, "submit": { "production": { "ios": { "appleId": "your@email.com", "ascAppId": "123456789" }, "android": { "serviceAccountKeyPath": "./google-services.json" } } } } ``` ```bash # Build commands eas build --platform ios --profile development eas build --platform android --profile preview eas build --platform all --profile production # Submit to stores eas submit --platform ios eas submit --platform android # OTA updates eas update --branch production --message "Bug fixes" ``` ## Best Practices ### Do's - **Use Expo** - Faster development, OTA updates, managed native code - **FlashList over FlatList** - Better performance for long lists - **Memoize components** - Prevent unnecessary re-renders - **Use Reanimated** - 60fps animations on native thread - **Test on real devices** - Simulators miss real-world issues ### Don'ts - **Don't inline styles** - Use StyleSheet.create for performance - **Don't fetch in render** - Use useEffect or React Query - **Don't ignore platform differences** - Test on both iOS and Android - **Don't store secrets in code** - Use environment variables - **Don't skip error boundaries** - Mobile crashes are unforgiving ## Resources - [Expo Documentation](https://docs.expo.dev/) - [Expo Router](https://docs.expo.dev/router/introduction/) - [React Native Performance](https://reactnative.dev/docs/performance) - [FlashList](https://shopify.github.io/flash-list/)
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dotnet-backend-patterns

Master C#/.NET backend development patterns for building robust

coding
⭐1
# .NET Backend Development Patterns Master C#/.NET patterns for building production-grade APIs, MCP servers, and enterprise backends with modern best practices (2024/2025). ## When to Use This Skill - Developing new .NET Web APIs or MCP servers - Reviewing C# code for quality and performance - Designing service architectures with dependency injection - Implementing caching strategies with Redis - Writing unit and integration tests - Optimizing database access with EF Core or Dapper - Configuring applications with IOptions pattern - Handling errors and implementing resilience patterns ## Core Concepts ### 1. Project Structure (Clean Architecture) ``` src/ β”œβ”€β”€ Domain/ # Core business logic (no dependencies) β”‚ β”œβ”€β”€ Entities/ β”‚ β”œβ”€β”€ Interfaces/ β”‚ β”œβ”€β”€ Exceptions/ β”‚ └── ValueObjects/ β”œβ”€β”€ Application/ # Use cases, DTOs, validation β”‚ β”œβ”€β”€ Services/ β”‚ β”œβ”€β”€ DTOs/ β”‚ β”œβ”€β”€ Validators/ β”‚ └── Interfaces/ β”œβ”€β”€ Infrastructure/ # External implementations β”‚ β”œβ”€β”€ Data/ # EF Core, Dapper repositories β”‚ β”œβ”€β”€ Caching/ # Redis, Memory cache β”‚ β”œβ”€β”€ External/ # HTTP clients, third-party APIs β”‚ └── DependencyInjection/ # Service registration └── Api/ # Entry point β”œβ”€β”€ Controllers/ # Or MinimalAPI endpoints β”œβ”€β”€ Middleware/ β”œβ”€β”€ Filters/ └── Program.cs ``` ### 2. Dependency Injection Patterns ```csharp // Service registration by lifetime public static class ServiceCollectionExtensions { public static IServiceCollection AddApplicationServices( this IServiceCollection services, IConfiguration configuration) { // Scoped: One instance per HTTP request services.AddScoped<IProductService, ProductService>(); services.AddScoped<IOrderService, OrderService>(); // Singleton: One instance for app lifetime services.AddSingleton<ICacheService, RedisCacheService>(); services.AddSingleton<IConnectionMultiplexer>(_ => ConnectionMultiplexer.Connect(configuration["Redis:Connection"]!)); // Transient: New instance every time services.AddTransient<IValidator<CreateOrderRequest>, CreateOrderValidator>(); // Options pattern for configuration services.Configure<CatalogOptions>(configuration.GetSection("Catalog")); services.Configure<RedisOptions>(configuration.GetSection("Redis")); // Factory pattern for conditional creation services.AddScoped<IPriceCalculator>(sp => { var options = sp.GetRequiredService<IOptions<PricingOptions>>().Value; return options.UseNewEngine ? sp.GetRequiredService<NewPriceCalculator>() : sp.GetRequiredService<LegacyPriceCalculator>(); }); // Keyed services (.NET 8+) services.AddKeyedScoped<IPaymentProcessor, StripeProcessor>("stripe"); services.AddKeyedScoped<IPaymentProcessor, PayPalProcessor>("paypal"); return services; } } // Usage with keyed services public class CheckoutService { public CheckoutService( [FromKeyedServices("stripe")] IPaymentProcessor stripeProcessor) { _processor = stripeProcessor; } } ``` ### 3. Async/Await Patterns ```csharp // βœ… CORRECT: Async all the way down public async Task<Product> GetProductAsync(string id, CancellationToken ct = default) { return await _repository.GetByIdAsync(id, ct); } // βœ… CORRECT: Parallel execution with WhenAll public async Task<(Stock, Price)> GetStockAndPriceAsync( string productId, CancellationToken ct = default) { var stockTask = _stockService.GetAsync(productId, ct); var priceTask = _priceService.GetAsync(productId, ct); await Task.WhenAll(stockTask, priceTask); return (await stockTask, await priceTask); } // βœ… CORRECT: ConfigureAwait in libraries public async Task<T> LibraryMethodAsync<T>(CancellationToken ct = default) { var result = await _httpClient.GetAsync(url, ct).ConfigureAwait(false); return await result.Content.ReadFromJsonAsync<T>(ct).ConfigureAwait(false); } // βœ… CORRECT: ValueTask for hot paths with caching public ValueTask<Product?> GetCachedProductAsync(string id) { if (_cache.TryGetValue(id, out Product? product)) return ValueTask.FromResult(product); return new ValueTask<Product?>(GetFromDatabaseAsync(id)); } // ❌ WRONG: Blocking on async (deadlock risk) var result = GetProductAsync(id).Result; // NEVER do this var result2 = GetProductAsync(id).GetAwaiter().GetResult(); // Also bad // ❌ WRONG: async void (except event handlers) public async void ProcessOrder() { } // Exceptions are lost // ❌ WRONG: Unnecessary Task.Run for already async code await Task.Run(async () => await GetDataAsync()); // Wastes thread ``` ### 4. Configuration with IOptions ```csharp // Configuration classes public class CatalogOptions { public const string SectionName = "Catalog"; public int DefaultPageSize { get; set; } = 50; public int MaxPageSize { get; set; } = 200; public TimeSpan CacheDuration { get; set; } = TimeSpan.FromMinutes(15); public bool EnableEnrichment { get; set; } = true; } public class RedisOptions { public const string SectionName = "Redis"; public string Connection { get; set; } = "localhost:6379"; public string KeyPrefix { get; set; } = "mcp:"; public int Database { get; set; } = 0; } // appsettings.json { "Catalog": { "DefaultPageSize": 50, "MaxPageSize": 200, "CacheDuration": "00:15:00", "EnableEnrichment": true }, "Redis": { "Connection": "localhost:6379", "KeyPrefix": "mcp:", "Database": 0 } } // Registration services.Configure<CatalogOptions>(configuration.GetSection(CatalogOptions.SectionName)); services.Configure<RedisOptions>(configuration.GetSection(RedisOptions.SectionName)); // Usage with IOptions (singleton, read once at startup) public class CatalogService { private readonly CatalogOptions _options; public CatalogService(IOptions<CatalogOptions> options) { _options = options.Value; } } // Usage with IOptionsSnapshot (scoped, re-reads on each request) public class DynamicService { private readonly CatalogOptions _options; public DynamicService(IOptionsSnapshot<CatalogOptions> options) { _options = options.Value; // Fresh value per request } } // Usage with IOptionsMonitor (singleton, notified on changes) public class MonitoredService { private CatalogOptions _options; public MonitoredService(IOptionsMonitor<CatalogOptions> monitor) { _options = monitor.CurrentValue; monitor.OnChange(newOptions => _options = newOptions); } } ``` ### 5. Result Pattern (Avoiding Exceptions for Flow Control) ```csharp // Generic Result type public class Result<T> { public bool IsSuccess { get; } public T? Value { get; } public string? Error { get; } public string? ErrorCode { get; } private Result(bool isSuccess, T? value, string? error, string? errorCode) { IsSuccess = isSuccess; Value = value; Error = error; ErrorCode = errorCode; } public static Result<T> Success(T value) => new(true, value, null, null); public static Result<T> Failure(string error, string? code = null) => new(false, default, error, code); public Result<TNew> Map<TNew>(Func<T, TNew> mapper) => IsSuccess ? Result<TNew>.Success(mapper(Value!)) : Result<TNew>.Failure(Error!, ErrorCode); public async Task<Result<TNew>> MapAsync<TNew>(Func<T, Task<TNew>> mapper) => IsSuccess ? Result<TNew>.Success(await mapper(Value!)) : Result<TNew>.Failure(Error!, ErrorCode); } // Usage in service public async Task<Result<Order>> CreateOrderAsync(CreateOrderRequest request, CancellationToken ct) { // Validation var validation = await _validator.ValidateAsync(request, ct); if (!validation.IsValid) return Result<Order>.Failure( validation.Errors.First().ErrorMessage, "VALIDATION_ERROR"); // Business rule check var stock = await _stockService.CheckAsync(request.ProductId, request.Quantity, ct); if (!stock.IsAvailable) return Result<Order>.Failure( $"Insufficient stock: {stock.Available} available, {request.Quantity} requested", "INSUFFICIENT_STOCK"); // Create order var order = await _repository.CreateAsync(request.ToEntity(), ct); return Result<Order>.Success(order); } // Usage in controller/endpoint app.MapPost("/orders", async ( CreateOrderRequest request, IOrderService orderService, CancellationToken ct) => { var result = await orderService.CreateOrderAsync(request, ct); return result.IsSuccess ? Results.Created($"/orders/{result.Value!.Id}", result.Value) : Results.BadRequest(new { error = result.Error, code = result.ErrorCode }); }); ``` ## Data Access Patterns ### Entity Framework Core ```csharp // DbContext configuration public class AppDbContext : DbContext { public DbSet<Product> Products => Set<Product>(); public DbSet<Order> Orders => Set<Order>(); protected override void OnModelCreating(ModelBuilder modelBuilder) { // Apply all configurations from assembly modelBuilder.ApplyConfigurationsFromAssembly(typeof(AppDbContext).Assembly); // Global query filters modelBuilder.Entity<Product>().HasQueryFilter(p => !p.IsDeleted); } } // Entity configuration public class ProductConfiguration : IEntityTypeConfiguration<Product> { public void Configure(EntityTypeBuilder<Product> builder) { builder.ToTable("Products"); builder.HasKey(p => p.Id); builder.Property(p => p.Id).HasMaxLength(40); builder.Property(p => p.Name).HasMaxLength(200).IsRequired(); builder.Property(p => p.Price).HasPrecision(18, 2); builder.HasIndex(p => p.Sku).IsUnique(); builder.HasIndex(p => new { p.CategoryId, p.Name }); builder.HasMany(p => p.OrderItems) .WithOne(oi => oi.Product) .HasForeignKey(oi => oi.ProductId); } } // Repository with EF Core public class ProductRepository : IProductRepository { private readonly AppDbContext _context; public async Task<Product?> GetByIdAsync(string id, CancellationToken ct = default) { return await _context.Products .AsNoTracking() .FirstOrDefaultAsync(p => p.Id == id, ct); } public async Task<IReadOnlyList<Product>> SearchAsync( ProductSearchCriteria criteria, CancellationToken ct = default) { var query = _context.Products.AsNoTracking(); if (!string.IsNullOrWhiteSpace(criteria.SearchTerm)) query = query.Where(p => EF.Functions.Like(p.Name, $"%{criteria.SearchTerm}%")); if (criteria.CategoryId.HasValue) query = query.Where(p => p.CategoryId == criteria.CategoryId); if (criteria.MinPrice.HasValue) query = query.Where(p => p.Price >= criteria.MinPrice); if (criteria.MaxPrice.HasValue) query = query.Where(p => p.Price <= criteria.MaxPrice); return await query .OrderBy(p => p.Name) .Skip((criteria.Page - 1) * criteria.PageSize) .Take(criteria.PageSize) .ToListAsync(ct); } } ``` ### Dapper for Performance ```csharp public class DapperProductRepository : IProductRepository { private readonly IDbConnection _connection; public async Task<Product?> GetByIdAsync(string id, CancellationToken ct = default) { const string sql = """ SELECT Id, Name, Sku, Price, CategoryId, Stock, CreatedAt FROM Products WHERE Id = @Id AND IsDeleted = 0 """; return await _connection.QueryFirstOrDefaultAsync<Product>( new CommandDefinition(sql, new { Id = id }, cancellationToken: ct)); } public async Task<IReadOnlyList<Product>> SearchAsync( ProductSearchCriteria criteria, CancellationToken ct = default) { var sql = new StringBuilder(""" SELECT Id, Name, Sku, Price, CategoryId, Stock, CreatedAt FROM Products WHERE IsDeleted = 0 """); var parameters = new DynamicParameters(); if (!string.IsNullOrWhiteSpace(criteria.SearchTerm)) { sql.Append(" AND Name LIKE @SearchTerm"); parameters.Add("SearchTerm", $"%{criteria.SearchTerm}%"); } if (criteria.CategoryId.HasValue) { sql.Append(" AND CategoryId = @CategoryId"); parameters.Add("CategoryId", criteria.CategoryId); } if (criteria.MinPrice.HasValue) { sql.Append(" AND Price >= @MinPrice"); parameters.Add("MinPrice", criteria.MinPrice); } if (criteria.MaxPrice.HasValue) { sql.Append(" AND Price <= @MaxPrice"); parameters.Add("MaxPrice", criteria.MaxPrice); } sql.Append(" ORDER BY Name OFFSET @Offset ROWS FETCH NEXT @PageSize ROWS ONLY"); parameters.Add("Offset", (criteria.Page - 1) * criteria.PageSize); parameters.Add("PageSize", criteria.PageSize); var results = await _connection.QueryAsync<Product>( new CommandDefinition(sql.ToString(), parameters, cancellationToken: ct)); return results.ToList(); } // Multi-mapping for related data public async Task<Order?> GetOrderWithItemsAsync(int orderId, CancellationToken ct = default) { const string sql = """ SELECT o.*, oi.*, p.* FROM Orders o LEFT JOIN OrderItems oi ON o.Id = oi.OrderId LEFT JOIN Products p ON oi.ProductId = p.Id WHERE o.Id = @OrderId """; var orderDictionary = new Dictionary<int, Order>(); await _connection.QueryAsync<Order, OrderItem, Product, Order>( new CommandDefinition(sql, new { OrderId = orderId }, cancellationToken: ct), (order, item, product) => { if (!orderDictionary.TryGetValue(order.Id, out var existingOrder)) { existingOrder = order; existingOrder.Items = new List<OrderItem>(); orderDictionary.Add(order.Id, existingOrder); } if (item != null) { item.Product = product; existingOrder.Items.Add(item); } return existingOrder; }, splitOn: "Id,Id"); return orderDictionary.Values.FirstOrDefault(); } } ``` ## Caching Patterns ### Multi-Level Cache with Redis ```csharp public class CachedProductService : IProductService { private readonly IProductRepository _repository; private readonly IMemoryCache _memoryCache; private readonly IDistributedCache _distributedCache; private readonly ILogger<CachedProductService> _logger; private static readonly TimeSpan MemoryCacheDuration = TimeSpan.FromMinutes(1); private static readonly TimeSpan DistributedCacheDuration = TimeSpan.FromMinutes(15); public async Task<Product?> GetByIdAsync(string id, CancellationToken ct = default) { var cacheKey = $"product:{id}"; // L1: Memory cache (in-process, fastest) if (_memoryCache.TryGetValue(cacheKey, out Product? cached)) { _logger.LogDebug("L1 cache hit for {CacheKey}", cacheKey); return cached; } // L2: Distributed cache (Redis) var distributed = await _distributedCache.GetStringAsync(cacheKey, ct); if (distributed != null) { _logger.LogDebug("L2 cache hit for {CacheKey}", cacheKey); var product = JsonSerializer.Deserialize<Product>(distributed); // Populate L1 _memoryCache.Set(cacheKey, product, MemoryCacheDuration); return product; } // L3: Database _logger.LogDebug("Cache miss for {CacheKey}, fetching from database", cacheKey); var fromDb = await _repository.GetByIdAsync(id, ct); if (fromDb != null) { var serialized = JsonSerializer.Serialize(fromDb); // Populate both caches await _distributedCache.SetStringAsync( cacheKey, serialized, new DistributedCacheEntryOptions { AbsoluteExpirationRelativeToNow = DistributedCacheDuration }, ct); _memoryCache.Set(cacheKey, fromDb, MemoryCacheDuration); } return fromDb; } public async Task InvalidateAsync(string id, CancellationToken ct = default) { var cacheKey = $"product:{id}"; _memoryCache.Remove(cacheKey); await _distributedCache.RemoveAsync(cacheKey, ct); _logger.LogInformation("Invalidated cache for {CacheKey}", cacheKey); } } // Stale-while-revalidate pattern public class StaleWhileRevalidateCache<T> { private readonly IDistributedCache _cache; private readonly TimeSpan _freshDuration; private readonly TimeSpan _staleDuration; public async Task<T?> GetOrCreateAsync( string key, Func<CancellationToken, Task<T>> factory, CancellationToken ct = default) { var cached = await _cache.GetStringAsync(key, ct); if (cached != null) { var entry = JsonSerializer.Deserialize<CacheEntry<T>>(cached)!; if (entry.IsStale && !entry.IsExpired) { // Return stale data immediately, refresh in background _ = Task.Run(async () => { var fresh = await factory(CancellationToken.None); await SetAsync(key, fresh, CancellationToken.None); }); } if (!entry.IsExpired) return entry.Value; } // Cache miss or expired var value = await factory(ct); await SetAsync(key, value, ct); return value; } private record CacheEntry<TValue>(TValue Value, DateTime CreatedAt) { public bool IsStale => DateTime.UtcNow - CreatedAt > _freshDuration; public bool IsExpired => DateTime.UtcNow - CreatedAt > _staleDuration; } } ``` ## Testing Patterns ### Unit Tests with xUnit and Moq ```csharp public class OrderServiceTests { private readonly Mock<IOrderRepository> _mockRepository; private readonly Mock<IStockService> _mockStockService; private readonly Mock<IValidator<CreateOrderRequest>> _mockValidator; private readonly OrderService _sut; // System Under Test public OrderServiceTests() { _mockRepository = new Mock<IOrderRepository>(); _mockStockService = new Mock<IStockService>(); _mockValidator = new Mock<IValidator<CreateOrderRequest>>(); // Default: validation passes _mockValidator .Setup(v => v.ValidateAsync(It.IsAny<CreateOrderRequest>(), It.IsAny<
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