Skip to main content
EVOKORE// BROWSE
>

./browse/prompts

23 NODES
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`.
πŸ‘0
πŸ‘οΈ14
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 ...
πŸ‘0
πŸ‘οΈ0
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 ...
πŸ‘0
πŸ‘οΈ0
docs
πŸ€–system promptβ€’6 months ago

Hook Observability and Session Replay

Instrument hooks with replayable logs, task-state visibility, and non-blocking observability around agent sessions.

devops
⭐1
# Hook Observability and Session Replay Imported from curated first-party documentation sources. ## What this covers Use this skill when you need to understand what hooks fired, what they emitted, and how a session can be replayed after the fact. ## Use this when - Debugging hook-driven automation - Replaying session events after failures - Adding observability without blocking interactive work ## Expected outcomes - Hook activity becomes inspectable and replayable - Operational state survives beyond a single terminal session - Observability stays useful without overwhelming operators ## Source synthesis - EVOKORE-MCP/docs/VOICE_AND_HOOKS.md (https://github.com/mattmre/EVOKORE-MCP/blob/main/docs/VOICE_AND_HOOKS.md) - EVOKORE-MCP/docs/USE_CASES_AND_WALKTHROUGHS.md (https://github.com/mattmre/EVOKORE-MCP/blob/main/docs/USE_CASES_AND_WALKTHROUGHS.md) ## Dedupe notes Focuses on replay and observability instead of importing the broader voice-sidecar guide verbatim. ## Source excerpts ### EVOKORE-MCP/docs/VOICE_AND_HOOKS.md EVOKORE currently has three separate voice-related systems plus a set of hook and observability utilities. They overlap in operator workflows, but they are not the same runtime. ## The three voice-related systems ### 1. ElevenLabs MCP proxy This is the optional `elevenlabs` child server configured in `mcp.config.json`. What it is: - proxied through the EVOKORE router - exposed as prefixed MCP tools - available to any EVOKORE-connected MCP client when configured successfully What it is for: - text-to-speech and other ElevenLabs MCP operations as tools - routing voice-related actions through the standard EVOKORE proxy/security stack Requirements: - `uvx` available on PATH - `ELEVENLABS_API_KEY` set ### 2. VoiceMode VoiceMode is a separate voice-conversation system for Claude Code. What it is: - registered separately from EVOKORE - not routed through EVOKOREÒ€ℒs stdio server - used for bidirectional voice conversation in Claude Code What it is for: - speaking to Claude and hearing spoken responses - using `OPENAI_API_KEY` and VoiceModeÒ€ℒs own runtime Windows note: - VoiceMode relies on `uvx` being directly available - set `OPENAI_API_KEY` in the shell that launches Claude Code ### 3. VoiceSidecar VoiceSidecar is a standalone WebSocket server implemented in `src/Voice ... ### EVOKORE-MCP/docs/USE_CASES_AND_WALKTHROUGHS.md This guide turns the runtime contracts into practical operator flows. ## Walkthrough 1: Adopt a workflow from the skill library Use this when you want EVOKORE to retrieve process guidance before the model starts acting. ### Goal Find and adopt an existing workflow such as `session-wrap`. ### Steps 1. Ask the client to search skills: ```text Search the MCP for a workflow about session wrap-up and continuity. ``` 2. EVOKORE uses `search_skills` and returns matching skills. 3. Ask for a specific skill: ```text Show me help for the session-wrap skill. ``` 4. EVOKORE uses `get_skill_help` and returns the skillÒ€ℒs internal instructions. 5. For broader task matching, ask: ```text Resolve a workflow for wrapping this session, documenting open risks, and preparing the next handoff. ``` 6. EVOKORE uses `resolve_workflow` and injects the top 1-3 relevant workflows directly into the tool response. ### Why this matters - keeps the model grounded in repo-specific process - reduces prompt drift - makes handoff and governance behavior repeatable ## Walkthrough 2: Use a proxied tool that requires HITL approval Use this when the tool is configured as `require_approval` in `permissions.yml`. ### Goal Allow a protected proxied tool call such as `fs_write_f ...
πŸ‘0
πŸ‘οΈ0
docs
πŸ€–system promptβ€’6 months ago

Multi-Server MCP Aggregation Pattern

Aggregate tools across multiple MCP child servers with prefixing, collision avoidance, and routing rules that stay deterministic.

architecture
⭐1
# Multi-Server MCP Aggregation Pattern Imported from curated first-party documentation sources. ## What this covers Use this pattern when an MCP host must broker tools from multiple child servers without sacrificing clarity or control. ## Use this when - Combining tools from several MCP backends - Avoiding tool-name collisions across providers - Keeping origin and routing visible during execution ## Expected outcomes - Server-prefixed names make tool origins obvious - Collisions are avoided without brittle manual renaming - Operators can extend the tool surface without losing determinism ## Source synthesis - EVOKORE-MCP/docs/AGENT33_IMPROVEMENT_INSTRUCTIONS.md (https://github.com/mattmre/EVOKORE-MCP/blob/main/docs/AGENT33_IMPROVEMENT_INSTRUCTIONS.md) - EVOKORE-MCP/docs/TOOLS_AND_DISCOVERY.md (https://github.com/mattmre/EVOKORE-MCP/blob/main/docs/TOOLS_AND_DISCOVERY.md) ## Dedupe notes Uses the improvement-transfer doc as the primary source, with the discovery doc covering prefixing and compatibility details. ## Source excerpts ### 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 ... ### EVOKORE-MCP/docs/TOOLS_AND_DISCOVERY.md This page explains how EVOKORE presents tools, how proxy names are built, and how `discover_tools` changes the visible tool surface. ## Two tool populations ### Native EVOKORE tools These tools are defined by EVOKORE itself: - `docs_architect` - `skill_creator` - `resolve_workflow` - `search_skills` - `get_skill_help` - `discover_tools` Properties: - always available - always visible - not subject to proxy prefixing ### Proxied child-server tools These come from child servers in `mcp.config.json`. Current configured sources: - `github` - `fs` - optional `elevenlabs` Properties: - fetched from child servers at startup - renamed with server prefixes - governed by `permissions.yml` - routed through `ProxyManager` ## Prefixing and compatibility EVOKORE rewrites proxied tool names to: ```text ${serverId}_${tool.name} ``` Why this exists: - prevents tool-name collisions across child servers - makes origin obvious during execution and review - keeps exact-name routing deterministic Examples: | Upstream tool | EVOKORE-exposed tool | |---|---| | `read_file` from `fs` | `fs_read_file` | | `create_issue` from `github` | `github_create_issue` | ### Duplicate-prefixed name policy If two child registrations would create the same final prefixed name: - the first registrati ...
πŸ‘0
πŸ‘οΈ0
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 ...
πŸ‘0
πŸ‘οΈ0
docs
πŸ“textβ€’6 months ago

Docker-Backed Jupyter Kernel Operator

Operate containerized Jupyter kernels with enablement, smoke checks, cleanup, and failure handling baked into the runbook.

devops
⭐1
# Docker-Backed Jupyter Kernel Operator Imported from curated first-party documentation sources. ## What this covers Use this runbook when you need isolated notebook execution, clear container lifecycle management, and troubleshooting guidance. ## Use this when - Enabling containerized notebook workflows - Cleaning up kernel containers safely - Troubleshooting notebook execution environments ## Expected outcomes - Kernel startup and cleanup steps are documented end-to-end - Failure modes are easier to diagnose - Notebook execution can be repeated without manual guesswork ## Source synthesis - AGENT33/docs/runbooks/jupyter-kernel-containers.md (https://github.com/mattmre/AGENT33/blob/main/docs/runbooks/jupyter-kernel-containers.md) ## Dedupe notes Uses the AGENT33 Jupyter container runbook as a focused operations import without duplicating broader walkthroughs. ## Source excerpts ### AGENT33/docs/runbooks/jupyter-kernel-containers.md ## Purpose Operate the Docker-backed Jupyter kernel adapter introduced for Phase 38 Stage 3 / Phase 42 follow-on work. ## Enablement Set: - `JUPYTER_KERNEL_ENABLED=true` - `JUPYTER_KERNEL_MODE=docker` Optional settings: - `JUPYTER_KERNEL_DOCKER_IMAGE` - `JUPYTER_KERNEL_ALLOWED_IMAGES` - `JUPYTER_KERNEL_NETWORK_ENABLED` - `JUPYTER_KERNEL_MOUNT_WORKDIR` - `JUPYTER_KERNEL_CONTAINER_WORKDIR` ## Operational Notes - Docker mode publishes kernel ports to the host and mounts a per-session runtime directory containing the Jupyter connection file. - When `JUPYTER_KERNEL_NETWORK_ENABLED=false`, the adapter starts containers with `--network none`. - Working-directory mounting is opt-in and should only point at paths already approved by workflow / execution policy. - The adapter enforces an image allowlist when one is configured. ## Failure Modes - `jupyter_client not installed`: install with `pip install agent33[jupyter]` - `docker executable not found`: install Docker and ensure `docker` is on `PATH` - `Docker image ... is not permitted`: align the requested image with `JUPYTER_KERNEL_ALLOWED_IMAGES` - kernel startup timeout: inspect Docker logs for the session container and verify the image includes `ipykernel` ## Cleanup - One-shot sessions are removed after execution. - Stateful sessions are removed explicitly or via adapter shutdown. - Forced cleanup uses `docker rm -f <container>` and deletes the runtime connection directory. ## Quick Smoke Workflow Register a minimal workflow that exercises the Docker-backed `code-interpreter` tool: ```bash curl -X POST http://localhost:8000/v1/workflows/ \ -H "Authorization: Bearer $TOKEN" \ -H "Content-Type: application/json" \ -d '{ "name": "docker-kernel-smoke", "version": "1.0.0", "description": "Validate Docker-backed Jupyter execution", "triggers": {"manual": true}, "inputs": {}, "outputs": { "result": {"type": "object"} }, "steps": [ { "id": "run-notebook-code", "action": "execute-code", "inputs": { "tool_id": "code-interpreter", "language": "python", "code": "print(6 * 7)" } } ], "execution": {"mode": "sequential"} }' ``` Then execute it: ```bash curl -X POST http://localhost:8000/v1/workflows/docker-kernel-smoke/execute \ -H "Authorization: Bearer $TOKEN" \ -H "Content-Type: application/json" \ -d '{"inputs": {}}' ```
πŸ‘0
πŸ‘οΈ0
docs
πŸ“textβ€’6 months ago

Improvement Cycle Review Wizard

Run a live improvement-cycle workflow that links review artifacts, approvals, tool requests, and operator checkpoints.

productivity
⭐1
# Improvement Cycle Review Wizard Imported from curated first-party documentation sources. ## What this covers Use this workflow when an operator needs a guided improvement cycle with live status, explicit approvals, and linked artifacts. ## Use this when - Operational review sessions with live progress - Approval-heavy improvement cycles - Coordinating review artifacts with execution steps ## Expected outcomes - Live workflow execution remains visible to operators - Artifacts, approvals, and tool requests stay connected - Improvement loops become easier to audit and rerun ## Source synthesis - AGENT33/docs/phase25-26-live-review-walkthrough.md (https://github.com/mattmre/AGENT33/blob/main/docs/phase25-26-live-review-walkthrough.md) - AGENT33/docs/operator-improvement-cycle-and-jupyter.md (https://github.com/mattmre/AGENT33/blob/main/docs/operator-improvement-cycle-and-jupyter.md) ## Dedupe notes Merges AGENT33 live review walkthrough details with the shorter operator-focused improvement-cycle guide. ## Source excerpts ### AGENT33/docs/phase25-26-live-review-walkthrough.md This guide documents the operator flow introduced by the Phase 25 live workflow transport and the Phase 26/27 improvement-cycle review wizard stack. ## Scope - Live workflow execution with run-scoped graph refresh - WebSocket-first status streaming with authenticated SSE fallback - Improvement-cycle preset creation - Explanation artifact generation for `plan_review` and `diff_review` - Linked review creation, risk assessment, L1/L2 signoff, and final approval - Pending tool-approval triage inside the same workflow domain > Note > This walkthrough reflects the review stack built on `codex/session58-phase26-wizard` and validated in `codex/session60-phase22-docs-validation`. If the related PRs are still open, `main` may not yet expose every surface described here. ## Operator Flow ```mermaid sequenceDiagram participant UI as "Frontend Control Plane" participant WF as "Workflow APIs" participant VIZ as "Visualization APIs" participant EXP as "Explanation APIs" participant REV as "Review APIs" participant HITL as "Tool Approval APIs" UI->>WF: "POST /v1/workflows/{name}/execute (single mode, caller run_id)" UI->>VIZ: "GET /v1/visualizations/workflows/{workflow_id}/graph?run_id=..." UI->>WF: "WS /v1/workflows/{run_id}/ws" alt "WebSocket unav ... ### AGENT33/docs/operator-improvement-cycle-and-jupyter.md This guide covers the merged operator surfaces for: - the Phase 26 improvement-cycle review wizard - the Phase 27 canonical workflow presets - the Phase 38 Docker-backed Jupyter kernel workflow Use it when you want the shortest current path from UI entry point to a real workflow run. ## 1. Improvement-Cycle Wizard The wizard is mounted inside the frontend control plane under the `Workflows` domain. ### Entry path 1. Open the frontend at `http://localhost:3000` 2. Authenticate with a bearer token or API key 3. Open `Advanced Settings` 4. Select the `Workflows` domain 5. Use the `Improvement Cycle Wizard` panel at the top of the page ### What the wizard does The wizard stitches together the backend surfaces that previously had to be called manually: - plan review / diff review generation - review creation and risk assessment - L1 and L2 review submission - tool approval request review and decision capture Reference implementation: - frontend: `frontend/src/features/improvement-cycle/ImprovementCycleWizard.tsx` - tests: `frontend/src/features/improvement-cycle/ImprovementCycleWizard.test.tsx` For the detailed review flow, keep using: - [`phase25-26-live-review-walkthrough.md`](phase25-26-live-review-walkthrough.md) ## 2. Canonical Workflow Presets The `Workflows` doma ...
πŸ‘0
πŸ‘οΈ0
docs
πŸ“textβ€’6 months ago

Evaluation and Regression Gate

Create evaluation runs, submit results, compute metrics, save baselines, and triage regressions before shipping changes.

analysis
⭐1
# Evaluation and Regression Gate Imported from curated first-party documentation sources. ## What this covers Use this workflow to formalize benchmarking, regression detection, and baseline management around agent or product changes. ## Use this when - Validating changes against prior baselines - Making regressions visible before rollout - Capturing repeatable evaluation evidence ## Expected outcomes - Evaluation runs produce comparable metrics - Baselines are stored after successful validation - Regression triage becomes a workflow instead of an afterthought ## Source synthesis - AGENT33/docs/functionality-and-workflows.md (https://github.com/mattmre/AGENT33/blob/main/docs/functionality-and-workflows.md) - AGENT33/docs/use-cases.md (https://github.com/mattmre/AGENT33/blob/main/docs/use-cases.md) ## Dedupe notes Combines AGENT33 evaluation lifecycle definitions with evaluation and regression use-case framing. ## Source excerpts ### AGENT33/docs/functionality-and-workflows.md ### 4.3 Evaluation Lifecycle Flow: 1. Create run (`/v1/evaluations/runs`) 2. Submit task results (`/runs/{id}/results`) 3. Compute metrics + gate report 4. Save baseline (`/runs/{id}/baseline`) 5. Triage/resolve regressions ### AGENT33/docs/use-cases.md ## 4. Evaluation and Regression Gates Goal: - Quantify quality and block regressions across PR/merge/release gates. Use these modules: - `api/routes/evaluations.py` - `evaluation/service.py` - `evaluation/gates.py` - `evaluation/regression.py` Typical flow: 1. Create run for gate type (`G-PR`, `G-MRG`, `G-REL`, `G-MON`). 2. Submit task results and quality metadata. 3. Compute metrics and gate verdict. 4. Save baseline for future comparison. 5. Triage/resolve regression records. Best fit: - Teams with golden-task style quality gates.
πŸ‘0
πŸ‘οΈ0
docs
πŸ“textβ€’6 months ago

Autonomy Budget Lifecycle

Define, approve, enforce, and escalate autonomous execution budgets before agents act on files, commands, or network requests.

productivity
⭐1
# Autonomy Budget Lifecycle Imported from curated first-party documentation sources. ## What this covers Use this lifecycle when you need bounded autonomous execution with approvals, preflight checks, and runtime enforcement. ## Use this when - Running agents with scoped permissions - Escalating risky actions instead of silently proceeding - Adding governance to autonomous sessions ## Expected outcomes - Autonomy state moves through draft, approval, and active phases - Preflight checks happen before execution begins - Escalations are tracked instead of disappearing into logs ## Source synthesis - AGENT33/docs/functionality-and-workflows.md (https://github.com/mattmre/AGENT33/blob/main/docs/functionality-and-workflows.md) - AGENT33/docs/use-cases.md (https://github.com/mattmre/AGENT33/blob/main/docs/use-cases.md) ## Dedupe notes Collapses autonomy lifecycle and use-case material into a single execution-governance entry. ## Source excerpts ### AGENT33/docs/functionality-and-workflows.md ### 4.4 Autonomy Budget Lifecycle States: - `draft -> pending_approval -> active -> suspended|expired|completed` Flow: 1. Create budget 2. Activate or transition 3. Run preflight checks 4. Create enforcer 5. Evaluate command/file/network requests 6. Track escalations ### AGENT33/docs/use-cases.md ## 3. Autonomous Execution Budgeting Goal: - Enforce hard runtime limits for file, command, and network activity. Use these modules: - `api/routes/autonomy.py` - `autonomy/service.py` - `autonomy/enforcement.py` - `autonomy/preflight.py` Typical flow: 1. Create and activate budget. 2. Run preflight checks. 3. Attach runtime enforcer. 4. Gate each file/command/network action through enforcement APIs. 5. Trigger and resolve escalations. Best fit: - High-control automation in regulated or sensitive environments.
πŸ‘0
πŸ‘οΈ0
docs
πŸ“textβ€’6 months ago

Release Command Center Workflow

Coordinate release freezes, candidate promotion, validation, publication, and rollback handling through a defined state model.

devops
⭐1
# Release Command Center Workflow Imported from curated first-party documentation sources. ## What this covers Use this workflow to move a release from planning to publication with explicit validation checkpoints and rollback branches. ## Use this when - Managing release freezes and candidate promotion - Tracking validation state before publication - Documenting rollback-friendly release operations ## Expected outcomes - Release state changes are visible and repeatable - Rollback paths are treated as first-class outcomes - Validation discipline is preserved between phases ## Source synthesis - AGENT33/docs/functionality-and-workflows.md (https://github.com/mattmre/AGENT33/blob/main/docs/functionality-and-workflows.md) - AGENT33/docs/use-cases.md (https://github.com/mattmre/AGENT33/blob/main/docs/use-cases.md) ## Dedupe notes Merges AGENT33 release lifecycle details with release-oriented use-case guidance. ## Source excerpts ### AGENT33/docs/functionality-and-workflows.md ### 4.2 Release Lifecycle States: - `planned -> frozen -> rc -> validating -> released` - Failure/rollback branches: `failed`, `rolled_back` Main APIs: - `/v1/releases/{id}/freeze` - `/v1/releases/{id}/rc` - `/v1/releases/{id}/validate` - `/v1/releases/{id}/publish` - `/v1/releases/{id}/rollback` ### AGENT33/docs/use-cases.md ## 2. Release Command Center Goal: - Move releases through frozen -> RC -> validate -> publish with checklist controls. Use these modules: - `api/routes/releases.py` - `release/service.py` - `release/checklist.py` - `release/sync.py` - `release/rollback.py` Typical flow: 1. Create release (`POST /v1/releases`). 2. Freeze, cut RC, validate. 3. Update/evaluate checklist items. 4. Publish when checks pass. 5. Run sync dry-runs or real syncs. 6. Initiate rollback if needed. Best fit: - Teams with repeatable release compliance requirements.
πŸ‘0
πŸ‘οΈ0
docs
πŸ“textβ€’6 months ago

Guardrailed Code Review Pipeline

Run a staged code review workflow with explicit risk assessment, reviewer handoffs, and merge gates.

coding
⭐1
# Guardrailed Code Review Pipeline Imported from curated first-party documentation sources. ## What this covers Use this workflow when a change needs structured review evidence, clear approval stages, and a rollback-aware path to merge. ## Use this when - Multi-step L1 and L2 review signoff - High-risk changes that need audit trails - Standardizing reviewer decisions before merge ## Expected outcomes - Review states and approval checkpoints stay explicit - Risk assessment and evidence are captured before merge - The workflow remains small enough to reuse across repositories ## Source synthesis - AGENT33/docs/functionality-and-workflows.md (https://github.com/mattmre/AGENT33/blob/main/docs/functionality-and-workflows.md) - AGENT33/docs/walkthroughs.md (https://github.com/mattmre/AGENT33/blob/main/docs/walkthroughs.md) ## Dedupe notes Combines AGENT33 lifecycle mapping and operator walkthrough material into one review-oriented site entry. ## Source excerpts ### AGENT33/docs/functionality-and-workflows.md ### 4.1 Review Lifecycle States: - `draft -> ready -> l1-review -> l1-approved -> (optional l2-review -> l2-approved) -> approved -> merged` Main APIs: - `/v1/reviews/{id}/assess` - `/v1/reviews/{id}/assign-l1` - `/v1/reviews/{id}/l1` - `/v1/reviews/{id}/assign-l2` - `/v1/reviews/{id}/l2` - `/v1/reviews/{id}/approve` - `/v1/reviews/{id}/merge` ### AGENT33/docs/walkthroughs.md ## 4. Review Lifecycle (Two-Layer Signoff) Create review: ```bash curl -X POST http://localhost:8000/v1/reviews/ \ -H "Authorization: Bearer $TOKEN" \ -H "Content-Type: application/json" \ -d '{"task_id":"TASK-101","branch":"feat/docs-refresh","pr_number":12}' ``` Assess risk: ```bash curl -X POST http://localhost:8000/v1/reviews/<review_id>/assess \ -H "Authorization: Bearer $TOKEN" \ -H "Content-Type: application/json" \ -d '{"triggers":["api-public","security"]}' ``` Move to ready and assign L1: ```bash curl -X POST http://localhost:8000/v1/reviews/<review_id>/ready -H "Authorization: Bearer $TOKEN" curl -X POST http://localhost:8000/v1/reviews/<review_id>/assign-l1 -H "Authorization: Bearer $TOKEN" ``` Submit L1 decision: ```bash curl -X POST http://localhost:8000/v1/reviews/<review_id>/l1 \ -H "Authorization: Bearer $TOKEN" \ -H "Content-Type: application/json" \ -d '{"decision":"approved","issues":[],"comments":"L1 pass"}' ``` If L2 required, continue: ```bash curl -X POST http://localhost:8000/v1/reviews/<review_id>/assign-l2 -H "Authorization: Bearer $TOKEN" curl -X POST http://localhost:8000/v1/reviews/<review_id>/l2 \ -H "Authorization: Bearer $TOKEN" \ -H "Content-Type: application/json" \ -d '{"decision":"approved","issues":[],"commen ...
πŸ‘0
πŸ‘οΈ0
docs
πŸ€–system promptβ€’7 months ago

parallel-debugging

Debug complex issues using competing hypotheses with parallel

coding
⭐1
# Parallel Debugging Framework for debugging complex issues using the Analysis of Competing Hypotheses (ACH) methodology with parallel agent investigation. ## When to Use This Skill - Bug has multiple plausible root causes - Initial debugging attempts haven't identified the issue - Issue spans multiple modules or components - Need systematic root cause analysis with evidence - Want to avoid confirmation bias in debugging ## Hypothesis Generation Framework Generate hypotheses across 6 failure mode categories: ### 1. Logic Error - Incorrect conditional logic (wrong operator, missing case) - Off-by-one errors in loops or array access - Missing edge case handling - Incorrect algorithm implementation ### 2. Data Issue - Invalid or unexpected input data - Type mismatch or coercion error - Null/undefined/None where value expected - Encoding or serialization problem - Data truncation or overflow ### 3. State Problem - Race condition between concurrent operations - Stale cache returning outdated data - Incorrect initialization or default values - Unintended mutation of shared state - State machine transition error ### 4. Integration Failure - API contract violation (request/response mismatch) - Version incompatibility between components - Configuration mismatch between environments - Missing or incorrect environment variables - Network timeout or connection failure ### 5. Resource Issue - Memory leak causing gradual degradation - Connection pool exhaustion - File descriptor or handle leak - Disk space or quota exceeded - CPU saturation from inefficient processing ### 6. Environment - Missing runtime dependency - Wrong library or framework version - Platform-specific behavior difference - Permission or access control issue - Timezone or locale-related behavior ## Evidence Collection Standards ### What Constitutes Evidence | Evidence Type | Strength | Example | | ----------------- | -------- | --------------------------------------------------------------- | | **Direct** | Strong | Code at `file.ts:42` shows `if (x > 0)` should be `if (x >= 0)` | | **Correlational** | Medium | Error rate increased after commit `abc123` | | **Testimonial** | Weak | "It works on my machine" | | **Absence** | Variable | No null check found in the code path | ### Citation Format Always cite evidence with file:line references: ``` **Evidence**: The validation function at `src/validators/user.ts:87` does not check for empty strings, only null/undefined. This allows empty email addresses to pass validation. ``` ### Confidence Levels | Level | Criteria | | ------------------- | ----------------------------------------------------------------------------------- | | **High (>80%)** | Multiple direct evidence pieces, clear causal chain, no contradicting evidence | | **Medium (50-80%)** | Some direct evidence, plausible causal chain, minor ambiguities | | **Low (<50%)** | Mostly correlational evidence, incomplete causal chain, some contradicting evidence | ## Result Arbitration Protocol After all investigators report: ### Step 1: Categorize Results - **Confirmed**: High confidence, strong evidence, clear causal chain - **Plausible**: Medium confidence, some evidence, reasonable causal chain - **Falsified**: Evidence contradicts the hypothesis - **Inconclusive**: Insufficient evidence to confirm or falsify ### Step 2: Compare Confirmed Hypotheses If multiple hypotheses are confirmed, rank by: 1. Confidence level 2. Number of supporting evidence pieces 3. Strength of causal chain 4. Absence of contradicting evidence ### Step 3: Determine Root Cause - If one hypothesis clearly dominates: declare as root cause - If multiple hypotheses are equally likely: may be compound issue (multiple contributing causes) - If no hypotheses confirmed: generate new hypotheses based on evidence gathered ### Step 4: Validate Fix Before declaring the bug fixed: - [ ] Fix addresses the identified root cause - [ ] Fix doesn't introduce new issues - [ ] Original reproduction case no longer fails - [ ] Related edge cases are covered - [ ] Relevant tests are added or updated
πŸ‘0
πŸ‘οΈ0
πŸ€– Auto-discovered
πŸ€–system promptβ€’7 months ago

distributed-tracing

Implement distributed tracing with Jaeger and Tempo to track

coding
⭐1
# Distributed Tracing Implement distributed tracing with Jaeger and Tempo for request flow visibility across microservices. ## Purpose Track requests across distributed systems to understand latency, dependencies, and failure points. ## When to Use - Debug latency issues - Understand service dependencies - Identify bottlenecks - Trace error propagation - Analyze request paths ## Distributed Tracing Concepts ### Trace Structure ``` Trace (Request ID: abc123) ↓ Span (frontend) [100ms] ↓ Span (api-gateway) [80ms] β”œβ†’ Span (auth-service) [10ms] β””β†’ Span (user-service) [60ms] β””β†’ Span (database) [40ms] ``` ### Key Components - **Trace** - End-to-end request journey - **Span** - Single operation within a trace - **Context** - Metadata propagated between services - **Tags** - Key-value pairs for filtering - **Logs** - Timestamped events within a span ## Jaeger Setup ### Kubernetes Deployment ```bash # Deploy Jaeger Operator kubectl create namespace observability kubectl create -f https://github.com/jaegertracing/jaeger-operator/releases/download/v1.51.0/jaeger-operator.yaml -n observability # Deploy Jaeger instance kubectl apply -f - <<EOF apiVersion: jaegertracing.io/v1 kind: Jaeger metadata: name: jaeger namespace: observability spec: strategy: production storage: type: elasticsearch options: es: server-urls: http://elasticsearch:9200 ingress: enabled: true EOF ``` ### Docker Compose ```yaml version: "3.8" services: jaeger: image: jaegertracing/all-in-one:latest ports: - "5775:5775/udp" - "6831:6831/udp" - "6832:6832/udp" - "5778:5778" - "16686:16686" # UI - "14268:14268" # Collector - "14250:14250" # gRPC - "9411:9411" # Zipkin environment: - COLLECTOR_ZIPKIN_HOST_PORT=:9411 ``` **Reference:** See `references/jaeger-setup.md` ## Application Instrumentation ### OpenTelemetry (Recommended) #### Python (Flask) ```python from opentelemetry import trace from opentelemetry.exporter.jaeger.thrift import JaegerExporter from opentelemetry.sdk.resources import SERVICE_NAME, Resource from opentelemetry.sdk.trace import TracerProvider from opentelemetry.sdk.trace.export import BatchSpanProcessor from opentelemetry.instrumentation.flask import FlaskInstrumentor from flask import Flask # Initialize tracer resource = Resource(attributes={SERVICE_NAME: "my-service"}) provider = TracerProvider(resource=resource) processor = BatchSpanProcessor(JaegerExporter( agent_host_name="jaeger", agent_port=6831, )) provider.add_span_processor(processor) trace.set_tracer_provider(provider) # Instrument Flask app = Flask(__name__) FlaskInstrumentor().instrument_app(app) @app.route('/api/users') def get_users(): tracer = trace.get_tracer(__name__) with tracer.start_as_current_span("get_users") as span: span.set_attribute("user.count", 100) # Business logic users = fetch_users_from_db() return {"users": users} def fetch_users_from_db(): tracer = trace.get_tracer(__name__) with tracer.start_as_current_span("database_query") as span: span.set_attribute("db.system", "postgresql") span.set_attribute("db.statement", "SELECT * FROM users") # Database query return query_database() ``` #### Node.js (Express) ```javascript const { NodeTracerProvider } = require("@opentelemetry/sdk-trace-node"); const { JaegerExporter } = require("@opentelemetry/exporter-jaeger"); const { BatchSpanProcessor } = require("@opentelemetry/sdk-trace-base"); const { registerInstrumentations } = require("@opentelemetry/instrumentation"); const { HttpInstrumentation } = require("@opentelemetry/instrumentation-http"); const { ExpressInstrumentation, } = require("@opentelemetry/instrumentation-express"); // Initialize tracer const provider = new NodeTracerProvider({ resource: { attributes: { "service.name": "my-service" } }, }); const exporter = new JaegerExporter({ endpoint: "http://jaeger:14268/api/traces", }); provider.addSpanProcessor(new BatchSpanProcessor(exporter)); provider.register(); // Instrument libraries registerInstrumentations({ instrumentations: [new HttpInstrumentation(), new ExpressInstrumentation()], }); const express = require("express"); const app = express(); app.get("/api/users", async (req, res) => { const tracer = trace.getTracer("my-service"); const span = tracer.startSpan("get_users"); try { const users = await fetchUsers(); span.setAttributes({ "user.count": users.length }); res.json({ users }); } finally { span.end(); } }); ``` #### Go ```go package main import ( "context" "go.opentelemetry.io/otel" "go.opentelemetry.io/otel/exporters/jaeger" "go.opentelemetry.io/otel/sdk/resource" sdktrace "go.opentelemetry.io/otel/sdk/trace" semconv "go.opentelemetry.io/otel/semconv/v1.4.0" ) func initTracer() (*sdktrace.TracerProvider, error) { exporter, err := jaeger.New(jaeger.WithCollectorEndpoint( jaeger.WithEndpoint("http://jaeger:14268/api/traces"), )) if err != nil { return nil, err } tp := sdktrace.NewTracerProvider( sdktrace.WithBatcher(exporter), sdktrace.WithResource(resource.NewWithAttributes( semconv.SchemaURL, semconv.ServiceNameKey.String("my-service"), )), ) otel.SetTracerProvider(tp) return tp, nil } func getUsers(ctx context.Context) ([]User, error) { tracer := otel.Tracer("my-service") ctx, span := tracer.Start(ctx, "get_users") defer span.End() span.SetAttributes(attribute.String("user.filter", "active")) users, err := fetchUsersFromDB(ctx) if err != nil { span.RecordError(err) return nil, err } span.SetAttributes(attribute.Int("user.count", len(users))) return users, nil } ``` **Reference:** See `references/instrumentation.md` ## Context Propagation ### HTTP Headers ``` traceparent: 00-0af7651916cd43dd8448eb211c80319c-b7ad6b7169203331-01 tracestate: congo=t61rcWkgMzE ``` ### Propagation in HTTP Requests #### Python ```python from opentelemetry.propagate import inject headers = {} inject(headers) # Injects trace context response = requests.get('http://downstream-service/api', headers=headers) ``` #### Node.js ```javascript const { propagation } = require("@opentelemetry/api"); const headers = {}; propagation.inject(context.active(), headers); axios.get("http://downstream-service/api", { headers }); ``` ## Tempo Setup (Grafana) ### Kubernetes Deployment ```yaml apiVersion: v1 kind: ConfigMap metadata: name: tempo-config data: tempo.yaml: | server: http_listen_port: 3200 distributor: receivers: jaeger: protocols: thrift_http: grpc: otlp: protocols: http: grpc: storage: trace: backend: s3 s3: bucket: tempo-traces endpoint: s3.amazonaws.com querier: frontend_worker: frontend_address: tempo-query-frontend:9095 --- apiVersion: apps/v1 kind: Deployment metadata: name: tempo spec: replicas: 1 template: spec: containers: - name: tempo image: grafana/tempo:latest args: - -config.file=/etc/tempo/tempo.yaml volumeMounts: - name: config mountPath: /etc/tempo volumes: - name: config configMap: name: tempo-config ``` **Reference:** See `assets/jaeger-config.yaml.template` ## Sampling Strategies ### Probabilistic Sampling ```yaml # Sample 1% of traces sampler: type: probabilistic param: 0.01 ``` ### Rate Limiting Sampling ```yaml # Sample max 100 traces per second sampler: type: ratelimiting param: 100 ``` ### Adaptive Sampling ```python from opentelemetry.sdk.trace.sampling import ParentBased, TraceIdRatioBased # Sample based on trace ID (deterministic) sampler = ParentBased(root=TraceIdRatioBased(0.01)) ``` ## Trace Analysis ### Finding Slow Requests **Jaeger Query:** ``` service=my-service duration > 1s ``` ### Finding Errors **Jaeger Query:** ``` service=my-service error=true tags.http.status_code >= 500 ``` ### Service Dependency Graph Jaeger automatically generates service dependency graphs showing: - Service relationships - Request rates - Error rates - Average latencies ## Best Practices 1. **Sample appropriately** (1-10% in production) 2. **Add meaningful tags** (user_id, request_id) 3. **Propagate context** across all service boundaries 4. **Log exceptions** in spans 5. **Use consistent naming** for operations 6. **Monitor tracing overhead** (<1% CPU impact) 7. **Set up alerts** for trace errors 8. **Implement distributed context** (baggage) 9. **Use span events** for important milestones 10. **Document instrumentation** standards ## Integration with Logging ### Correlated Logs ```python import logging from opentelemetry import trace logger = logging.getLogger(__name__) def process_request(): span = trace.get_current_span() trace_id = span.get_span_context().trace_id logger.info( "Processing request", extra={"trace_id": format(trace_id, '032x')} ) ``` ## Troubleshooting **No traces appearing:** - Check collector endpoint - Verify network connectivity - Check sampling configuration - Review application logs **High latency overhead:** - Reduce sampling rate - Use batch span processor - Check exporter configuration ## Reference Files - `references/jaeger-setup.md` - Jaeger installation - `references/instrumentation.md` - Instrumentation patterns - `assets/jaeger-config.yaml.template` - Jaeger configuration ## Related Skills - `prometheus-configuration` - For metrics - `grafana-dashboards` - For visualization - `slo-implementation` - For latency SLOs
πŸ‘0
πŸ‘οΈ0
πŸ€– Auto-discovered
πŸ€–system promptβ€’7 months ago

langchain-architecture

Design LLM applications using LangChain 1.x and LangGraph for

coding
⭐1
# LangChain & LangGraph Architecture Master modern LangChain 1.x and LangGraph for building sophisticated LLM applications with agents, state management, memory, and tool integration. ## When to Use This Skill - Building autonomous AI agents with tool access - Implementing complex multi-step LLM workflows - Managing conversation memory and state - Integrating LLMs with external data sources and APIs - Creating modular, reusable LLM application components - Implementing document processing pipelines - Building production-grade LLM applications ## Package Structure (LangChain 1.x) ``` langchain (1.2.x) # High-level orchestration langchain-core (1.2.x) # Core abstractions (messages, prompts, tools) langchain-community # Third-party integrations langgraph # Agent orchestration and state management langchain-openai # OpenAI integrations langchain-anthropic # Anthropic/Claude integrations langchain-voyageai # Voyage AI embeddings langchain-pinecone # Pinecone vector store ``` ## Core Concepts ### 1. LangGraph Agents LangGraph is the standard for building agents in 2026. It provides: **Key Features:** - **StateGraph**: Explicit state management with typed state - **Durable Execution**: Agents persist through failures - **Human-in-the-Loop**: Inspect and modify state at any point - **Memory**: Short-term and long-term memory across sessions - **Checkpointing**: Save and resume agent state **Agent Patterns:** - **ReAct**: Reasoning + Acting with `create_react_agent` - **Plan-and-Execute**: Separate planning and execution nodes - **Multi-Agent**: Supervisor routing between specialized agents - **Tool-Calling**: Structured tool invocation with Pydantic schemas ### 2. State Management LangGraph uses TypedDict for explicit state: ```python from typing import Annotated, TypedDict from langgraph.graph import MessagesState # Simple message-based state class AgentState(MessagesState): """Extends MessagesState with custom fields.""" context: Annotated[list, "retrieved documents"] # Custom state for complex agents class CustomState(TypedDict): messages: Annotated[list, "conversation history"] context: Annotated[dict, "retrieved context"] current_step: str results: list ``` ### 3. Memory Systems Modern memory implementations: - **ConversationBufferMemory**: Stores all messages (short conversations) - **ConversationSummaryMemory**: Summarizes older messages (long conversations) - **ConversationTokenBufferMemory**: Token-based windowing - **VectorStoreRetrieverMemory**: Semantic similarity retrieval - **LangGraph Checkpointers**: Persistent state across sessions ### 4. Document Processing Loading, transforming, and storing documents: **Components:** - **Document Loaders**: Load from various sources - **Text Splitters**: Chunk documents intelligently - **Vector Stores**: Store and retrieve embeddings - **Retrievers**: Fetch relevant documents ### 5. Callbacks & Tracing LangSmith is the standard for observability: - Request/response logging - Token usage tracking - Latency monitoring - Error tracking - Trace visualization ## Quick Start ### Modern ReAct Agent with LangGraph ```python from langgraph.prebuilt import create_react_agent from langgraph.checkpoint.memory import MemorySaver from langchain_anthropic import ChatAnthropic from langchain_core.tools import tool import ast import operator # Initialize LLM (Claude Sonnet 4.6 recommended) llm = ChatAnthropic(model="claude-sonnet-4-6", temperature=0) # Define tools with Pydantic schemas @tool def search_database(query: str) -> str: """Search internal database for information.""" # Your database search logic return f"Results for: {query}" @tool def calculate(expression: str) -> str: """Safely evaluate a mathematical expression. Supports: +, -, *, /, **, %, parentheses Example: '(2 + 3) * 4' returns '20' """ # Safe math evaluation using ast allowed_operators = { ast.Add: operator.add, ast.Sub: operator.sub, ast.Mult: operator.mul, ast.Div: operator.truediv, ast.Pow: operator.pow, ast.Mod: operator.mod, ast.USub: operator.neg, } def _eval(node): if isinstance(node, ast.Constant): return node.value elif isinstance(node, ast.BinOp): left = _eval(node.left) right = _eval(node.right) return allowed_operators[type(node.op)](left, right) elif isinstance(node, ast.UnaryOp): operand = _eval(node.operand) return allowed_operators[type(node.op)](operand) else: raise ValueError(f"Unsupported operation: {type(node)}") try: tree = ast.parse(expression, mode='eval') return str(_eval(tree.body)) except Exception as e: return f"Error: {e}" tools = [search_database, calculate] # Create checkpointer for memory persistence checkpointer = MemorySaver() # Create ReAct agent agent = create_react_agent( llm, tools, checkpointer=checkpointer ) # Run agent with thread ID for memory config = {"configurable": {"thread_id": "user-123"}} result = await agent.ainvoke( {"messages": [("user", "Search for Python tutorials and calculate 25 * 4")]}, config=config ) ``` ## Architecture Patterns ### Pattern 1: RAG with LangGraph ```python from langgraph.graph import StateGraph, START, END from langchain_anthropic import ChatAnthropic from langchain_voyageai import VoyageAIEmbeddings from langchain_pinecone import PineconeVectorStore from langchain_core.documents import Document from langchain_core.prompts import ChatPromptTemplate from typing import TypedDict, Annotated class RAGState(TypedDict): question: str context: Annotated[list[Document], "retrieved documents"] answer: str # Initialize components llm = ChatAnthropic(model="claude-sonnet-4-6") embeddings = VoyageAIEmbeddings(model="voyage-3-large") vectorstore = PineconeVectorStore(index_name="docs", embedding=embeddings) retriever = vectorstore.as_retriever(search_kwargs={"k": 4}) # Define nodes async def retrieve(state: RAGState) -> RAGState: """Retrieve relevant documents.""" docs = await retriever.ainvoke(state["question"]) return {"context": docs} async def generate(state: RAGState) -> RAGState: """Generate answer from context.""" prompt = ChatPromptTemplate.from_template( """Answer based on the context below. If you cannot answer, say so. Context: {context} Question: {question} Answer:""" ) context_text = "\n\n".join(doc.page_content for doc in state["context"]) response = await llm.ainvoke( prompt.format(context=context_text, question=state["question"]) ) return {"answer": response.content} # Build graph builder = StateGraph(RAGState) builder.add_node("retrieve", retrieve) builder.add_node("generate", generate) builder.add_edge(START, "retrieve") builder.add_edge("retrieve", "generate") builder.add_edge("generate", END) rag_chain = builder.compile() # Use the chain result = await rag_chain.ainvoke({"question": "What is the main topic?"}) ``` ### Pattern 2: Custom Agent with Structured Tools ```python from langchain_core.tools import StructuredTool from pydantic import BaseModel, Field class SearchInput(BaseModel): """Input for database search.""" query: str = Field(description="Search query") filters: dict = Field(default={}, description="Optional filters") class EmailInput(BaseModel): """Input for sending email.""" recipient: str = Field(description="Email recipient") subject: str = Field(description="Email subject") content: str = Field(description="Email body") async def search_database(query: str, filters: dict = {}) -> str: """Search internal database for information.""" # Your database search logic return f"Results for '{query}' with filters {filters}" async def send_email(recipient: str, subject: str, content: str) -> str: """Send an email to specified recipient.""" # Email sending logic return f"Email sent to {recipient}" tools = [ StructuredTool.from_function( coroutine=search_database, name="search_database", description="Search internal database", args_schema=SearchInput ), StructuredTool.from_function( coroutine=send_email, name="send_email", description="Send an email", args_schema=EmailInput ) ] agent = create_react_agent(llm, tools) ``` ### Pattern 3: Multi-Step Workflow with StateGraph ```python from langgraph.graph import StateGraph, START, END from typing import TypedDict, Literal class WorkflowState(TypedDict): text: str entities: list analysis: str summary: str current_step: str async def extract_entities(state: WorkflowState) -> WorkflowState: """Extract key entities from text.""" prompt = f"Extract key entities from: {state['text']}\n\nReturn as JSON list." response = await llm.ainvoke(prompt) return {"entities": response.content, "current_step": "analyze"} async def analyze_entities(state: WorkflowState) -> WorkflowState: """Analyze extracted entities.""" prompt = f"Analyze these entities: {state['entities']}\n\nProvide insights." response = await llm.ainvoke(prompt) return {"analysis": response.content, "current_step": "summarize"} async def generate_summary(state: WorkflowState) -> WorkflowState: """Generate final summary.""" prompt = f"""Summarize: Entities: {state['entities']} Analysis: {state['analysis']} Provide a concise summary.""" response = await llm.ainvoke(prompt) return {"summary": response.content, "current_step": "complete"} def route_step(state: WorkflowState) -> Literal["analyze", "summarize", "end"]: """Route to next step based on current state.""" step = state.get("current_step", "extract") if step == "analyze": return "analyze" elif step == "summarize": return "summarize" return "end" # Build workflow builder = StateGraph(WorkflowState) builder.add_node("extract", extract_entities) builder.add_node("analyze", analyze_entities) builder.add_node("summarize", generate_summary) builder.add_edge(START, "extract") builder.add_conditional_edges("extract", route_step, { "analyze": "analyze", "summarize": "summarize", "end": END }) builder.add_conditional_edges("analyze", route_step, { "summarize": "summarize", "end": END }) builder.add_edge("summarize", END) workflow = builder.compile() ``` ### Pattern 4: Multi-Agent Orchestration ```python from langgraph.graph import StateGraph, START, END from langgraph.prebuilt import create_react_agent from langchain_core.messages import HumanMessage from typing import Literal class MultiAgentState(TypedDict): messages: list next_agent: str # Create specialized agents researcher = create_react_agent(llm, research_tools) writer = create_react_agent(llm, writing_tools) reviewer = create_react_agent(llm, review_tools) async def supervisor(state: MultiAgentState) -> MultiAgentState: """Route to appropriate agent based on task.""" prompt = f"""Based on the conversation, which agent should handle this? Options: - researcher: For finding information - writer: For creating content - reviewer: For reviewing and editing - FINISH: Task is complete Messages: {state['messages']} Respond with just the agent name.""" response = await llm.ainvoke(prompt) return {"next_agent": response.content.strip().lower()} def route_to_agent(state: MultiAgentState) -> Literal["researcher", "writer", "reviewer", "end"]: """Route based on supervisor decision.""" next_agent = state.get("next_agent", "").lower() if next_agent == "finish": return "end" return next_agent if next_agent in ["researcher", "writer", "reviewer"] else "end" # Build multi-agent graph builder = StateGraph(MultiAgentState) builder.add_node("supervisor", supervisor) builder.add_node("researcher", researcher) builder.add_node("writer", writer) builder.add_node("reviewer", reviewer) builder.add_edge(START, "supervisor") builder.add_conditional_edges("supervisor", route_to_agent, { "researcher": "researcher", "writer": "writer", "reviewer": "reviewer", "end": END }) # Each agent returns to supervisor for agent in ["researcher", "writer", "reviewer"]: builder.add_edge(agent, "supervisor") multi_agent = builder.compile() ``` ## Memory Management ### Token-Based Memory with LangGraph ```python from langgraph.checkpoint.memory import MemorySaver from langgraph.prebuilt import create_react_agent # In-memory checkpointer (development) checkpointer = MemorySaver() # Create agent with persistent memory agent = create_react_agent(llm, tools, checkpointer=checkpointer) # Each thread_id maintains separate conversation config = {"configurable": {"thread_id": "session-abc123"}} # Messages persist across invocations with same thread_id result1 = await agent.ainvoke({"messages": [("user", "My name is Alice")]}, config) result2 = await agent.ainvoke({"messages": [("user", "What's my name?")]}, config) # Agent remembers: "Your name is Alice" ``` ### Production Memory with PostgreSQL ```python from langgraph.checkpoint.postgres import PostgresSaver # Production checkpointer checkpointer = PostgresSaver.from_conn_string( "postgresql://user:pass@localhost/langgraph" ) agent = create_react_agent(llm, tools, checkpointer=checkpointer) ``` ### Vector Store Memory for Long-Term Context ```python from langchain_community.vectorstores import Chroma from langchain_voyageai import VoyageAIEmbeddings embeddings = VoyageAIEmbeddings(model="voyage-3-large") memory_store = Chroma( collection_name="conversation_memory", embedding_function=embeddings, persist_directory="./memory_db" ) async def retrieve_relevant_memory(query: str, k: int = 5) -> list: """Retrieve relevant past conversations.""" docs = await memory_store.asimilarity_search(query, k=k) return [doc.page_content for doc in docs] async def store_memory(content: str, metadata: dict = {}): """Store conversation in long-term memory.""" await memory_store.aadd_texts([content], metadatas=[metadata]) ``` ## Callback System & LangSmith ### LangSmith Tracing ```python import os from langchain_anthropic import ChatAnthropic # Enable LangSmith tracing os.environ["LANGCHAIN_TRACING_V2"] = "true" os.environ["LANGCHAIN_API_KEY"] = "your-api-key" os.environ["LANGCHAIN_PROJECT"] = "my-project" # All LangChain/LangGraph operations are automatically traced llm = ChatAnthropic(model="claude-sonnet-4-6") ``` ### Custom Callback Handler ```python from langchain_core.callbacks import BaseCallbackHandler from typing import Any, Dict, List class CustomCallbackHandler(BaseCallbackHandler): def on_llm_start( self, serialized: Dict[str, Any], prompts: List[str], **kwargs ) -> None: print(f"LLM started with {len(prompts)} prompts") def on_llm_end(self, response, **kwargs) -> None: print(f"LLM completed: {len(response.generations)} generations") def on_llm_error(self, error: Exception, **kwargs) -> None: print(f"LLM error: {error}") def on_tool_start( self, serialized: Dict[str, Any], input_str: str, **kwargs ) -> None: print(f"Tool started: {serialized.get('name')}") def on_tool_end(self, output: str, **kwargs) -> None: print(f"Tool completed: {output[:100]}...") # Use callbacks result = await agent.ainvoke( {"messages": [("user", "query")]}, config={"callbacks": [CustomCallbackHandler()]} ) ``` ## Streaming Responses ```python from langchain_anthropic import ChatAnthropic llm = ChatAnthropic(model="claude-sonnet-4-6", streaming=True) # Stream tokens async for chunk in llm.astream("Tell me a story"): print(chunk.content, end="", flush=True) # Stream agent events async for event in agent.astream_events( {"messages": [("user", "Search and summarize")]}, version="v2" ): if event["event"] == "on_chat_model_stream": print(event["data"]["chunk"].content, end="") elif event["event"] == "on_tool_start": print(f"\n[Using tool: {event['name']}]") ``` ## Testing Strategies ```python import pytest from unittest.mock import AsyncMock, patch @pytest.mark.asyncio async def test_agent_tool_selection(): """Test agent selects correct tool.""" with patch.object(llm, 'ainvoke') as mock_llm: mock_llm.return_value = AsyncMock(content="Using search_database") result = await agent.ainvoke({ "messages": [("user", "search for documents")] }) # Verify tool was called assert "search_database" in str(result) @pytest.mark.asyncio async def test_memory_persistence(): """Test memory persists across invocations.""" config = {"configurable": {"thread_id": "test-thread"}} # First message await agent.ainvoke( {"messages": [("user", "Remember: the code is 12345")]}, config ) # Second message should remember result = await agent.ainvoke( {"messages": [("user", "What was the code?")]}, config ) assert "12345" in result["messages"][-1].content ``` ## Performance Optimization ### 1. Caching with Redis ```python from langchain_community.cache import RedisCache from langchain_core.globals import set_llm_cache import redis redis_client = redis.Redis.from_url("redis://localhost:6379") set_llm_cache(RedisCache(redis_client)) ``` ### 2. Async Batch Processing ```python import asyncio from langchain_core.documents import Document async def process_documents(documents: list[Document]) -> list: """Process documents in parallel.""" tasks = [process_single(doc) for doc in documents] return await asyncio.gather(*tasks) async def process_single(doc: Document) -> dict: """Process a single document.""" chunks = text_splitter.split_documents([doc]) embeddings = await embeddings_model.aembed_documents( [c.page_content for c in chunks] ) return {"doc_id": doc.metadata.get("id"), "embeddings": embeddings} ``` ### 3. Connection Pooling ```python from langchain_pinecone import PineconeVectorStore from pinecone import Pinecone # Reuse Pinecone client pc = Pinecone(api_key=os.environ["PINECONE_API_KEY"]) index = pc.Index("my-index") # Create vector store with existing index vectorstore = PineconeVectorStore(index=index, embedding=embeddings) ``` ## Resources - [LangChain Documentation](https://python.langchain.com/docs/) - [LangGraph Documentation](https://langchain-ai.github.io/langgraph/) - [LangSmith Platform](https://smith.langchain.com/) - [LangChain GitHub](https://github.com/langchain-ai/langchain) - [LangGraph GitHub](https://github.com/langchain-ai/langgraph) ## Common Pitfalls 1. **Using Deprecated APIs**: Use LangGraph for agents, not `initialize_agent` 2. **Memory Overflow**: Use checkpointers with TTL for long-running agents 3. **Poor Tool Descriptions**: Clear descriptions help LLM select correct tools 4. **Context Window Exceeded**: Use summarization or sliding window memory 5. **No Error Handling**: Wrap too
πŸ‘0
πŸ‘οΈ0
πŸ€– Auto-discovered
πŸ€–system promptβ€’7 months ago

gitops-workflow

Implement GitOps workflows with ArgoCD and Flux for automated,

architecture
⭐1
# GitOps Workflow Complete guide to implementing GitOps workflows with ArgoCD and Flux for automated Kubernetes deployments. ## Purpose Implement declarative, Git-based continuous delivery for Kubernetes using ArgoCD or Flux CD, following OpenGitOps principles. ## When to Use This Skill - Set up GitOps for Kubernetes clusters - Automate application deployments from Git - Implement progressive delivery strategies - Manage multi-cluster deployments - Configure automated sync policies - Set up secret management in GitOps ## OpenGitOps Principles 1. **Declarative** - Entire system described declaratively 2. **Versioned and Immutable** - Desired state stored in Git 3. **Pulled Automatically** - Software agents pull desired state 4. **Continuously Reconciled** - Agents reconcile actual vs desired state ## ArgoCD Setup ### 1. Installation ```bash # Create namespace kubectl create namespace argocd # Install ArgoCD kubectl apply -n argocd -f https://raw.githubusercontent.com/argoproj/argo-cd/stable/manifests/install.yaml # Get admin password kubectl -n argocd get secret argocd-initial-admin-secret -o jsonpath="{.data.password}" | base64 -d ``` **Reference:** See `references/argocd-setup.md` for detailed setup ### 2. Repository Structure ``` gitops-repo/ β”œβ”€β”€ apps/ β”‚ β”œβ”€β”€ production/ β”‚ β”‚ β”œβ”€β”€ app1/ β”‚ β”‚ β”‚ β”œβ”€β”€ kustomization.yaml β”‚ β”‚ β”‚ └── deployment.yaml β”‚ β”‚ └── app2/ β”‚ └── staging/ β”œβ”€β”€ infrastructure/ β”‚ β”œβ”€β”€ ingress-nginx/ β”‚ β”œβ”€β”€ cert-manager/ β”‚ └── monitoring/ └── argocd/ β”œβ”€β”€ applications/ └── projects/ ``` ### 3. Create Application ```yaml # argocd/applications/my-app.yaml apiVersion: argoproj.io/v1alpha1 kind: Application metadata: name: my-app namespace: argocd spec: project: default source: repoURL: https://github.com/org/gitops-repo targetRevision: main path: apps/production/my-app destination: server: https://kubernetes.default.svc namespace: production syncPolicy: automated: prune: true selfHeal: true syncOptions: - CreateNamespace=true ``` ### 4. App of Apps Pattern ```yaml apiVersion: argoproj.io/v1alpha1 kind: Application metadata: name: applications namespace: argocd spec: project: default source: repoURL: https://github.com/org/gitops-repo targetRevision: main path: argocd/applications destination: server: https://kubernetes.default.svc namespace: argocd syncPolicy: automated: {} ``` ## Flux CD Setup ### 1. Installation ```bash # Install Flux CLI curl -s https://fluxcd.io/install.sh | sudo bash # Bootstrap Flux flux bootstrap github \ --owner=org \ --repository=gitops-repo \ --branch=main \ --path=clusters/production \ --personal ``` ### 2. Create GitRepository ```yaml apiVersion: source.toolkit.fluxcd.io/v1 kind: GitRepository metadata: name: my-app namespace: flux-system spec: interval: 1m url: https://github.com/org/my-app ref: branch: main ``` ### 3. Create Kustomization ```yaml apiVersion: kustomize.toolkit.fluxcd.io/v1 kind: Kustomization metadata: name: my-app namespace: flux-system spec: interval: 5m path: ./deploy prune: true sourceRef: kind: GitRepository name: my-app ``` ## Sync Policies ### Auto-Sync Configuration **ArgoCD:** ```yaml syncPolicy: automated: prune: true # Delete resources not in Git selfHeal: true # Reconcile manual changes allowEmpty: false retry: limit: 5 backoff: duration: 5s factor: 2 maxDuration: 3m ``` **Flux:** ```yaml spec: interval: 1m prune: true wait: true timeout: 5m ``` **Reference:** See `references/sync-policies.md` ## Progressive Delivery ### Canary Deployment with ArgoCD Rollouts ```yaml apiVersion: argoproj.io/v1alpha1 kind: Rollout metadata: name: my-app spec: replicas: 5 strategy: canary: steps: - setWeight: 20 - pause: { duration: 1m } - setWeight: 50 - pause: { duration: 2m } - setWeight: 100 ``` ### Blue-Green Deployment ```yaml strategy: blueGreen: activeService: my-app previewService: my-app-preview autoPromotionEnabled: false ``` ## Secret Management ### External Secrets Operator ```yaml apiVersion: external-secrets.io/v1beta1 kind: ExternalSecret metadata: name: db-credentials spec: refreshInterval: 1h secretStoreRef: name: aws-secrets-manager kind: SecretStore target: name: db-credentials data: - secretKey: password remoteRef: key: prod/db/password ``` ### Sealed Secrets ```bash # Encrypt secret kubeseal --format yaml < secret.yaml > sealed-secret.yaml # Commit sealed-secret.yaml to Git ``` ## Best Practices 1. **Use separate repos or branches** for different environments 2. **Implement RBAC** for Git repositories 3. **Enable notifications** for sync failures 4. **Use health checks** for custom resources 5. **Implement approval gates** for production 6. **Keep secrets out of Git** (use External Secrets) 7. **Use App of Apps pattern** for organization 8. **Tag releases** for easy rollback 9. **Monitor sync status** with alerts 10. **Test changes** in staging first ## Troubleshooting **Sync failures:** ```bash argocd app get my-app argocd app sync my-app --prune ``` **Out of sync status:** ```bash argocd app diff my-app argocd app sync my-app --force ``` ## Related Skills - `k8s-manifest-generator` - For creating manifests - `helm-chart-scaffolding` - For packaging applications
πŸ‘0
πŸ‘οΈ0
πŸ€– Auto-discovered
πŸ€–system promptβ€’7 months ago

nodejs-backend-patterns

Build production-ready Node.js backend services with

coding
⭐1
# Node.js Backend Patterns Comprehensive guidance for building scalable, maintainable, and production-ready Node.js backend applications with modern frameworks, architectural patterns, and best practices. ## When to Use This Skill - Building REST APIs or GraphQL servers - Creating microservices with Node.js - Implementing authentication and authorization - Designing scalable backend architectures - Setting up middleware and error handling - Integrating databases (SQL and NoSQL) - Building real-time applications with WebSockets - Implementing background job processing ## Core Frameworks ### Express.js - Minimalist Framework **Basic Setup:** ```typescript import express, { Request, Response, NextFunction } from "express"; import helmet from "helmet"; import cors from "cors"; import compression from "compression"; const app = express(); // Security middleware app.use(helmet()); app.use(cors({ origin: process.env.ALLOWED_ORIGINS?.split(",") })); app.use(compression()); // Body parsing app.use(express.json({ limit: "10mb" })); app.use(express.urlencoded({ extended: true, limit: "10mb" })); // Request logging app.use((req: Request, res: Response, next: NextFunction) => { console.log(`${req.method} ${req.path}`); next(); }); const PORT = process.env.PORT || 3000; app.listen(PORT, () => { console.log(`Server running on port ${PORT}`); }); ``` ### Fastify - High Performance Framework **Basic Setup:** ```typescript import Fastify from "fastify"; import helmet from "@fastify/helmet"; import cors from "@fastify/cors"; import compress from "@fastify/compress"; const fastify = Fastify({ logger: { level: process.env.LOG_LEVEL || "info", transport: { target: "pino-pretty", options: { colorize: true }, }, }, }); // Plugins await fastify.register(helmet); await fastify.register(cors, { origin: true }); await fastify.register(compress); // Type-safe routes with schema validation fastify.post<{ Body: { name: string; email: string }; Reply: { id: string; name: string }; }>( "/users", { schema: { body: { type: "object", required: ["name", "email"], properties: { name: { type: "string", minLength: 1 }, email: { type: "string", format: "email" }, }, }, }, }, async (request, reply) => { const { name, email } = request.body; return { id: "123", name }; }, ); await fastify.listen({ port: 3000, host: "0.0.0.0" }); ``` ## Architectural Patterns ### Pattern 1: Layered Architecture **Structure:** ``` src/ β”œβ”€β”€ controllers/ # Handle HTTP requests/responses β”œβ”€β”€ services/ # Business logic β”œβ”€β”€ repositories/ # Data access layer β”œβ”€β”€ models/ # Data models β”œβ”€β”€ middleware/ # Express/Fastify middleware β”œβ”€β”€ routes/ # Route definitions β”œβ”€β”€ utils/ # Helper functions β”œβ”€β”€ config/ # Configuration └── types/ # TypeScript types ``` **Controller Layer:** ```typescript // controllers/user.controller.ts import { Request, Response, NextFunction } from "express"; import { UserService } from "../services/user.service"; import { CreateUserDTO, UpdateUserDTO } from "../types/user.types"; export class UserController { constructor(private userService: UserService) {} async createUser(req: Request, res: Response, next: NextFunction) { try { const userData: CreateUserDTO = req.body; const user = await this.userService.createUser(userData); res.status(201).json(user); } catch (error) { next(error); } } async getUser(req: Request, res: Response, next: NextFunction) { try { const { id } = req.params; const user = await this.userService.getUserById(id); res.json(user); } catch (error) { next(error); } } async updateUser(req: Request, res: Response, next: NextFunction) { try { const { id } = req.params; const updates: UpdateUserDTO = req.body; const user = await this.userService.updateUser(id, updates); res.json(user); } catch (error) { next(error); } } async deleteUser(req: Request, res: Response, next: NextFunction) { try { const { id } = req.params; await this.userService.deleteUser(id); res.status(204).send(); } catch (error) { next(error); } } } ``` **Service Layer:** ```typescript // services/user.service.ts import { UserRepository } from "../repositories/user.repository"; import { CreateUserDTO, UpdateUserDTO, User } from "../types/user.types"; import { NotFoundError, ValidationError } from "../utils/errors"; import bcrypt from "bcrypt"; export class UserService { constructor(private userRepository: UserRepository) {} async createUser(userData: CreateUserDTO): Promise<User> { // Validation const existingUser = await this.userRepository.findByEmail(userData.email); if (existingUser) { throw new ValidationError("Email already exists"); } // Hash password const hashedPassword = await bcrypt.hash(userData.password, 10); // Create user const user = await this.userRepository.create({ ...userData, password: hashedPassword, }); // Remove password from response const { password, ...userWithoutPassword } = user; return userWithoutPassword as User; } async getUserById(id: string): Promise<User> { const user = await this.userRepository.findById(id); if (!user) { throw new NotFoundError("User not found"); } const { password, ...userWithoutPassword } = user; return userWithoutPassword as User; } async updateUser(id: string, updates: UpdateUserDTO): Promise<User> { const user = await this.userRepository.update(id, updates); if (!user) { throw new NotFoundError("User not found"); } const { password, ...userWithoutPassword } = user; return userWithoutPassword as User; } async deleteUser(id: string): Promise<void> { const deleted = await this.userRepository.delete(id); if (!deleted) { throw new NotFoundError("User not found"); } } } ``` **Repository Layer:** ```typescript // repositories/user.repository.ts import { Pool } from "pg"; import { CreateUserDTO, UpdateUserDTO, UserEntity } from "../types/user.types"; export class UserRepository { constructor(private db: Pool) {} async create( userData: CreateUserDTO & { password: string }, ): Promise<UserEntity> { const query = ` INSERT INTO users (name, email, password) VALUES ($1, $2, $3) RETURNING id, name, email, password, created_at, updated_at `; const { rows } = await this.db.query(query, [ userData.name, userData.email, userData.password, ]); return rows[0]; } async findById(id: string): Promise<UserEntity | null> { const query = "SELECT * FROM users WHERE id = $1"; const { rows } = await this.db.query(query, [id]); return rows[0] || null; } async findByEmail(email: string): Promise<UserEntity | null> { const query = "SELECT * FROM users WHERE email = $1"; const { rows } = await this.db.query(query, [email]); return rows[0] || null; } async update(id: string, updates: UpdateUserDTO): Promise<UserEntity | null> { const fields = Object.keys(updates); const values = Object.values(updates); const setClause = fields .map((field, idx) => `${field} = $${idx + 2}`) .join(", "); const query = ` UPDATE users SET ${setClause}, updated_at = CURRENT_TIMESTAMP WHERE id = $1 RETURNING * `; const { rows } = await this.db.query(query, [id, ...values]); return rows[0] || null; } async delete(id: string): Promise<boolean> { const query = "DELETE FROM users WHERE id = $1"; const { rowCount } = await this.db.query(query, [id]); return rowCount > 0; } } ``` ### Pattern 2: Dependency Injection **DI Container:** ```typescript // di-container.ts import { Pool } from "pg"; import { UserRepository } from "./repositories/user.repository"; import { UserService } from "./services/user.service"; import { UserController } from "./controllers/user.controller"; import { AuthService } from "./services/auth.service"; class Container { private instances = new Map<string, any>(); register<T>(key: string, factory: () => T): void { this.instances.set(key, factory); } resolve<T>(key: string): T { const factory = this.instances.get(key); if (!factory) { throw new Error(`No factory registered for ${key}`); } return factory(); } singleton<T>(key: string, factory: () => T): void { let instance: T; this.instances.set(key, () => { if (!instance) { instance = factory(); } return instance; }); } } export const container = new Container(); // Register dependencies container.singleton( "db", () => new Pool({ host: process.env.DB_HOST, port: parseInt(process.env.DB_PORT || "5432"), database: process.env.DB_NAME, user: process.env.DB_USER, password: process.env.DB_PASSWORD, max: 20, idleTimeoutMillis: 30000, connectionTimeoutMillis: 2000, }), ); container.singleton( "userRepository", () => new UserRepository(container.resolve("db")), ); container.singleton( "userService", () => new UserService(container.resolve("userRepository")), ); container.register( "userController", () => new UserController(container.resolve("userService")), ); container.singleton( "authService", () => new AuthService(container.resolve("userRepository")), ); ``` ## Middleware Patterns ### Authentication Middleware ```typescript // middleware/auth.middleware.ts import { Request, Response, NextFunction } from "express"; import jwt from "jsonwebtoken"; import { UnauthorizedError } from "../utils/errors"; interface JWTPayload { userId: string; email: string; } declare global { namespace Express { interface Request { user?: JWTPayload; } } } export const authenticate = async ( req: Request, res: Response, next: NextFunction, ) => { try { const token = req.headers.authorization?.replace("Bearer ", ""); if (!token) { throw new UnauthorizedError("No token provided"); } const payload = jwt.verify(token, process.env.JWT_SECRET!) as JWTPayload; req.user = payload; next(); } catch (error) { next(new UnauthorizedError("Invalid token")); } }; export const authorize = (...roles: string[]) => { return async (req: Request, res: Response, next: NextFunction) => { if (!req.user) { return next(new UnauthorizedError("Not authenticated")); } // Check if user has required role const hasRole = roles.some((role) => req.user?.roles?.includes(role)); if (!hasRole) { return next(new UnauthorizedError("Insufficient permissions")); } next(); }; }; ``` ### Validation Middleware ```typescript // middleware/validation.middleware.ts import { Request, Response, NextFunction } from "express"; import { AnyZodObject, ZodError } from "zod"; import { ValidationError } from "../utils/errors"; export const validate = (schema: AnyZodObject) => { return async (req: Request, res: Response, next: NextFunction) => { try { await schema.parseAsync({ body: req.body, query: req.query, params: req.params, }); next(); } catch (error) { if (error instanceof ZodError) { const errors = error.errors.map((err) => ({ field: err.path.join("."), message: err.message, })); next(new ValidationError("Validation failed", errors)); } else { next(error); } } }; }; // Usage with Zod import { z } from "zod"; const createUserSchema = z.object({ body: z.object({ name: z.string().min(1), email: z.string().email(), password: z.string().min(8), }), }); router.post("/users", validate(createUserSchema), userController.createUser); ``` ### Rate Limiting Middleware ```typescript // middleware/rate-limit.middleware.ts import rateLimit from "express-rate-limit"; import RedisStore from "rate-limit-redis"; import Redis from "ioredis"; const redis = new Redis({ host: process.env.REDIS_HOST, port: parseInt(process.env.REDIS_PORT || "6379"), }); export const apiLimiter = rateLimit({ store: new RedisStore({ client: redis, prefix: "rl:", }), windowMs: 15 * 60 * 1000, // 15 minutes max: 100, // Limit each IP to 100 requests per windowMs message: "Too many requests from this IP, please try again later", standardHeaders: true, legacyHeaders: false, }); export const authLimiter = rateLimit({ store: new RedisStore({ client: redis, prefix: "rl:auth:", }), windowMs: 15 * 60 * 1000, max: 5, // Stricter limit for auth endpoints skipSuccessfulRequests: true, }); ``` ### Request Logging Middleware ```typescript // middleware/logger.middleware.ts import { Request, Response, NextFunction } from "express"; import pino from "pino"; const logger = pino({ level: process.env.LOG_LEVEL || "info", transport: { target: "pino-pretty", options: { colorize: true }, }, }); export const requestLogger = ( req: Request, res: Response, next: NextFunction, ) => { const start = Date.now(); // Log response when finished res.on("finish", () => { const duration = Date.now() - start; logger.info({ method: req.method, url: req.url, status: res.statusCode, duration: `${duration}ms`, userAgent: req.headers["user-agent"], ip: req.ip, }); }); next(); }; export { logger }; ``` ## Error Handling ### Custom Error Classes ```typescript // utils/errors.ts export class AppError extends Error { constructor( public message: string, public statusCode: number = 500, public isOperational: boolean = true, ) { super(message); Object.setPrototypeOf(this, AppError.prototype); Error.captureStackTrace(this, this.constructor); } } export class ValidationError extends AppError { constructor( message: string, public errors?: any[], ) { super(message, 400); } } export class NotFoundError extends AppError { constructor(message: string = "Resource not found") { super(message, 404); } } export class UnauthorizedError extends AppError { constructor(message: string = "Unauthorized") { super(message, 401); } } export class ForbiddenError extends AppError { constructor(message: string = "Forbidden") { super(message, 403); } } export class ConflictError extends AppError { constructor(message: string) { super(message, 409); } } ``` ### Global Error Handler ```typescript // middleware/error-handler.ts import { Request, Response, NextFunction } from "express"; import { AppError } from "../utils/errors"; import { logger } from "./logger.middleware"; export const errorHandler = ( err: Error, req: Request, res: Response, next: NextFunction, ) => { if (err instanceof AppError) { return res.status(err.statusCode).json({ status: "error", message: err.message, ...(err instanceof ValidationError && { errors: err.errors }), }); } // Log unexpected errors logger.error({ error: err.message, stack: err.stack, url: req.url, method: req.method, }); // Don't leak error details in production const message = process.env.NODE_ENV === "production" ? "Internal server error" : err.message; res.status(500).json({ status: "error", message, }); }; // Async error wrapper export const asyncHandler = ( fn: (req: Request, res: Response, next: NextFunction) => Promise<any>, ) => { return (req: Request, res: Response, next: NextFunction) => { Promise.resolve(fn(req, res, next)).catch(next); }; }; ``` ## Database Patterns ### PostgreSQL with Connection Pool ```typescript // config/database.ts import { Pool, PoolConfig } from "pg"; const poolConfig: PoolConfig = { host: process.env.DB_HOST, port: parseInt(process.env.DB_PORT || "5432"), database: process.env.DB_NAME, user: process.env.DB_USER, password: process.env.DB_PASSWORD, max: 20, idleTimeoutMillis: 30000, connectionTimeoutMillis: 2000, }; export const pool = new Pool(poolConfig); // Test connection pool.on("connect", () => { console.log("Database connected"); }); pool.on("error", (err) => { console.error("Unexpected database error", err); process.exit(-1); }); // Graceful shutdown export const closeDatabase = async () => { await pool.end(); console.log("Database connection closed"); }; ``` ### MongoDB with Mongoose ```typescript // config/mongoose.ts import mongoose from "mongoose"; const connectDB = async () => { try { await mongoose.connect(process.env.MONGODB_URI!, { maxPoolSize: 10, serverSelectionTimeoutMS: 5000, socketTimeoutMS: 45000, }); console.log("MongoDB connected"); } catch (error) { console.error("MongoDB connection error:", error); process.exit(1); } }; mongoose.connection.on("disconnected", () => { console.log("MongoDB disconnected"); }); mongoose.connection.on("error", (err) => { console.error("MongoDB error:", err); }); export { connectDB }; // Model example import { Schema, model, Document } from "mongoose"; interface IUser extends Document { name: string; email: string; password: string; createdAt: Date; updatedAt: Date; } const userSchema = new Schema<IUser>( { name: { type: String, required: true }, email: { type: String, required: true, unique: true }, password: { type: String, required: true }, }, { timestamps: true, }, ); // Indexes userSchema.index({ email: 1 }); export const User = model<IUser>("User", userSchema); ``` ### Transaction Pattern ```typescript // services/order.service.ts import { Pool } from "pg"; export class OrderService { constructor(private db: Pool) {} async createOrder(userId: string, items: any[]) { const client = await this.db.connect(); try { await client.query("BEGIN"); // Create order const orderResult = await client.query( "INSERT INTO orders (user_id, total) VALUES ($1, $2) RETURNING id", [userId, calculateTotal(items)], ); const orderId = orderResult.rows[0].id; // Create order items for (const item of items) { await client.query( "INSERT INTO order_items (order_id, product_id, quantity, price) VALUES ($1, $2, $3, $4)", [orderId, item.productId, item.quantity, item.price], ); // Update inventory await client.query( "UPDATE products SET stock = stock - $1 WHERE id = $2", [item.quantity, item.productId], ); } await client.query("COMMIT"); return orderId; } catch (error) { await client.query("ROLLBACK"); throw error; } finally { client.release(); } } } ``` ## Authentication & Authorization ### JWT Authentication ```typescript // services/auth.service.ts import jwt from
πŸ‘0
πŸ‘οΈ0
πŸ€– Auto-discovered
πŸ€–system promptβ€’7 months ago

postmortem-writing

Write effective blameless postmortems with root cause analysis,

coding
⭐1
# Postmortem Writing Comprehensive guide to writing effective, blameless postmortems that drive organizational learning and prevent incident recurrence. ## When to Use This Skill - Conducting post-incident reviews - Writing postmortem documents - Facilitating blameless postmortem meetings - Identifying root causes and contributing factors - Creating actionable follow-up items - Building organizational learning culture ## Core Concepts ### 1. Blameless Culture | Blame-Focused | Blameless | | ------------------------ | --------------------------------- | | "Who caused this?" | "What conditions allowed this?" | | "Someone made a mistake" | "The system allowed this mistake" | | Punish individuals | Improve systems | | Hide information | Share learnings | | Fear of speaking up | Psychological safety | ### 2. Postmortem Triggers - SEV1 or SEV2 incidents - Customer-facing outages > 15 minutes - Data loss or security incidents - Near-misses that could have been severe - Novel failure modes - Incidents requiring unusual intervention ## Quick Start ### Postmortem Timeline ``` Day 0: Incident occurs Day 1-2: Draft postmortem document Day 3-5: Postmortem meeting Day 5-7: Finalize document, create tickets Week 2+: Action item completion Quarterly: Review patterns across incidents ``` ## Templates ### Template 1: Standard Postmortem ```markdown # Postmortem: [Incident Title] **Date**: 2024-01-15 **Authors**: @alice, @bob **Status**: Draft | In Review | Final **Incident Severity**: SEV2 **Incident Duration**: 47 minutes ## Executive Summary On January 15, 2024, the payment processing service experienced a 47-minute outage affecting approximately 12,000 customers. The root cause was a database connection pool exhaustion triggered by a configuration change in deployment v2.3.4. The incident was resolved by rolling back to v2.3.3 and increasing connection pool limits. **Impact**: - 12,000 customers unable to complete purchases - Estimated revenue loss: $45,000 - 847 support tickets created - No data loss or security implications ## Timeline (All times UTC) | Time | Event | | ----- | ----------------------------------------------- | | 14:23 | Deployment v2.3.4 completed to production | | 14:31 | First alert: `payment_error_rate > 5%` | | 14:33 | On-call engineer @alice acknowledges alert | | 14:35 | Initial investigation begins, error rate at 23% | | 14:41 | Incident declared SEV2, @bob joins | | 14:45 | Database connection exhaustion identified | | 14:52 | Decision to rollback deployment | | 14:58 | Rollback to v2.3.3 initiated | | 15:10 | Rollback complete, error rate dropping | | 15:18 | Service fully recovered, incident resolved | ## Root Cause Analysis ### What Happened The v2.3.4 deployment included a change to the database query pattern that inadvertently removed connection pooling for a frequently-called endpoint. Each request opened a new database connection instead of reusing pooled connections. ### Why It Happened 1. **Proximate Cause**: Code change in `PaymentRepository.java` replaced pooled `DataSource` with direct `DriverManager.getConnection()` calls. 2. **Contributing Factors**: - Code review did not catch the connection handling change - No integration tests specifically for connection pool behavior - Staging environment has lower traffic, masking the issue - Database connection metrics alert threshold was too high (90%) 3. **5 Whys Analysis**: - Why did the service fail? β†’ Database connections exhausted - Why were connections exhausted? β†’ Each request opened new connection - Why did each request open new connection? β†’ Code bypassed connection pool - Why did code bypass connection pool? β†’ Developer unfamiliar with codebase patterns - Why was developer unfamiliar? β†’ No documentation on connection management patterns ### System Diagram ``` [Client] β†’ [Load Balancer] β†’ [Payment Service] β†’ [Database] ↓ Connection Pool (broken) ↓ Direct connections (cause) ``` ## Detection ### What Worked - Error rate alert fired within 8 minutes of deployment - Grafana dashboard clearly showed connection spike - On-call response was swift (2 minute acknowledgment) ### What Didn't Work - Database connection metric alert threshold too high - No deployment-correlated alerting - Canary deployment would have caught this earlier ### Detection Gap The deployment completed at 14:23, but the first alert didn't fire until 14:31 (8 minutes). A deployment-aware alert could have detected the issue faster. ## Response ### What Worked - On-call engineer quickly identified database as the issue - Rollback decision was made decisively - Clear communication in incident channel ### What Could Be Improved - Took 10 minutes to correlate issue with recent deployment - Had to manually check deployment history - Rollback took 12 minutes (could be faster) ## Impact ### Customer Impact - 12,000 unique customers affected - Average impact duration: 35 minutes - 847 support tickets (23% of affected users) - Customer satisfaction score dropped 12 points ### Business Impact - Estimated revenue loss: $45,000 - Support cost: ~$2,500 (agent time) - Engineering time: ~8 person-hours ### Technical Impact - Database primary experienced elevated load - Some replica lag during incident - No permanent damage to systems ## Lessons Learned ### What Went Well 1. Alerting detected the issue before customer reports 2. Team collaborated effectively under pressure 3. Rollback procedure worked smoothly 4. Communication was clear and timely ### What Went Wrong 1. Code review missed critical change 2. Test coverage gap for connection pooling 3. Staging environment doesn't reflect production traffic 4. Alert thresholds were not tuned properly ### Where We Got Lucky 1. Incident occurred during business hours with full team available 2. Database handled the load without failing completely 3. No other incidents occurred simultaneously ## Action Items | Priority | Action | Owner | Due Date | Ticket | |----------|--------|-------|----------|--------| | P0 | Add integration test for connection pool behavior | @alice | 2024-01-22 | ENG-1234 | | P0 | Lower database connection alert threshold to 70% | @bob | 2024-01-17 | OPS-567 | | P1 | Document connection management patterns | @alice | 2024-01-29 | DOC-89 | | P1 | Implement deployment-correlated alerting | @bob | 2024-02-05 | OPS-568 | | P2 | Evaluate canary deployment strategy | @charlie | 2024-02-15 | ENG-1235 | | P2 | Load test staging with production-like traffic | @dave | 2024-02-28 | QA-123 | ## Appendix ### Supporting Data #### Error Rate Graph [Link to Grafana dashboard snapshot] #### Database Connection Graph [Link to metrics] ### Related Incidents - 2023-11-02: Similar connection issue in User Service (POSTMORTEM-42) ### References - [Connection Pool Best Practices](internal-wiki/connection-pools) - [Deployment Runbook](internal-wiki/deployment-runbook) ``` ### Template 2: 5 Whys Analysis ```markdown # 5 Whys Analysis: [Incident] ## Problem Statement Payment service experienced 47-minute outage due to database connection exhaustion. ## Analysis ### Why #1: Why did the service fail? **Answer**: Database connections were exhausted, causing all new requests to fail. **Evidence**: Metrics showed connection count at 100/100 (max), with 500+ pending requests. --- ### Why #2: Why were database connections exhausted? **Answer**: Each incoming request opened a new database connection instead of using the connection pool. **Evidence**: Code diff shows direct `DriverManager.getConnection()` instead of pooled `DataSource`. --- ### Why #3: Why did the code bypass the connection pool? **Answer**: A developer refactored the repository class and inadvertently changed the connection acquisition method. **Evidence**: PR #1234 shows the change, made while fixing a different bug. --- ### Why #4: Why wasn't this caught in code review? **Answer**: The reviewer focused on the functional change (the bug fix) and didn't notice the infrastructure change. **Evidence**: Review comments only discuss business logic. --- ### Why #5: Why isn't there a safety net for this type of change? **Answer**: We lack automated tests that verify connection pool behavior and lack documentation about our connection patterns. **Evidence**: Test suite has no tests for connection handling; wiki has no article on database connections. ## Root Causes Identified 1. **Primary**: Missing automated tests for infrastructure behavior 2. **Secondary**: Insufficient documentation of architectural patterns 3. **Tertiary**: Code review checklist doesn't include infrastructure considerations ## Systemic Improvements | Root Cause | Improvement | Type | | ------------- | --------------------------------- | ---------- | | Missing tests | Add infrastructure behavior tests | Prevention | | Missing docs | Document connection patterns | Prevention | | Review gaps | Update review checklist | Detection | | No canary | Implement canary deployments | Mitigation | ``` ### Template 3: Quick Postmortem (Minor Incidents) ```markdown # Quick Postmortem: [Brief Title] **Date**: 2024-01-15 | **Duration**: 12 min | **Severity**: SEV3 ## What Happened API latency spiked to 5s due to cache miss storm after cache flush. ## Timeline - 10:00 - Cache flush initiated for config update - 10:02 - Latency alerts fire - 10:05 - Identified as cache miss storm - 10:08 - Enabled cache warming - 10:12 - Latency normalized ## Root Cause Full cache flush for minor config update caused thundering herd. ## Fix - Immediate: Enabled cache warming - Long-term: Implement partial cache invalidation (ENG-999) ## Lessons Don't full-flush cache in production; use targeted invalidation. ``` ## Facilitation Guide ### Running a Postmortem Meeting ```markdown ## Meeting Structure (60 minutes) ### 1. Opening (5 min) - Remind everyone of blameless culture - "We're here to learn, not to blame" - Review meeting norms ### 2. Timeline Review (15 min) - Walk through events chronologically - Ask clarifying questions - Identify gaps in timeline ### 3. Analysis Discussion (20 min) - What failed? - Why did it fail? - What conditions allowed this? - What would have prevented it? ### 4. Action Items (15 min) - Brainstorm improvements - Prioritize by impact and effort - Assign owners and due dates ### 5. Closing (5 min) - Summarize key learnings - Confirm action item owners - Schedule follow-up if needed ## Facilitation Tips - Keep discussion on track - Redirect blame to systems - Encourage quiet participants - Document dissenting views - Time-box tangents ``` ## Anti-Patterns to Avoid | Anti-Pattern | Problem | Better Approach | | ----------------------- | -------------------------- | ------------------------------- | | **Blame game** | Shuts down learning | Focus on systems | | **Shallow analysis** | Doesn't prevent recurrence | Ask "why" 5 times | | **No action items** | Waste of time | Always have concrete next steps | | **Unrealistic actions** | Never completed | Scope to achievable tasks | | **No follow-up** | Actions forgotten | Track in ticketing system | ## Best Practices ### Do's - **Start immediately** - Memory fades fast - **Be specific** - Exact times, exact errors - **Include graphs** - Visual evidence - **Assign owners** - No orphan action items - **Share widely** - Organizational learning ### Don'ts - **Don't name and shame** - Ever - **Don't skip small incidents** - They reveal patterns - **Don't make it a blame doc** - That kills learning - **Don't create busywork** - Actions should be meaningful - **Don't skip follow-up** - Verify actions completed ## Resources - [Google SRE - Postmortem Culture](https://sre.google/sre-book/postmortem-culture/) - [Etsy's Blameless Postmortems](https://codeascraft.com/2012/05/22/blameless-postmortems/) - [PagerDuty Postmortem Guide](https://postmortems.pagerduty.com/)
πŸ‘0
πŸ‘οΈ0
πŸ€– Auto-discovered
πŸ€–system promptβ€’7 months ago

mtls-configuration

Configure mutual TLS (mTLS) for zero-trust service-to-service

architecture
⭐1
# mTLS Configuration Comprehensive guide to implementing mutual TLS for zero-trust service mesh communication. ## When to Use This Skill - Implementing zero-trust networking - Securing service-to-service communication - Certificate rotation and management - Debugging TLS handshake issues - Compliance requirements (PCI-DSS, HIPAA) - Multi-cluster secure communication ## Core Concepts ### 1. mTLS Flow ``` β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”‚ Service β”‚ β”‚ Service β”‚ β”‚ A β”‚ β”‚ B β”‚ β””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” TLS Handshake β”Œβ”€β”€β”€β”€β”΄β”€β”€β”€β”€β” β”‚ Proxy │◄───────────────────────────►│ Proxy β”‚ β”‚(Sidecar)β”‚ 1. ClientHello β”‚(Sidecar)β”‚ β”‚ β”‚ 2. ServerHello + Cert β”‚ β”‚ β”‚ β”‚ 3. Client Cert β”‚ β”‚ β”‚ β”‚ 4. Verify Both Certs β”‚ β”‚ β”‚ β”‚ 5. Encrypted Channel 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. Certificate Hierarchy ``` Root CA (Self-signed, long-lived) β”‚ β”œβ”€β”€ Intermediate CA (Cluster-level) β”‚ β”‚ β”‚ β”œβ”€β”€ Workload Cert (Service A) β”‚ └── Workload Cert (Service B) β”‚ └── Intermediate CA (Multi-cluster) β”‚ └── Cross-cluster certs ``` ## Templates ### Template 1: Istio mTLS (Strict Mode) ```yaml # Enable strict mTLS mesh-wide apiVersion: security.istio.io/v1beta1 kind: PeerAuthentication metadata: name: default namespace: istio-system spec: mtls: mode: STRICT --- # Namespace-level override (permissive for migration) apiVersion: security.istio.io/v1beta1 kind: PeerAuthentication metadata: name: default namespace: legacy-namespace spec: mtls: mode: PERMISSIVE --- # Workload-specific policy apiVersion: security.istio.io/v1beta1 kind: PeerAuthentication metadata: name: payment-service namespace: production spec: selector: matchLabels: app: payment-service mtls: mode: STRICT portLevelMtls: 8080: mode: STRICT 9090: mode: DISABLE # Metrics port, no mTLS ``` ### Template 2: Istio Destination Rule for mTLS ```yaml apiVersion: networking.istio.io/v1beta1 kind: DestinationRule metadata: name: default namespace: istio-system spec: host: "*.local" trafficPolicy: tls: mode: ISTIO_MUTUAL --- # TLS to external service apiVersion: networking.istio.io/v1beta1 kind: DestinationRule metadata: name: external-api spec: host: api.external.com trafficPolicy: tls: mode: SIMPLE caCertificates: /etc/certs/external-ca.pem --- # Mutual TLS to external service apiVersion: networking.istio.io/v1beta1 kind: DestinationRule metadata: name: partner-api spec: host: api.partner.com trafficPolicy: tls: mode: MUTUAL clientCertificate: /etc/certs/client.pem privateKey: /etc/certs/client-key.pem caCertificates: /etc/certs/partner-ca.pem ``` ### Template 3: Cert-Manager with Istio ```yaml # Install cert-manager issuer for Istio apiVersion: cert-manager.io/v1 kind: ClusterIssuer metadata: name: istio-ca spec: ca: secretName: istio-ca-secret --- # Create Istio CA secret apiVersion: v1 kind: Secret metadata: name: istio-ca-secret namespace: cert-manager type: kubernetes.io/tls data: tls.crt: <base64-encoded-ca-cert> tls.key: <base64-encoded-ca-key> --- # Certificate for workload apiVersion: cert-manager.io/v1 kind: Certificate metadata: name: my-service-cert namespace: my-namespace spec: secretName: my-service-tls duration: 24h renewBefore: 8h issuerRef: name: istio-ca kind: ClusterIssuer commonName: my-service.my-namespace.svc.cluster.local dnsNames: - my-service - my-service.my-namespace - my-service.my-namespace.svc - my-service.my-namespace.svc.cluster.local usages: - server auth - client auth ``` ### Template 4: SPIFFE/SPIRE Integration ```yaml # SPIRE Server configuration apiVersion: v1 kind: ConfigMap metadata: name: spire-server namespace: spire data: server.conf: | server { bind_address = "0.0.0.0" bind_port = "8081" trust_domain = "example.org" data_dir = "/run/spire/data" log_level = "INFO" ca_ttl = "168h" default_x509_svid_ttl = "1h" } plugins { DataStore "sql" { plugin_data { database_type = "sqlite3" connection_string = "/run/spire/data/datastore.sqlite3" } } NodeAttestor "k8s_psat" { plugin_data { clusters = { "demo-cluster" = { service_account_allow_list = ["spire:spire-agent"] } } } } KeyManager "memory" { plugin_data {} } UpstreamAuthority "disk" { plugin_data { key_file_path = "/run/spire/secrets/bootstrap.key" cert_file_path = "/run/spire/secrets/bootstrap.crt" } } } --- # SPIRE Agent DaemonSet (abbreviated) apiVersion: apps/v1 kind: DaemonSet metadata: name: spire-agent namespace: spire spec: selector: matchLabels: app: spire-agent template: spec: containers: - name: spire-agent image: ghcr.io/spiffe/spire-agent:1.8.0 volumeMounts: - name: spire-agent-socket mountPath: /run/spire/sockets volumes: - name: spire-agent-socket hostPath: path: /run/spire/sockets type: DirectoryOrCreate ``` ### Template 5: Linkerd mTLS (Automatic) ```yaml # Linkerd enables mTLS automatically # Verify with: # linkerd viz edges deployment -n my-namespace # For external services without mTLS apiVersion: policy.linkerd.io/v1beta1 kind: Server metadata: name: external-api namespace: my-namespace spec: podSelector: matchLabels: app: my-app port: external-api proxyProtocol: HTTP/1 # or TLS for passthrough --- # Skip TLS for specific port apiVersion: v1 kind: Service metadata: name: my-service annotations: config.linkerd.io/skip-outbound-ports: "3306" # MySQL ``` ## Certificate Rotation ```bash # Istio - Check certificate expiry istioctl proxy-config secret deploy/my-app -o json | \ jq '.dynamicActiveSecrets[0].secret.tlsCertificate.certificateChain.inlineBytes' | \ tr -d '"' | base64 -d | openssl x509 -text -noout # Force certificate rotation kubectl rollout restart deployment/my-app # Check Linkerd identity linkerd identity -n my-namespace ``` ## Debugging mTLS Issues ```bash # Istio - Check if mTLS is enabled istioctl authn tls-check my-service.my-namespace.svc.cluster.local # Verify peer authentication kubectl get peerauthentication --all-namespaces # Check destination rules kubectl get destinationrule --all-namespaces # Debug TLS handshake istioctl proxy-config log deploy/my-app --level debug kubectl logs deploy/my-app -c istio-proxy | grep -i tls # Linkerd - Check mTLS status linkerd viz edges deployment -n my-namespace linkerd viz tap deploy/my-app --to deploy/my-backend ``` ## Best Practices ### Do's - **Start with PERMISSIVE** - Migrate gradually to STRICT - **Monitor certificate expiry** - Set up alerts - **Use short-lived certs** - 24h or less for workloads - **Rotate CA periodically** - Plan for CA rotation - **Log TLS errors** - For debugging and audit ### Don'ts - **Don't disable mTLS** - For convenience in production - **Don't ignore cert expiry** - Automate rotation - **Don't use self-signed certs** - Use proper CA hierarchy - **Don't skip verification** - Verify the full chain ## Resources - [Istio Security](https://istio.io/latest/docs/concepts/security/) - [SPIFFE/SPIRE](https://spiffe.io/) - [cert-manager](https://cert-manager.io/) - [Zero Trust Architecture (NIST)](https://www.nist.gov/publications/zero-trust-architecture)
πŸ‘0
πŸ‘οΈ0
πŸ€– Auto-discovered
πŸ€–system promptβ€’7 months ago

team-composition-patterns

Design optimal agent team compositions with sizing heuristics,

coding
⭐1
# Team Composition Patterns Best practices for composing multi-agent teams, selecting team sizes, choosing agent types, and configuring display modes for Claude Code's Agent Teams feature. ## When to Use This Skill - Deciding how many teammates to spawn for a task - Choosing between preset team configurations - Selecting the right agent type (subagent_type) for each role - Configuring teammate display modes (tmux, iTerm2, in-process) - Building custom team compositions for non-standard workflows ## Team Sizing Heuristics | Complexity | Team Size | When to Use | | ------------ | --------- | ----------------------------------------------------------- | | Simple | 1-2 | Single-dimension review, isolated bug, small feature | | Moderate | 2-3 | Multi-file changes, 2-3 concerns, medium features | | Complex | 3-4 | Cross-cutting concerns, large features, deep debugging | | Very Complex | 4-5 | Full-stack features, comprehensive reviews, systemic issues | **Rule of thumb**: Start with the smallest team that covers all required dimensions. Adding teammates increases coordination overhead. ## Preset Team Compositions ### Review Team - **Size**: 3 reviewers - **Agents**: 3x `team-reviewer` - **Default dimensions**: security, performance, architecture - **Use when**: Code changes need multi-dimensional quality assessment ### Debug Team - **Size**: 3 investigators - **Agents**: 3x `team-debugger` - **Default hypotheses**: 3 competing hypotheses - **Use when**: Bug has multiple plausible root causes ### Feature Team - **Size**: 3 (1 lead + 2 implementers) - **Agents**: 1x `team-lead` + 2x `team-implementer` - **Use when**: Feature can be decomposed into parallel work streams ### Fullstack Team - **Size**: 4 (1 lead + 3 implementers) - **Agents**: 1x `team-lead` + 1x frontend `team-implementer` + 1x backend `team-implementer` + 1x test `team-implementer` - **Use when**: Feature spans frontend, backend, and test layers ### Research Team - **Size**: 3 researchers - **Agents**: 3x `general-purpose` - **Default areas**: Each assigned a different research question, module, or topic - **Capabilities**: Codebase search (Grep, Glob, Read), web search (WebSearch, WebFetch) - **Use when**: Need to understand a codebase, research libraries, compare approaches, or gather information from code and web sources in parallel ### Security Team - **Size**: 4 reviewers - **Agents**: 4x `team-reviewer` - **Default dimensions**: OWASP/vulnerabilities, auth/access control, dependencies/supply chain, secrets/configuration - **Use when**: Comprehensive security audit covering multiple attack surfaces ### Migration Team - **Size**: 4 (1 lead + 2 implementers + 1 reviewer) - **Agents**: 1x `team-lead` + 2x `team-implementer` + 1x `team-reviewer` - **Use when**: Large codebase migration (framework upgrade, language port, API version bump) requiring parallel work with correctness verification ## Agent Type Selection When spawning teammates with the Task tool, choose `subagent_type` based on what tools the teammate needs: | Agent Type | Tools Available | Use For | | ------------------------------ | ----------------------------------------- | ---------------------------------------------------------- | | `general-purpose` | All tools (Read, Write, Edit, Bash, etc.) | Implementation, debugging, any task requiring file changes | | `Explore` | Read-only tools (Read, Grep, Glob) | Research, code exploration, analysis | | `Plan` | Read-only tools | Architecture planning, task decomposition | | `agent-teams:team-reviewer` | All tools | Code review with structured findings | | `agent-teams:team-debugger` | All tools | Hypothesis-driven investigation | | `agent-teams:team-implementer` | All tools | Building features within file ownership boundaries | | `agent-teams:team-lead` | All tools | Team orchestration and coordination | **Key distinction**: Read-only agents (Explore, Plan) cannot modify files. Never assign implementation tasks to read-only agents. ## Display Mode Configuration Configure in `~/.claude/settings.json`: ```json { "teammateMode": "tmux" } ``` | Mode | Behavior | Best For | | -------------- | ------------------------------ | ------------------------------------------------- | | `"tmux"` | Each teammate in a tmux pane | Development workflows, monitoring multiple agents | | `"iterm2"` | Each teammate in an iTerm2 tab | macOS users who prefer iTerm2 | | `"in-process"` | All teammates in same process | Simple tasks, CI/CD environments | ## Custom Team Guidelines When building custom teams: 1. **Every team needs a coordinator** β€” Either designate a `team-lead` or have the user coordinate directly 2. **Match roles to agent types** β€” Use specialized agents (reviewer, debugger, implementer) when available 3. **Avoid duplicate roles** β€” Two agents doing the same thing wastes resources 4. **Define boundaries upfront** β€” Each teammate needs clear ownership of files or responsibilities 5. **Keep it small** β€” 2-4 teammates is the sweet spot; 5+ requires significant coordination overhead
πŸ‘0
πŸ‘οΈ0
πŸ€– Auto-discovered