moai-workflow-jit-docs
moai-workflow-jit-docs is a just-in-time documentation loader that retrieves relevant technical resources on demand by detecting user intent, keywords, and domain context. It searches local project files, official documentation, and web sources to provide implementation guidance, troubleshooting help, and best practices for frameworks, libraries, and tools mentioned in user conversations.
git clone --depth 1 https://github.com/modu-ai/moai-adk /tmp/moai-workflow-jit-docs && cp -r /tmp/moai-workflow-jit-docs/.moai/archive/skills/v3.0/moai-workflow-jit-docs ~/.claude/skills/moai-workflow-jit-docsSKILL.md
## Quick Reference (30 seconds) Purpose: Load relevant documentation on-demand based on user intent and context. Primary Tools: - WebSearch: Find latest documentation and resources online - WebFetch: Retrieve specific documentation pages - Context7 MCP: Access official library documentation (when available) - Read, Grep, Glob: Search local project documentation Trigger Patterns: - User asks specific technical questions - Technology keywords detected in conversation - Domain expertise required for task completion - Implementation guidance needed ## Implementation Guide ### Intent Detection The system recognizes documentation needs through several patterns: Question-Based Triggers: - When users ask specific implementation questions (e.g., "how do I implement JWT authentication?") - When users seek best practices or optimization guidance - When troubleshooting questions arise Technology-Specific Triggers: - Detection of framework names: FastAPI, React, PostgreSQL, Docker, Kubernetes - Detection of library names: pytest, TypeScript, GraphQL, Redis - Detection of tool names: npm, pip, cargo, maven Domain-Specific Triggers: - Authentication and authorization topics - Database and data modeling discussions - Performance optimization inquiries - Security-related questions Pattern-Based Triggers: - Implementation requests: "implement", "create", "build" - Architecture discussions: "design", "structure", "pattern" - Troubleshooting: "debug", "fix", "error", "not working" ### Documentation Sources The system retrieves documentation from multiple sources in priority order: Local Project Documentation (Highest Priority): - Check .moai/docs/ for project-specific documentation - Check .moai/specs/ for requirements and specifications - Check README.md for project overview - Check docs/ directory for comprehensive documentation Official Documentation Sources: - Use WebFetch to retrieve official framework documentation - Use Context7 MCP tools when available for library documentation - Access technology-specific official websites Community Resources: - Use WebSearch to find high-quality tutorials - Search for Stack Overflow solutions with high vote counts - Find GitHub discussions for specific issues Real-Time Web Research: - Use WebSearch with current year for latest information - Search for recent best practices and updates - Find new features and deprecation notices ### Loading Strategies Intent Analysis Process: - Identify technologies mentioned in user request - Determine domain areas relevant to the question - Classify question type (implementation, troubleshooting, conceptual) - Assess complexity to determine documentation depth needed Source Prioritization: - If local documentation exists: Load project-specific docs first - If official documentation available: Retrieve authoritative sources - If implementation examples needed: Search community resources - If latest information required: Perform web research Context-Aware Caching: - Cache retrieved documentation within session - Maintain relevance based on current conversation context - Remove outdated content when context shifts - Prioritize frequently accessed documentation ### Quality Assessment Content Quality Evaluation: - Authority: Official sources receive highest trust - Recency: Content within 12 months preferred for fast-moving technologies - Completeness: Documentation with examples ranked higher - Relevance: Match between content and user intent Relevance Ranking: - Calculate match between documentation content and user question - Weight authority (30%), recency (25%), completeness (25%), relevance (20%) - Return highest-scoring documentation first - Indicate confidence level in retrieved information ### Practical Workflows Authentication Implementation Workflow: - When user asks about authentication: Detect technologies (e.g., FastAPI, JWT) - Identify domains: authentication, security - Load FastAPI security documentation via WebFetch - Search for JWT best practices via WebSearch - Provide comprehensive guidance with source attribution Database Optimization Workflow: - When user asks about query performance: Detect database technology - Identify domain: performance, optimization - Load official database documentation - Search for optimization guides and tutorials - Provide actionable recommendations with sources New Technology Adoption Workflow: - When user introduces unfamiliar technology: Detect technology name - Load official getting started documentation - Search for migration guides if applicable - Find integration patterns with existing stack - Provide strategic adoption guidance ### Error Handling Network Failures: - If web search fails: Fall back to cached content - If WebFetch fails: Use local documentation if available - Indicate partial results when some sources unreachable Content Quality Issues: - If retrieved content seems outdated: Search for newer sources - If relevance unclear: Ask user for clarification - If conflicting information found: Present multiple sources with dates Relevance Mismatches: - If initial search yields poor results: Refine search query - If user context unclear: Request clarification before loading - If documentation gap exists: Acknowledge limitation ### Performance Optimization Caching Strategy: - Maintain session-level cache for frequently accessed docs - Keep project-specific documentation in memory - Evict stale content based on access time Efficient Loading: - Load documentation only when explicitly needed - Avoid preloading all possible documentation - Use targeted searches rather than broad queries Batch Processing: - Combine related searches when possible - Group documentation requests by technology - Process multiple sources in parallel when appropriate ## Advanced Patterns Multi-Source Aggregation: - Combine official documentation with community examples - Cross-reference multiple authoritative sources - Synthesize comprehens
Claude Code upstream change tracker -> moai-adk update plan + docs sync workflow (dev-only). Tracks new CC release notes, classifies changes by impact tier, cross-references official docs, generates update plan at .moai/research/ or .moai/specs/, and synchronizes docs-site 4-locale + README. NOT distributed to user projects.
GitHub Workflow - Manage issues and review PRs with Agent Teams (dev-only). NOT distributed to user projects.
MoAI-ADK production release via Enhanced GitHub Flow (CLAUDE.local.md §18). Creates release/vX.Y.Z branch, version bump, CHANGELOG (bilingual), PR to main, merge commit (NOT squash), then scripts/release.sh for tag + GoReleaser. Hotfix support via --hotfix flag. All git operations delegated to manager-git. Quality failures escalate to expert-debug. NOT distributed to user projects (dev-only).
Run the 7-phase /moai brain ideation workflow to convert ideas into validated proposals
Identify and safely remove dead code with test verification
Scan codebase and generate architecture documentation in codemaps/
Analyze test coverage, identify gaps, and generate missing tests
Hybrid design workflow — Claude Design import (path A) or code-based brand design (path B)