# Modu AI Moai Adk Moai Workflow Jit Docs > Quick Reference (30 seconds) - Skill: `tomevault-io/modu-ai-moai-adk-moai-workflow-jit-docs` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/modu-ai-moai-adk-moai-workflow-jit-docs` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/modu-ai-moai-adk-moai-workflow-jit-docs/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Productivity - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/modu-ai-moai-adk-moai-workflow-jit-docs --- ## 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 comprehensive answers from diverse materials Context Persistence: - Remember documentation loaded earlier in conversation - Avoid redundant loading of same documentation - Build cumulative knowledge through session Proactive Loading: - Anticipate documentation needs based on conversation flow - Pre-load related topics when discussing complex features - Suggest relevant documentation before user asks --- ## Works Well With Agents: - workflow-docs: Documentation generation - core-planner: Documentation planning - workflow-spec: SPEC documentation Skills: - moai-docs-generation: Documentation generation - moai-workflow-docs: Documentation validation - moai-library-nextra: Nextra documentation Commands: - /moai:3-sync: Documentation synchronization - /moai:9-feedback: Documentation improvements --- > Converted and distributed by [TomeVault](https://tomevault.io/claim/modu-ai) — claim your Tome and manage your conversions.