🎯

moai-workflow-jit-docs

🎯Skill

from modu-ai/moai-rank

VibeIndex|
What it does

Intelligently discovers, loads, and caches relevant documentation in real-time based on user intent and project context.

πŸ“¦

Part of

modu-ai/moai-rank(43 items)

moai-workflow-jit-docs

Installation

Install ScriptRun install script
curl -LsSf https://modu-ai.github.io/moai-adk/install.sh | sh
Install ScriptRun install script
curl -LsSf https://astral.sh/uv/install.sh | sh
git cloneClone repository
git clone https://github.com/your-org/moai-rank.git
BunRun with Bun
bun install
BunRun with Bun
bun run db:generate

+ 4 more commands

πŸ“– Extracted from docs: modu-ai/moai-rank
6Installs
-
AddedFeb 4, 2026

Skill Details

SKILL.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 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

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