moai-workflow-jit-docs

moai-workflow-jit-docs is a skill for Claude Code from modu-ai/moai-adk. It costs 42 tokens per session (1,912 once invoked), scanned A, original, Apache-2.0.

A just-in-time documentation workflow that finds and loads only the technical information relevant to a user's request and project. It can search project files, web documentation, and official library references.

In plain words
What is it for?
Use it when answering implementation questions, troubleshooting technology, or looking up guidance for frameworks, libraries, tools, and domain-specific features.
Why use it?
It reduces the need to load large amounts of documentation before knowing what information is relevant.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter. Also seen: mentions Claude Code.

Good fit Use it when answering implementation questions, troubleshooting technology, or looking up guidance for frameworks, libraries, tools, and domain-specific features.

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Install with agentmods
npx agentmods add skills/modu-ai/moai-adk/moai-workflow-jit-docs
About the project

MoAI-ADK is a Go-based harness that organizes and verifies Claude Code work across planning, implementation, synchronization, and review stages. Developers use it to structure agentic coding tasks, apply quality gates, and route work across language models, while the catalogue entries extend its workflow with skills, hooks, commands, MCP servers, instructions, and settings.

modu-ai/moai-adk · 1,204 stars · on GitHub · adk.mo.ai.kr

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add modu-ai/moai-adk --skill moai-workflow-jit-docs
Clone the repo
git clone --depth 1 https://github.com/modu-ai/moai-adk

Made for: Claude Code.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for moai-workflow-jit-docs

README.md
[![agentmods](https://agentmods.dev/badge/skills/modu-ai/moai-adk/moai-workflow-jit-docs/github.svg)](https://agentmods.dev/skills/modu-ai/moai-adk/moai-workflow-jit-docs)
Your own site
<a href="https://agentmods.dev/skills/modu-ai/moai-adk/moai-workflow-jit-docs"><img src="https://agentmods.dev/badge/skills/modu-ai/moai-adk/moai-workflow-jit-docs/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for moai-workflow-jit-docs

Your own site · 80×15
<a href="https://agentmods.dev/skills/modu-ai/moai-adk/moai-workflow-jit-docs"><img src="https://agentmods.dev/badge/skills/modu-ai/moai-adk/moai-workflow-jit-docs.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 42 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,912 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00042 $0.01912
Opus 5 $0.00021 $0.00956
Sonnet 5 $0.00008 $0.00382
Haiku 4.5 $0.00004 $0.00191

Measured 6d ago against content hash 7827a3466fd7, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

moai-workflow-jit-docs scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.moai/archive/skills/v3.0/moai-workflow-jit-docs/SKILL.md · 290 lines

How it starts

The opening of the file, as written. The whole thing — 290 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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

Read the full file on GitHub · 290 lines

Files

What ships with it

5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 6d ago First seen · 290 lines · 42 tokens per session scan A 7827a3466fd7

Subscribe to this mod's changes

moai-workflow-jit-docs is a skill published in the GitHub repository modu-ai/moai-adk (1,204 stars, last pushed today), licensed Apache-2.0. It adds 42 tokens to every session and 1,912 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.

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