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Documentation Lookup

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JasonxzWen
documentation-lookup

Load when a task needs current library, framework, SDK, API, CLI, or cloud-service documentation; fetch docs instead of relying on training data or ordinary repo evidence.

Overview

PublisherJasonxzWen
Repositoryharness-hub
Skill namedocumentation-lookup
Stars
71
Forks
0
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by JasonxzWen on GitHub. Read the source before you install it.

Installation

Install the Documentation Lookup AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/JasonxzWen/harness-hub.git /tmp/harness-hub
mkdir -p .claude/skills
cp -r /tmp/harness-hub/skills/documentation-lookup .claude/skills/documentation-lookup
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Documentation Lookup in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Documentation Lookup on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Documentation Lookup is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Documentation Lookup (Context7)

When the user asks about libraries, frameworks, or APIs, fetch current documentation via the Context7 MCP (tools resolve-library-id and query-docs) instead of relying on training data.

Core Concepts

  • Context7: MCP server that exposes live documentation; use it instead of training data for libraries and APIs.
  • resolve-library-id: Returns Context7-compatible library IDs (e.g. /vercel/next.js) from a library name and query.
  • query-docs: Fetches documentation and code snippets for a given library ID and question. Always call resolve-library-id first to get a valid library ID.

When to use

Activate when the user:

  • Asks setup or configuration questions (e.g. "How do I configure Next.js middleware?")
  • Requests code that depends on a library ("Write a Prisma query for...")
  • Needs API or reference information ("What are the Supabase auth methods?")
  • Mentions specific frameworks or libraries (React, Vue, Svelte, Express, Tailwind, Prisma, Supabase, etc.)

Use this skill whenever the request depends on accurate, up-to-date behavior of a library, framework, or API. Applies across harnesses that have the Context7 MCP configured.

How it works

Step 1: Resolve the Library ID

Call the resolve-library-id MCP tool with:

  • libraryName: The library or product name taken from the user's question (e.g. Next.js, Prisma, Supabase).
  • query: The user's full question. This improves relevance ranking of results.

You must obtain a Context7-compatible library ID (format /org/project or /org/project/version) before querying docs. Do not call query-docs without a valid library ID from this step.

Step 2: Select the Best Match

From the resolution results, choose one result using:

  • Name match: Prefer exact or closest match to what the user asked for.
  • Benchmark score: Higher scores indicate better documentation quality (100 is highest).
  • Source reputation: Prefer High or Medium reputation when available.
  • Version: If the user specified a version (e.g. "React 19", "Next.js 15"), prefer a version-specific library ID if listed (e.g. /org/project/v1.2.0).

Step 3: Fetch the Documentation

Call the query-docs MCP tool with:

  • libraryId: The selected Context7 library ID from Step 2 (e.g. /vercel/next.js).
  • query: The user's specific question or task. Be specific to get relevant snippets.

Limit: do not call query-docs (or resolve-library-id) more than 3 times per question. If the answer is unclear after 3 calls, state the uncertainty and use the best information you have rather than guessing.

Step 4: Use the Documentation

  • Answer the user's question using the fetched, current information.
  • Include relevant code examples from the docs when helpful.
  • Cite the library or version when it matters (e.g. "In Next.js 15...").

Examples

Example: Next.js middleware

  1. Call resolve-library-id with libraryName: "Next.js", query: "How do I set up Next.js middleware?".
  2. From results, pick the best match (e.g. /vercel/next.js) by name and benchmark score.
  3. Call query-docs with libraryId: "/vercel/next.js", query: "How do I set up Next.js middleware?".
  4. Use the returned snippets and text to answer; include a minimal middleware.ts example from the docs if relevant.

Example: Prisma query

  1. Call resolve-library-id with libraryName: "Prisma", query: "How do I query with relations?".
  2. Select the official Prisma library ID (e.g. /prisma/prisma).
  3. Call query-docs with that libraryId and the query.
  4. Return the Prisma Client pattern (e.g. include or select) with a short code snippet from the docs.

Example: Supabase auth methods

  1. Call resolve-library-id with libraryName: "Supabase", query: "What are the auth methods?".
  2. Pick the Supabase docs library ID.
  3. Call query-docs; summarize the auth methods and show minimal examples from the fetched docs.

Best Practices

  • Be specific: Use the user's full question as the query where possible for better relevance.
  • Version awareness: When users mention versions, use version-specific library IDs from the resolve step when available.
  • Prefer official sources: When multiple matches exist, prefer official or primary packages over community forks.
  • No sensitive data: Redact API keys, passwords, tokens, and other secrets from any query sent to Context7. Treat the user's question as potentially containing secrets before passing it to resolve-library-id or query-docs.

Frequently asked questions

What does the Documentation Lookup AI skill do?

Load when a task needs current library, framework, SDK, API, CLI, or cloud-service documentation; fetch docs instead of relying on training data or ordinary repo evidence.

Why use Documentation Lookup on TypingMind?

Because you install it once and use it with any model. Documentation Lookup is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Documentation Lookup in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/JasonxzWen/harness-hub/tree/main/skills/documentation-lookup. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Documentation Lookup?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Documentation Lookup?

As many as you like. As long as a model supports skills, you can use Documentation Lookup with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Documentation Lookup AI skill free?

Yes. It is published on GitHub by JasonxzWen under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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