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Find Docs

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mxyhi
find-docs

Retrieves up-to-date documentation, API references, and code examples for any developer technology. Use this skill whenever the user asks about a specific library, framework, SDK, CLI tool, or cloud service — even for well-known ones like React, Next.js, Prisma, Express, Tailwind, Django, or Spring Boot. Your training data may not reflect recent API changes or version updates. Always use for: API syntax questions, configuration options, version migration issues, "how do I" questions mentioning a library name, debugging that involves library-specific behavior, setup instructions, and CLI tool usage. Use even when you think you know the answer — do not rely on training data for API details, signatures, or configuration options as they are frequently outdated. Always verify against current docs. Prefer this over web search for library documentation and API details.

Overview

Publishermxyhi
Repositoryok-skills
Skill namefind-docs
Stars
490
Forks
46
Bundled files
Instructions only
LicenseApache-2.0
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 mxyhi on GitHub. Read the source before you install it.

Installation

Install the Find Docs 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/mxyhi/ok-skills.git /tmp/ok-skills
mkdir -p .claude/skills
cp -r /tmp/ok-skills/find-docs .claude/skills/find-docs
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Find Docs 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 Find Docs 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 Find Docs 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

Retrieve current documentation and code examples for any library using the Context7 CLI.

Run commands with npx ctx7@latest so setup always uses the latest CLI without a global install:

bash
npx ctx7@latest library <name> "<query>"
npx ctx7@latest docs <libraryId> "<query>"

Optionally install globally if you prefer a bare ctx7 command:

bash
npm install -g ctx7@latest

Workflow

Two-step process: resolve the library name to an ID, then query docs with that ID.

bash
# Step 1: Resolve library ID
npx ctx7@latest library <name> "<query>"

# Step 2: Query documentation
npx ctx7@latest docs <libraryId> "<query>"

You MUST call library first to obtain a valid library ID UNLESS the user explicitly provides a library ID in the format /org/project or /org/project/version.

IMPORTANT: Do not run these commands more than 3 times per question. If you cannot find what you need after 3 attempts, use the best result you have.

Step 1: Resolve a Library

Resolves a package/product name to a Context7-compatible library ID and returns matching libraries.

bash
npx ctx7@latest library React "How to clean up useEffect with async operations"
npx ctx7@latest library "Next.js" "How to set up app router with middleware"
npx ctx7@latest library Prisma "How to define one-to-many relations with cascade delete"

Use the official library name with proper punctuation (e.g., "Next.js" not "nextjs", "Customer.io" not "customerio", "Three.js" not "threejs"). If results look wrong, try alternate spellings such as next.js before changing the query.

Always pass a query argument — it is required and directly affects result ranking. Use the user's intent to form the query, which helps disambiguate when multiple libraries share a similar name. Do not include any sensitive or confidential information such as API keys, passwords, credentials, personal data, or proprietary code in your query.

Result fields

Each result includes:

  • Library ID — Context7-compatible identifier (format: /org/project)
  • Name — Library or package name
  • Description — Short summary
  • Code Snippets — Number of available code examples
  • Source Reputation — Authority indicator (High, Medium, Low, or Unknown)
  • Benchmark Score — Quality indicator (100 is the highest score)
  • Versions — List of versions if available. Use one of those versions if the user provides a version in their query. The format is /org/project/version.

Selection process

  1. Analyze the query to understand what library/package the user is looking for
  2. Select the most relevant match based on:
    • Name similarity to the query (exact matches prioritized)
    • Description relevance to the query's intent
    • Documentation coverage (prioritize libraries with higher Code Snippet counts)
    • Source reputation (consider libraries with High or Medium reputation more authoritative)
    • Benchmark score (higher is better, 100 is the maximum)
  3. If multiple good matches exist, acknowledge this but proceed with the most relevant one
  4. If no good matches exist, clearly state this and suggest query refinements
  5. For ambiguous queries, request clarification before proceeding with a best-guess match

Version-specific IDs

If the user mentions a specific version, use a version-specific library ID:

bash
# General (latest indexed)
npx ctx7@latest docs /vercel/next.js "How to set up app router"

# Version-specific
npx ctx7@latest docs /vercel/next.js/v14.3.0-canary.87 "How to set up app router"

The available versions are listed in the library command output. Use the closest match to what the user specified.

Step 2: Query Documentation

Retrieves up-to-date documentation and code examples for the resolved library.

bash
npx ctx7@latest docs /facebook/react "How to clean up useEffect with async operations"
npx ctx7@latest docs /vercel/next.js "How to add authentication middleware to app router"
npx ctx7@latest docs /prisma/prisma "How to define one-to-many relations with cascade delete"

Writing good queries

The query directly affects the quality of results. Be specific and include relevant details, but keep each query to one topic — if the question spans multiple distinct concepts, run a separate docs command per concept instead of combining them, unless the question is about how the concepts interact. Do not include any sensitive or confidential information such as API keys, passwords, credentials, personal data, or proprietary code in your query.

QualityExample
Good"How to set up authentication with JWT in Express.js"
Good"React useEffect cleanup function with async operations"
Bad (too vague)"auth"
Bad (too vague)"hooks"
Bad (too broad)"routing and auth and caching in Next.js"

Describe what to look up in the library's documentation, rather than the task to complete — vague one-word queries return generic results, and multi-topic queries dilute ranking and return shallow results for each topic.

The output contains two types of content: code snippets (titled, with language-tagged blocks) and info snippets (prose explanations with breadcrumb context).

Authentication

Works without authentication. For higher rate limits:

bash
# Option A: environment variable
export CONTEXT7_API_KEY=your_key

# Option B: OAuth login
npx ctx7@latest login

Error Handling

If a command fails with a quota error ("Monthly quota reached" or "quota exceeded"):

  1. Inform the user their Context7 quota is exhausted
  2. Suggest they authenticate for higher limits: npx ctx7@latest login
  3. If they cannot or choose not to authenticate, answer from training knowledge and clearly note it may be outdated

Do not silently fall back to training data — always tell the user why Context7 was not used.

Common Mistakes

  • Library IDs require a / prefix — /facebook/react not facebook/react
  • Always run npx ctx7@latest library first — npx ctx7@latest docs react "hooks" will fail without a valid ID
  • Use descriptive queries, not single words — "React useEffect cleanup function" not "hooks"
  • One topic per query — split "routing and auth and caching" into a separate docs command per concept, unless the question is about how they interact
  • Do not include sensitive information (API keys, passwords, credentials) in queries

Frequently asked questions

What does the Find Docs AI skill do?

Retrieves up-to-date documentation, API references, and code examples for any developer technology. Use this skill whenever the user asks about a specific library, framework, SDK, CLI tool, or cloud service — even for well-known ones like React, Next.js, Prisma, Express, Tailwind, Django, or Spring Boot. Your training data may not reflect recent API changes or version updates. Always use for: API syntax questions, configuration options, version migration issues, "how do I" questions mentioning a library name, debugging that involves library-specific behavior, setup instructions, and CLI too...

Why use Find Docs on TypingMind?

Because you install it once and use it with any model. Find Docs 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 Find Docs in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/mxyhi/ok-skills/tree/main/find-docs. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Find Docs?

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 Find Docs?

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

Is the Find Docs AI skill free?

Yes. It is published on GitHub by mxyhi under the Apache-2.0 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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