Vc Docs Seeker logo

Vc Docs Seeker

CommunityPopular
withkynam
vc-docs-seeker

Search library/framework documentation via llms.txt (context7.com). Use for API docs, GitHub repository analysis, technical documentation lookup, latest library features.

Overview

Publisherwithkynam
Repositoryvibecode-pro-max-kit
Skill namevc-docs-seeker
Stars
1.1K
Forks
234
Bundled files
15
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.

  • 15 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

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

Installation

Install the Vc Docs Seeker 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.
    https://github.com/withkynam/vibecode-pro-max-kit/tree/main/.claude/skills/vc-docs-seeker
  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/withkynam/vibecode-pro-max-kit.git /tmp/vibecode-pro-max-kit
mkdir -p .claude/skills
cp -r /tmp/vibecode-pro-max-kit/.claude/skills/vc-docs-seeker .claude/skills/vc-docs-seeker
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Vc Docs Seeker 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 Vc Docs Seeker 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 Vc Docs Seeker 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 Discovery

Output style: Follow process/development-protocols/communication-standards.md — answer-first, plain language, no unexplained jargon, TL;DR on long responses.

Overview

Use Context7 MCP as the default path for documentation lookup. The local scripts in this skill are fallback helpers for llms.txt-based discovery when Context7 does not cover the target cleanly.

Primary Workflow

Default workflow: Context7 first

  1. Resolve the library with Context7.
  2. Query the exact API/config/setup question with Context7.
  3. Only fall back to the scripts below when Context7 coverage is missing, incomplete, or the user explicitly wants llms.txt/repository-oriented discovery.

Fallback script workflow:

bash
# 1. DETECT query type (topic-specific vs general)
node scripts/detect-topic.js "<user query>"

# 2. FETCH documentation using script output
node scripts/fetch-docs.js "<user query>"

# 3. ANALYZE results (if multiple URLs returned)
cat llms.txt | node scripts/analyze-llms-txt.js -

Scripts handle URL construction, fallback chains, and error handling automatically.

Scripts

detect-topic.js - Classify query type

  • Identifies topic-specific vs general queries
  • Extracts library name + topic keyword
  • Returns JSON: {topic, library, isTopicSpecific}
  • Zero-token execution

fetch-docs.js - Retrieve documentation

  • Constructs context7.com URLs automatically
  • Handles fallback: topic → general → error
  • Outputs llms.txt content or error message
  • Zero-token execution

analyze-llms-txt.js - Process llms.txt

  • Categorizes URLs (critical/important/supplementary)
  • Recommends agent distribution (1 agent, 3 agents, 7 agents, phased)
  • Returns JSON with strategy
  • Zero-token execution

Workflow References

Topic-Specific Search - Fastest path (10-15s)

General Library Search - Comprehensive coverage (30-60s)

Repository Analysis - Fallback strategy

References

context7-patterns.md - URL patterns, known repositories

errors.md - Error handling, fallback strategies

advanced.md - Edge cases, versioning, multi-language

Execution Principles

  1. Context7 first - Use Context7 MCP before any local script fallback
  2. Zero-token overhead - Scripts run without context loading
  3. Automatic fallback - Scripts handle topic → general → error chains
  4. Progressive disclosure - Load workflows/references only when needed
  5. Scripts are fallback helpers - Use them only when Context7 coverage is missing or the user wants llms.txt-style discovery

Quick Start

Topic query: "How do I use date picker in shadcn?"

bash
node scripts/detect-topic.js "<query>"  # → {topic, library, isTopicSpecific}
node scripts/fetch-docs.js "<query>"    # → 2-3 URLs
# Use Context7 results first; only use script-discovered URLs when you intentionally fall back

General query: "Documentation for Next.js"

bash
node scripts/detect-topic.js "<query>"         # → {isTopicSpecific: false}
node scripts/fetch-docs.js "<query>"           # → 8+ URLs
cat llms.txt | node scripts/analyze-llms-txt.js -  # → {totalUrls, distribution}
# Use Context7 results first; only use script-discovered URLs when fallback discovery is required

Environment

Scripts load .env: process.env > .claude/skills/vc-docs-seeker/.env > .claude/skills/.env > .claude/.env

Optional env vars: CONTEXT7_API_KEY (Bearer token for Context7 API), DEBUG=true (verbose logging).

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Vc Docs Seeker AI skill do?

Search library/framework documentation via llms.txt (context7.com). Use for API docs, GitHub repository analysis, technical documentation lookup, latest library features.

Why use Vc Docs Seeker on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/withkynam/vibecode-pro-max-kit/tree/main/.claude/skills/vc-docs-seeker. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Vc Docs Seeker?

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 Vc Docs Seeker?

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

Is the Vc Docs Seeker AI skill free?

Yes. It is published on GitHub by withkynam 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇