How do I install the Docs Seeker skill?

Run npx skillmds add majiayu000/docs-seeker-8 in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Docs Seeker safe to use?

This skill has not completed SkillMD's automated safety review yet. Capability flags: makes network calls, reads secrets. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Docs Seeker?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Docs Seeker free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Docs Seeker?

majiayu000 (@majiayu000) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Docs Seeker by majiayu000 — Agent Skill

Name: Docs Seeker
Rating: 4.5 (4 reviews)
Author: majiayu000

Docs Seeker

Search technical documentation using executable scripts to detect query type, fetch from llms.txt sources (context7.com), and analyze results. Use when user needs: (1) Topic-specific documentation (features/components/concepts), (2) Library/framework documentation, (3) GitHub repository analysis, (4) Documentation discovery with automated agent distribution strategy

majiayu000 a48757d 2 files · 4.2 KB Updated Sep 9, 2026 567 repo stars

File contents

Documentation Discovery via Scripts

Overview

Script-first documentation discovery using llms.txt standard.

Execute scripts to handle entire workflow - no manual URL construction needed.

Primary Workflow

ALWAYS execute scripts in this order:

# 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

Scripts first - Execute scripts instead of manual URL construction
Zero-token overhead - Scripts run without context loading
Automatic fallback - Scripts handle topic → general → error chains
Progressive disclosure - Load workflows/references only when needed
Agent distribution - Scripts recommend parallel agent strategy

Quick Start

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

node scripts/detect-topic.js "<query>"  # → {topic, library, isTopicSpecific}
node scripts/fetch-docs.js "<query>"    # → 2-3 URLs
# Read URLs with WebFetch

General query: "Documentation for Next.js"

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}
# Deploy agents per recommendation

Environment

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

See .env.example for configuration options.

---name: docs-seeker-8description: Search technical documentation using executable scripts to detect query type, fetch from llms.txt sources (context7.com), and analyze results. Use when user needs: (1) Topic-specific documentation (features/components/concepts), (2) Library/framework documentation, (3) GitHub repository analysis, (4) Documentation discovery with automated agent distribution strategy--- # Documentation Discovery via Scripts ## Overview **Script-first** documentation discovery using llms.txt standard. Execute scripts to handle entire workflow - no manual URL construction needed. ## Primary Workflow **ALWAYS execute scripts in this order:** ```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](./workflows/topic-search.md)** - Fastest path (10-15s) **[General Library Search](./workflows/library-search.md)** - Comprehensive coverage (30-60s) **[Repository Analysis](./workflows/repo-analysis.md)** - Fallback strategy ## References **[context7-patterns.md](./references/context7-patterns.md)** - URL patterns, known repositories **[errors.md](./references/errors.md)** - Error handling, fallback strategies **[advanced.md](./references/advanced.md)** - Edge cases, versioning, multi-language ## Execution Principles 1. **Scripts first** - Execute scripts instead of manual URL construction 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. **Agent distribution** - Scripts recommend parallel agent strategy ## 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 # Read URLs with WebFetch ``` **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} # Deploy agents per recommendation ``` ## Environment Scripts load `.env`: `process.env` > `.codex/skills/docs-seeker/.env` > `.codex/skills/.env` > `.codex/.env` See `.env.example` for configuration options.

Frequently asked questions