hongbietcode/synthetic-claude/docs-seeker

Claude Code and agent skill by hongbietcode/synthetic-claude

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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

Install

npx vskill@latest install hongbietcode/synthetic-claude/docs-seeker

SKILL.md

The same SKILL.md was published earlier as khoi1009/vibecode-with-multi-agent-longcot/docs-seeker.

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 > .claude/skills/docs-seeker/.env > .claude/skills/.env > .claude/.env

See .env.example for configuration options.

Security

  • Security scanPassed, 90 of 100Pattern checks on the skill's files.
  • Model reviewPassed

Quality evaluation

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