smithery.ai

ck:docs-seeker

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

First seen Mar 20, 2026

Installation

$ npx skills add https://smithery.ai

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More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

Claude Code Declared
Cursor Not declared
Codex Not declared
GitHub Copilot Not declared
Windsurf Not declared
Gemini CLI Not declared
Cline Not declared
OpenCode Not declared

Skill metadata

Parsed from SKILL.md frontmatter.

Version3.1.0
Declared agents claude-code
More metadata
author
claudekit
version
3.1.0

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 3,275 B
  • docs SUMMARY.md 387 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 7 installs

SKILL.md

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](./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?"

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

See .env.example for configuration options.