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Docs Seeker

  • 20 installs
  • 95 repo stars
  • Updated July 7, 2026
  • binjuhor/shadcn-lar

docs-seeker is a Claude skill that searches technical documentation via executable scripts, detecting query type and fetching from llms.txt sources like context7.com.

About

This skill searches technical documentation using executable scripts that detect the query type, fetch content from llms.txt sources such as context7.com, and analyze the results. A developer uses it to find topic-specific docs, library or framework documentation, or to analyze a GitHub repository. It recommends an agent distribution strategy when multiple documentation URLs are returned.

  • Script-first documentation discovery using the llms.txt standard
  • Detects query type, fetches from context7.com, and analyzes results
  • Recommends an agent distribution strategy for multi-URL results

Docs Seeker by the numbers

  • 20 all-time installs (skills.sh)
  • Ranked #999 of 1,879 Documentation skills by installs in the Skillselion catalog
  • Data as of Aug 2, 2026 (Skillselion catalog sync)
At a glance

docs-seeker capabilities & compatibility

Capabilities
documentation · research · web search
Use cases
documentation · research · web search
From the docs

What docs-seeker says it does

Script-first** documentation discovery using llms.txt standard.
SKILL.md
Constructs context7.com URLs automatically
SKILL.md
Categorizes URLs (critical/important/supplementary)
SKILL.md
npx skills add https://github.com/binjuhor/shadcn-lar --skill docs-seeker

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Listed on Skillselion
Installs20
repo stars95
Last updatedJuly 7, 2026
Repositorybinjuhor/shadcn-lar

What it does

Discover and fetch technical documentation for a library, framework, or topic via the llms.txt standard.

Who is it for?

Developers who need to quickly find library, framework, or topic-specific documentation while coding.

Skip if: Users needing to author or generate new documentation rather than retrieve existing docs.

When should I use this skill?

You need topic-specific documentation, library or framework docs, GitHub repository analysis, or documentation discovery.

What you get

Relevant documentation is detected, fetched, and analyzed automatically with a recommended agent distribution strategy.

By the numbers

  • Three scripts: detect-topic.js, fetch-docs.js, analyze-llms-txt.js

Files

SKILL.mdMarkdownGitHub ↗

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.

Related skills

FAQ

How does docs-seeker find documentation?

It runs scripts that detect the query type, fetch docs from llms.txt sources such as context7.com, and analyze the returned URLs.

What does it do with multiple documentation URLs?

It categorizes URLs as critical, important, or supplementary and recommends an agent distribution strategy (1 agent, 3 agents, 7 agents, or phased).

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