Skip to main content

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

Aller à l'installation

Informations de source

Dépôt
LongLeo287/SEOSONA-OS
Dernière activité de la source
4 août 2026 à 05:01
Langue détectée de SKILL.md
anglais
Étoiles
2
Forks
1

Options d'installation

Le prompt qui vérifie d'abord la source est sélectionné par défaut. Vous pouvez passer à une commande directe ou télécharger une copie locale.

Vérifiez les fichiers source

Lisez SKILL.md et les fichiers associés affichés par SkillsMP avant de décider de l'installer.

Explorateur de fichiers
17 fichiers

Affichage de SKILL.md

SKILL.md
Instructions source · Aperçu en lecture seule
name
docs-seeker
description
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
argument-hint
[library-name] [topic]
metadata
{"author":"claudekit","version":"3.1.0"}
# 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` > `.opencode/skills/docs-seeker/.env` > `.opencode/skills/.env` > `.opencode/.env` See `.env.example` for configuration options.
Voir sur GitHub