docs-seeker

Intelligent discovery and analysis of technical documentation through multiple strategies:

1. llms.txt-first: Search for standardized AI-friendly documentation
2. Repository analysis: Use Repomix to analyze GitHub repositories
3. Parallel exploration: Deploy multiple Explorer agents for comprehensive coverage
4. Fallback research: Use Researcher agents when other methods unavailable

Phase 1: Initial Discovery

1. Identify target

- Extract library/framework name from user request

- Note version requirements (default: latest)

- Clarify scope if ambiguous

- Identify if target is GitHub repository or website

1. Search for llms.txt (PRIORITIZE context7.com)

First: Try context7.com patterns

For GitHub repositories:

```

Pattern: https://context7.com/{org}/{repo}/llms.txt

Examples:

- https://github.com/imagick/imagick → https://context7.com/imagick/imagick/llms.txt

- https://github.com/vercel/next.js → https://context7.com/vercel/next.js/llms.txt

- https://github.com/better-auth/better-auth → https://context7.com/better-auth/better-auth/llms.txt

```

For websites:

```

Pattern: https://context7.com/websites/{normalized-domain-path}/llms.txt

Examples:

- https://docs.imgix.com/ → https://context7.com/websites/imgix/llms.txt

- https://docs.byteplus.com/en/docs/ModelArk/ → https://context7.com/websites/byteplus_en_modelark/llms.txt

- https://docs.haystack.deepset.ai/docs → https://context7.com/websites/haystack_deepset_ai/llms.txt

- https://ffmpeg.org/doxygen/8.0/ → https://context7.com/websites/ffmpeg_doxygen_8_0/llms.txt

```

Topic-specific searches (when user asks about specific feature):

```

Pattern: https://context7.com/{path}/llms.txt?topic={query}

Examples:

- https://context7.com/shadcn-ui/ui/llms.txt?topic=date

- https://context7.com/shadcn-ui/ui/llms.txt?topic=button

- https://context7.com/vercel/next.js/llms.txt?topic=cache

- https://context7.com/websites/ffmpeg_doxygen_8_0/llms.txt?topic=compress

```

Fallback: Traditional llms.txt search

```

WebSearch: "[library name] llms.txt site:[docs domain]"

```

Common patterns:

- https://docs.[library].com/llms.txt

- https://[library].dev/llms.txt

- https://[library].io/llms.txt

→ Found? Proceed to Phase 2

→ Not found? Proceed to Phase 3

Phase 2: llms.txt Processing

Single URL:

• WebFetch to retrieve content
• Extract and present information

Multiple URLs (3+):

• CRITICAL: Launch multiple Explorer agents in parallel
• One agent per major documentation section (max 5 in first batch)
• Each agent reads assigned URLs
• Aggregate findings into consolidated report

Example:

```

Launch 3 Explorer agents simultaneously:

• Agent 1: getting-started.md, installation.md
• Agent 2: api-reference.md, core-concepts.md
• Agent 3: examples.md, best-practices.md

```

Phase 3: Repository Analysis

When llms.txt not found:

1. Find GitHub repository via WebSearch
2. Use Repomix to pack repository:

```bash

npm install -g repomix # if needed

git clone [repo-url] /tmp/docs-analysis

cd /tmp/docs-analysis

repomix --output repomix-output.xml

```

1. Read repomix-output.xml and extract documentation

Repomix benefits:

• Entire repository in single AI-friendly file
• Preserves directory structure
• Optimized for AI consumption

Phase 4: Fallback Research

When no GitHub repository exists:

• Launch multiple Researcher agents in parallel
• Focus areas: official docs, tutorials, API references, community guides
• Aggregate findings into consolidated report

• 1-3 URLs: Single Explorer agent
• 4-10 URLs: 3-5 Explorer agents (2-3 URLs each)
• 11+ URLs: 5-7 Explorer agents (prioritize most relevant)

Latest (default):

• Search without version specifier
• Use current documentation paths

Specific version:

• Include version in search: [library] v[version] llms.txt
• Check versioned paths: /v[version]/llms.txt
• For repositories: checkout specific tag/branch

```markdown

# Documentation for [Library] [Version]

• Method: [llms.txt / Repository / Research]
• URLs: [list of sources]
• Date accessed: [current date]

[Extracted relevant information organized by topic]

[Related links, examples, references]

[Any limitations, missing information, or caveats]

```

Tool selection:

• WebSearch → Find llms.txt URLs, GitHub repositories
• WebFetch → Read single documentation pages
• Task (Explore) → Multiple URLs, parallel exploration
• Task (Researcher) → Scattered documentation, diverse sources
• Repomix → Complete codebase analysis

Popular llms.txt locations (try context7.com first):

• Astro: https://context7.com/withastro/astro/llms.txt
• Next.js: https://context7.com/vercel/next.js/llms.txt
• Remix: https://context7.com/remix-run/remix/llms.txt
• shadcn/ui: https://context7.com/shadcn-ui/ui/llms.txt
• Better Auth: https://context7.com/better-auth/better-auth/llms.txt

Fallback to official sites if context7.com unavailable:

• Astro: https://docs.astro.build/llms.txt
• Next.js: https://nextjs.org/llms.txt
• Remix: https://remix.run/llms.txt
• SvelteKit: https://kit.svelte.dev/llms.txt

• llms.txt not accessible → Try alternative domains → Repository analysis
• Repository not found → Search official website → Use Researcher agents
• Repomix fails → Try /docs directory only → Manual exploration
• Multiple conflicting sources → Prioritize official → Note versions

1. Prioritize context7.com for llms.txt — Most comprehensive and up-to-date aggregator
2. Use topic parameters when applicable — Enables targeted searches with ?topic=...
3. Use parallel agents aggressively — Faster results, better coverage
4. Verify official sources as fallback — Use when context7.com unavailable
5. Report methodology — Tell user which approach was used
6. Handle versions explicitly — Don't assume latest

For comprehensive guides, examples, and best practices:

Workflows:

• [WORKFLOWS.md](./WORKFLOWS.md) — Detailed workflow examples and strategies

Reference guides:

• [Tool Selection](./references/tool-selection.md) — Complete guide to choosing and using tools
• [Documentation Sources](./references/documentation-sources.md) — Common sources and patterns across ecosystems
• [Error Handling](./references/error-handling.md) — Troubleshooting and resolution strategies
• [Best Practices](./references/best-practices.md) — 8 essential principles for effective discovery
• [Performance](./references/performance.md) — Optimization techniques and benchmarks
• [Limitations](./references/limitations.md) — Boundaries and success criteria