Docs Seeker

Documentation Discovery & Analysis

Overview

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

Core Workflow

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

2. 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:

   npm install -g repomix  # if needed
   git clone [repo-url] /tmp/docs-analysis
   cd /tmp/docs-analysis
   repomix --output repomix-output.xml

3. 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

Agent Distribution Guidelines

• 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)

Version Handling

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

Output Format

# Documentation for [Library] [Version]

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

## Key Information
[Extracted relevant information organized by topic]

## Additional Resources
[Related links, examples, references]

## Notes
[Any limitations, missing information, or caveats]

Quick Reference

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

Error Handling

• 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

Key Principles

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

Detailed Documentation

For comprehensive guides, examples, and best practices:

Workflows:

• WORKFLOWS.md — Detailed workflow examples and strategies

Reference guides:

• Tool Selection — Complete guide to choosing and using tools
• Documentation Sources — Common sources and patterns across ecosystems
• Error Handling — Troubleshooting and resolution strategies
• Best Practices — 8 essential principles for effective discovery
• Performance — Optimization techniques and benchmarks
• Limitations — Boundaries and success criteria

Best Practices

Essential principles and proven strategies for effective documentation discovery.

1. Prioritize context7.com for llms.txt

Why

• Comprehensive aggregator: Single source for most documentation
• Most efficient: Instant access without searching
• Authoritative: Aggregates official sources
• Up-to-date: Continuously maintained
• Fast: Direct URL construction vs searching
• Topic filtering: Targeted results with ?topic= parameter

Implementation

Step 1: Try context7.com (ALWAYS FIRST)
  ↓
Know GitHub repo?
  YES → https://context7.com/{org}/{repo}/llms.txt
  NO → Continue
  ↓
Know website?
  YES → https://context7.com/websites/{normalized-path}/llms.txt
  NO → Continue
  ↓
Specific topic needed?
  YES → Add ?topic={query} parameter
  NO → Use base URL
  ↓
Found?
  YES → Use as primary source
  NO → Fall back to WebSearch for llms.txt
  ↓
Still not found?
  YES → Fall back to repository analysis

Examples

Best approach (context7.com):
1. Direct URL: https://context7.com/vercel/next.js/llms.txt
2. WebFetch llms.txt
3. Launch Explorer agents for URLs
Total time: ~15 seconds

Topic-specific approach:
1. Direct URL: https://context7.com/shadcn-ui/ui/llms.txt?topic=date
2. WebFetch filtered content
3. Present focused results
Total time: ~10 seconds

Good fallback approach:
1. context7.com returns 404
2. WebSearch: "Astro llms.txt site:docs.astro.build"
3. Found → WebFetch llms.txt
4. Launch Explorer agents for URLs
Total time: ~60 seconds

Poor approach:
1. Skip context7.com entirely
2. Search for various documentation pages
3. Manually collect URLs
4. Process one by one
Total time: ~5 minutes

When Not Available

Fallback strategy when context7.com unavailable:

• If context7.com returns 404 → try WebSearch for llms.txt
• If WebSearch finds nothing in 30 seconds → move to repository
• If domain is incorrect → try 2-3 alternatives, then move on
• If documentation is very old → likely doesn't have llms.txt

2. Use Parallel Agents Aggressively

Why

• Speed: N tasks in time of 1 (vs N × time)
• Efficiency: Better resource utilization
• Coverage: Comprehensive results faster
• Scalability: Handles large documentation sets

Guidelines

Always use parallel for 3+ URLs:

3 URLs → 1 Explorer agent (acceptable)
4-10 URLs → 3-5 Explorer agents (optimal)
11+ URLs → 5-7 agents in phases (best)

Launch all agents in single message:

Good:
[Send one message with 5 Task tool calls]

Bad:
[Send message with Task call]
[Wait for result]
[Send another message with Task call]
[Wait for result]
...

Distribution Strategy

Even distribution:

10 URLs, 5 agents:
Agent 1: URLs 1-2
Agent 2: URLs 3-4
Agent 3: URLs 5-6
Agent 4: URLs 7-8
Agent 5: URLs 9-10

Topic-based distribution:

10 URLs, 3 agents:
Agent 1: Installation & Setup (URLs 1-3)
Agent 2: Core Concepts & API (URLs 4-7)
Agent 3: Examples & Guides (URLs 8-10)

When Not to Parallelize

• Single URL (use WebFetch)
• 2 URLs (single agent is fine)
• Dependencies between tasks (sequential required)
• Limited documentation (1-2 pages)

3. Verify Official Sources

Why

• Accuracy: Avoid outdated information
• Security: Prevent malicious content
• Credibility: Maintain trust
• Relevance: Match user's version/needs

Verification Checklist

For llms.txt:

[ ] Domain matches official site
[ ] HTTPS connection
[ ] Content format is valid
[ ] URLs point to official docs
[ ] Last-Modified header is recent (if available)

For repositories:

[ ] Organization matches official entity
[ ] Star count appropriate for library
[ ] Recent commits (last 6 months)
[ ] README mentions official status
[ ] Links back to official website
[ ] License matches expectations

For documentation:

[ ] Domain is official
[ ] Version matches user request
[ ] Last updated date visible
[ ] Content is complete (not stubs)
[ ] Links work (not 404s)

Red Flags

⚠️ Unofficial sources:

• Personal GitHub forks
• Outdated tutorials (>2 years old)
• Unmaintained repositories
• Suspicious domains
• No version information
• Conflicting with official docs

When to Use Unofficial Sources

Acceptable when:

• No official documentation exists
• Clearly labeled as community resource
• Recent and well-maintained
• Cross-referenced with official info
• User is aware of unofficial status

4. Report Methodology

Why

• Transparency: User knows how you found info
• Reproducibility: User can verify
• Troubleshooting: Helps debug issues
• Trust: Builds confidence in results

What to Include

Always report:

## Source

**Method**: llms.txt / Repository / Research / Mixed
**Primary source**: [main URL or repository]
**Additional sources**: [list]
**Date accessed**: [current date]
**Version**: [documentation version]

For llms.txt:

**Method**: llms.txt
**URL**: https://docs.astro.build/llms.txt
**URLs processed**: 8
**Date accessed**: 2025-10-26
**Version**: Latest (as of Oct 2025)

For repository:

**Method**: Repository analysis (Repomix)
**Repository**: https://github.com/org/library
**Commit**: abc123f (2025-10-20)
**Stars**: 15.2k
**Analysis date**: 2025-10-26

For research:

**Method**: Multi-source research
**Sources**:
- Official website: [url]
- Package registry: [url]
- Stack Overflow: [url]
- Community tutorials: [urls]
**Date accessed**: 2025-10-26
**Note**: No official llms.txt or repository available

Limitations Disclosure

Always note:

## ⚠️ Limitations

- Documentation for v2.x (user may need v3.x)
- API reference section incomplete
- Examples based on TypeScript (Python examples unavailable)
- Last updated 6 months ago

5. Handle Versions Explicitly

Why

• Compatibility: Avoid version mismatch errors
• Accuracy: Features vary by version
• Migration: Support upgrade paths
• Clarity: No ambiguity about what's covered

Version Detection

Check these sources:

1. URL path: /docs/v2/
2. Page header/title
3. Version selector on page
4. Git tag/branch name
5. Package.json or equivalent
6. Release date correlation

Version Handling Rules

User specifies version:

Request: "Documentation for React 18"
→ Search: "React v18 documentation"
→ Verify: Check version in content
→ Report: "Documentation for React v18.2.0"

User doesn't specify:

Request: "Documentation for Next.js"
→ Default: Assume latest
→ Confirm: "I'll find the latest Next.js documentation"
→ Report: "Documentation for Next.js 14.0 (latest as of [date])"

Version mismatch found:

Request: "Docs for v2"
Found: Only v3 documentation
→ Report: "⚠️ Requested v2, but only v3 docs available. Here's v3 with migration guide."

Multi-Version Scenarios

Comparison request:

Request: "Compare v1 and v2"
→ Find both versions
→ Launch parallel agents (set A for v1, set B for v2)
→ Present side-by-side analysis

Migration request:

Request: "How to migrate from v1 to v2"
→ Find v2 migration guide
→ Also fetch v1 and v2 docs
→ Highlight breaking changes
→ Provide code examples (before/after)

6. Aggregate Intelligently

Why

• Clarity: Easier to understand
• Efficiency: Less cognitive load
• Completeness: Unified view
• Actionability: Clear next steps

Bad Aggregation (Don't Do This)

## Results

Agent 1 found:
[dump of agent 1 output]

Agent 2 found:
[dump of agent 2 output]

Agent 3 found:
[dump of agent 3 output]

Problems:

• Redundant information repeated
• No synthesis
• Hard to scan
• Lacks narrative

Good Aggregation (Do This)

## Installation

[Synthesized from agents 1 & 2]
Three installation methods available:

1. **npm (recommended)**:

npm install library-name

2. **CDN**: [from agent 1]

<script src="..."></script>

3. **Manual**: [from agent 3]
   Download and include in project

## Core Concepts

[Synthesized from agents 2 & 4]
The library is built around three main concepts:

1. **Components**: [definition from agent 2]
2. **State**: [definition from agent 4]
3. **Effects**: [definition from agent 2]

## Examples

[From agents 3 & 5, deduplicated]
...

Benefits:

• Organized by topic
• Deduplicated
• Clear narrative
• Easy to scan

Synthesis Techniques

Deduplication:

Agent 1: "Install with npm install foo"
Agent 2: "You can install using npm: npm install foo"
→ Synthesized: "Install: `npm install foo`"

Prioritization:

Agent 1: Basic usage example
Agent 2: Basic usage example (same)
Agent 3: Advanced usage example
→ Keep: Basic (from agent 1) + Advanced (from agent 3)

Organization:

Agents returned mixed information:
- Installation steps
- Configuration
- Usage example
- Installation requirements
- More usage examples

→ Reorganize:
1. Installation (requirements + steps)
2. Configuration
3. Usage (all examples together)

7. Time Management

Why

• User experience: Fast results
• Resource efficiency: Don't waste compute
• Fail fast: Quickly try alternatives
• Practical limits: Avoid hanging

Timeouts

Set explicit timeouts:

WebSearch: 30 seconds
WebFetch: 60 seconds
Repository clone: 5 minutes
Repomix processing: 10 minutes
Explorer agent: 5 minutes per URL
Researcher agent: 10 minutes

Time Budgets

Simple query (single library, latest version):

Target: <2 minutes total

Phase 1 (Discovery): 30 seconds
- llms.txt search: 15 seconds
- Fetch llms.txt: 15 seconds

Phase 2 (Exploration): 60 seconds
- Launch agents: 5 seconds
- Agents fetch URLs: 60 seconds (parallel)

Phase 3 (Aggregation): 30 seconds
- Synthesize results
- Format output

Total: ~2 minutes

Complex query (multiple versions, comparison):

Target: <5 minutes total

Phase 1 (Discovery): 60 seconds
- Search both versions
- Fetch both llms.txt files

Phase 2 (Exploration): 180 seconds
- Launch 6 agents (2 sets of 3)
- Parallel exploration

Phase 3 (Comparison): 60 seconds
- Analyze differences
- Format side-by-side

Total: ~5 minutes

When to Extend Timeouts

Acceptable to go longer when:

• User explicitly requests comprehensive analysis
• Repository is large but necessary
• Multiple fallbacks attempted
• User is informed of delay

When to Give Up

Move to next method after:

• 3 failed attempts on same approach
• Timeout exceeded by 2x
• No progress for 30 seconds
• Error indicates permanent failure (404, auth required)

8. Cache Findings

Why

• Speed: Instant results for repeated requests
• Efficiency: Reduce network requests
• Consistency: Same results within session
• Reliability: Less dependent on network

What to Cache

High value (always cache):

- Repomix output (large, expensive to generate)
- llms.txt content (static, frequently referenced)
- Repository README (relatively static)
- Package registry metadata (changes rarely)

Medium value (cache within session):

- Documentation page content
- Search results
- Repository structure
- Version lists

Low value (don't cache):

- Real-time data (latest releases)
- User-specific content
- Time-sensitive information

Cache Duration

Within conversation:
- All fetched content (reuse freely)

Within session:
- Repomix output (until conversation ends)
- llms.txt content (until new version requested)

Across sessions:
- Don't cache (start fresh each time)

Cache Invalidation

Refresh cache when:

- User requests specific different version
- User says "get latest" or "refresh"
- Explicit time reference ("docs from today")
- Previous cache is from different library

Implementation

# First request for library X
1. Fetch llms.txt
2. Store content in session variable
3. Use for processing

# Second request for library X (same session)
1. Check if llms.txt cached
2. Reuse cached content
3. Skip redundant fetch

# Request for library Y
1. Don't reuse library X cache
2. Fetch fresh for library Y

Cache Hit Messages

ℹ️ Using cached llms.txt from 5 minutes ago.
To fetch fresh, say "refresh" or "get latest".

Quick Reference Checklist

Before Starting

• [ ] Identify library name clearly
• [ ] Confirm version (default: latest)
• [ ] Check if cached data available
• [ ] Plan method (llms.txt → repo → research)

During Discovery

• [ ] Start with llms.txt search
• [ ] Verify source is official
• [ ] Check version matches requirement
• [ ] Set timeout for each operation
• [ ] Fall back quickly if method fails

During Exploration

• [ ] Use parallel agents for 3+ URLs
• [ ] Launch all agents in single message
• [ ] Distribute workload evenly
• [ ] Monitor for errors/timeouts
• [ ] Be ready to retry or fallback

Before Presenting

• [ ] Synthesize by topic (not by agent)
• [ ] Deduplicate repeated information
• [ ] Verify version is correct
• [ ] Include source attribution
• [ ] Note any limitations
• [ ] Format clearly
• [ ] Check completeness

Quality Gates

Ask before presenting:

• [ ] Is information accurate?
• [ ] Are sources official?
• [ ] Does version match request?
• [ ] Are all key topics covered?
• [ ] Are limitations noted?
• [ ] Is methodology documented?
• [ ] Is output well-organized?

Common Documentation Sources

Reference guide for locating documentation across popular platforms and ecosystems.

context7.com Locations (PRIORITY)

ALWAYS try context7.com first for all libraries

JavaScript/TypeScript Frameworks

• 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
• SvelteKit: https://context7.com/sveltejs/kit/llms.txt
• Nuxt: https://context7.com/nuxt/nuxt/llms.txt

Frontend Libraries & UI

• React: https://context7.com/facebook/react/llms.txt
• Vue: https://context7.com/vuejs/core/llms.txt
• Svelte: https://context7.com/sveltejs/svelte/llms.txt
• shadcn/ui: https://context7.com/shadcn-ui/ui/llms.txt
• Radix UI: https://context7.com/radix-ui/primitives/llms.txt

Backend/Full-stack

• Hono: https://context7.com/honojs/hono/llms.txt
• Fastify: https://context7.com/fastify/fastify/llms.txt
• tRPC: https://context7.com/trpc/trpc/llms.txt

Build Tools

• Vite: https://context7.com/vitejs/vite/llms.txt
• Turbo: https://context7.com/vercel/turbo/llms.txt

Databases/ORMs

• Prisma: https://context7.com/prisma/prisma/llms.txt
• Drizzle: https://context7.com/drizzle-team/drizzle-orm/llms.txt

Authentication

• Better Auth: https://context7.com/better-auth/better-auth/llms.txt
• Auth.js: https://context7.com/nextauthjs/next-auth/llms.txt

Image Processing

• ImageMagick: https://context7.com/imagick/imagick/llms.txt

Topic-Specific Examples

• shadcn/ui date components: https://context7.com/shadcn-ui/ui/llms.txt?topic=date
• shadcn/ui buttons: https://context7.com/shadcn-ui/ui/llms.txt?topic=button
• Next.js caching: https://context7.com/vercel/next.js/llms.txt?topic=cache
• FFmpeg compression: https://context7.com/websites/ffmpeg_doxygen_8_0/llms.txt?topic=compress

Official llms.txt Locations (FALLBACK)

Use these only if context7.com returns 404:

JavaScript/TypeScript Frameworks

• 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
• Nuxt: https://nuxt.com/llms.txt

Frontend Libraries

• React: https://react.dev/llms.txt
• Vue: https://vuejs.org/llms.txt
• Svelte: https://svelte.dev/llms.txt

Backend/Full-stack

• Hono: https://hono.dev/llms.txt
• Fastify: https://fastify.dev/llms.txt
• tRPC: https://trpc.io/llms.txt

Build Tools

• Vite: https://vitejs.dev/llms.txt
• Turbopack: https://turbo.build/llms.txt

Databases/ORMs

• Prisma: https://prisma.io/llms.txt
• Drizzle: https://orm.drizzle.team/llms.txt

Repository Patterns

GitHub (Most Common)

URL patterns:

https://github.com/[org]/[repo]
https://github.com/[user]/[repo]

Common organization names:

• Company name: github.com/vercel/next.js
• Project name: github.com/remix-run/remix
• Community: github.com/facebook/react

Documentation locations in repositories:

/docs/
/documentation/
/website/docs/
/packages/docs/
README.md
CONTRIBUTING.md
/examples/

GitLab

URL pattern:

https://gitlab.com/[org]/[repo]

Bitbucket (Less Common)

URL pattern:

https://bitbucket.org/[org]/[repo]

Package Registries

npm (JavaScript/TypeScript)

URL: https://npmjs.com/package/[name]

Available info:

• Description
• Homepage link
• Repository link
• Version history
• Dependencies

Useful for:

• Finding official links
• Version information
• Package metadata

PyPI (Python)

URL: https://pypi.org/project/[name]

Available info:

• Description
• Documentation link
• Homepage
• Repository link
• Release history

Useful for:

• Python package documentation
• Official links
• Version compatibility

RubyGems (Ruby)

URL: https://rubygems.org/gems/[name]

Available info:

• Description
• Homepage
• Documentation
• Source code link
• Dependencies

Useful for:

• Ruby gem documentation
• Version information

Cargo (Rust)

URL: https://crates.io/crates/[name]

Available info:

• Description
• docs.rs link (auto-generated docs)
• Repository
• Version history

Useful for:

• Rust crate documentation
• Auto-generated API docs
• Repository link

Maven Central (Java)

URL: https://search.maven.org/artifact/[group]/[artifact]

Available info:

• Versions
• Dependencies
• Repository link
• License

Useful for:

• Java library information
• Dependency management

Documentation Hosting Platforms

Read the Docs

URL patterns:

https://[project].readthedocs.io
https://readthedocs.org/projects/[project]

Features:

• Version switching
• Multiple formats (HTML, PDF, ePub)
• Search functionality
• Often auto-generated from reStructuredText/Markdown

GitBook

URL patterns:

https://[org].gitbook.io/[project]
https://docs.[domain].com  (often GitBook-powered)

Features:

• Clean, modern interface
• Good navigation
• Often manually curated
• May require API key for programmatic access

Docusaurus

URL patterns:

https://[project].io
https://docs.[project].com

Common in:

• React ecosystem
• Meta/Facebook projects
• Modern open-source projects

Features:

• React-based
• Fast, static site
• Version management
• Good search

MkDocs

URL patterns:

https://[user].github.io/[project]
https://[custom-domain].com

Features:

• Python ecosystem
• Static site from Markdown
• Often on GitHub Pages
• Material theme popular

VitePress

URL patterns:

https://[project].dev
https://docs.[project].com

Common in:

• Vue ecosystem
• Modern projects
• Vite-based projects

Features:

• Vue-powered
• Very fast
• Clean design
• Good DX

Documentation Search Patterns

Finding llms.txt

ALWAYS try context7.com first:

For GitHub repositories:

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

For websites:

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

With topic filter:

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

Fallback: Traditional search if context7.com returns 404:

"[library] llms.txt site:[known-domain]"

Alternative domains to try:

site:docs.[library].com
site:[library].dev
site:[library].io
site:[library].org

Finding Official Repository

Search pattern:

"[library] official github repository"
"[library] source code github"

Verification checklist:

• Check organization/user is official
• Verify star count (popular libraries have many)
• Check last commit date (active maintenance)
• Look for official links in README

Finding Official Documentation

Search patterns:

"[library] official documentation"
"[library] docs site:official-domain"
"[library] API reference"

Domain patterns:

docs.[library].com
[library].dev/docs
docs.[library].io
[library].readthedocs.io

Common Documentation Structures

Typical Section Names

Getting started:

• Getting Started
• Quick Start
• Introduction
• Installation
• Setup

Core concepts:

• Core Concepts
• Fundamentals
• Basics
• Key Concepts
• Architecture

Guides:

• Guides
• How-To Guides
• Tutorials
• Examples
• Recipes

Reference:

• API Reference
• API Documentation
• Reference
• API
• CLI Reference

Advanced:

• Advanced
• Advanced Topics
• Deep Dives
• Internals
• Performance

Common File Names

README.md
GETTING_STARTED.md
INSTALLATION.md
CONTRIBUTING.md
CHANGELOG.md
API.md
TUTORIAL.md
EXAMPLES.md
FAQ.md

Framework-Specific Patterns

React Ecosystem

Common patterns:

- Uses Docusaurus
- Documentation at [project].dev or docs.[project].com
- Often has interactive examples
- CodeSandbox/StackBlitz embeds

Vue Ecosystem

Common patterns:

- Uses VitePress
- Documentation at [project].vuejs.org
- Bilingual (English/Chinese)
- API reference auto-generated

Python Ecosystem

Common patterns:

- Read the Docs hosting
- Sphinx-generated
- reStructuredText format
- [project].readthedocs.io

Rust Ecosystem

Common patterns:

- docs.rs for API docs
- Book format for guides ([project].rs/book)
- Markdown in repository
- Well-structured examples/

Quick Lookup Table

Error Handling Guide

Comprehensive troubleshooting and error resolution strategies for documentation discovery.

context7.com Not Accessible

Symptoms

• 404 error (library not indexed)
• Connection timeout
• Server error (500)
• Empty response

Troubleshooting Steps

1. Verify URL pattern:

For GitHub repos:

✓ Correct: https://context7.com/vercel/next.js/llms.txt
✗ Wrong: https://context7.com/nextjs/llms.txt

For websites:

✓ Correct: https://context7.com/websites/imgix/llms.txt
✗ Wrong: https://context7.com/docs.imgix.com/llms.txt

2. Try official llms.txt as fallback:

https://docs.[library].com/llms.txt
https://[library].dev/llms.txt
https://[library].io/llms.txt

3. Search for llms.txt if still not found:

WebSearch: "[library] llms.txt"
WebSearch: "[library] documentation AI format"

4. Fall back to repository analysis:

• If no llms.txt available anywhere
• Note in report: "llms.txt not available, used repository analysis"

Common Causes

• Library not yet indexed by context7.com
• Very new or obscure library
• Private repository
• context7.com temporary outage

Example Resolution

Problem: https://context7.com/org/new-lib/llms.txt returns 404

Steps:
1. Check official site: https://new-lib.dev/llms.txt ✗ Not found
2. WebSearch for llms.txt ✗ Not found
3. Fall back to repository: https://github.com/org/new-lib ✓ Found
4. Use Repomix for documentation extraction
5. Note in report: "No llms.txt available, analyzed repository directly"

llms.txt Not Accessible (Official Sites)

Symptoms

• 404 error
• Connection timeout
• Access denied (403)
• Empty response

Troubleshooting Steps

1. ALWAYS try context7.com first:

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

2. Try alternative official domains:

https://[name].dev/llms.txt
https://[name].io/llms.txt
https://[name].com/llms.txt
https://docs.[name].com/llms.txt
https://www.[name].com/llms.txt

3. Check for redirects:

• Old domain → new domain
• Non-HTTPS → HTTPS
• www → non-www or vice versa
• Root → /docs subdirectory

4. Search for llms.txt mention:

WebSearch: "[library] llms.txt"
WebSearch: "[library] documentation AI format"

5. Check documentation announcements:

• Blog posts about llms.txt
• GitHub discussions
• Recent release notes

6. If all fail:

• Fall back to repository analysis (Phase 3)
• Note in report: "llms.txt not available"

Common Causes

• Documentation recently moved/redesigned
• llms.txt not yet implemented
• Domain configuration issues
• Rate limiting or IP blocking
• Firewall/security restrictions

Example Resolution

Problem: https://example.dev/llms.txt returns 404

Steps:
1. Try: https://docs.example.dev/llms.txt ✓ Works!
2. Note: Documentation moved to docs subdomain
3. Proceed with Phase 2 using correct URL

Repository Not Found

Symptoms

• GitHub 404 error
• No official repository found
• Repository is private/requires auth
• Multiple competing repositories

Troubleshooting Steps

1. Search official website:

WebSearch: "[library] official website"

2. Check package registries:

WebSearch: "[library] npm"
WebSearch: "[library] pypi"
WebSearch: "[library] crates.io"

3. Look for organization GitHub:

WebSearch: "[company] github organization"
WebSearch: "[library] github org:[known-org]"

4. Check for mirrors or forks:

WebSearch: "[library] github mirror"
WebSearch: "[library] source code"

5. Verify through package manager:

# npm example
npm info [package-name] repository

# pip example
pip show [package-name]

6. If all fail:

• Use Researcher agents (Phase 4)
• Note: "No public repository available"

Common Causes

• Proprietary/closed-source software
• Documentation separate from code repository
• Company uses internal hosting (GitLab, Bitbucket, self-hosted)
• Project discontinued or archived
• Repository renamed/moved

Verification Checklist

When you find a repository, verify:

• [ ] Organization/user matches official entity
• [ ] Star count appropriate for library popularity
• [ ] Recent commits (active maintenance)
• [ ] README mentions official status
• [ ] Links back to official website
• [ ] License matches expectations

Repomix Failures

Symptoms

• Out of memory error
• Command hangs indefinitely
• Output file empty or corrupted
• Permission errors
• Network timeout during clone

Troubleshooting Steps

1. Check repository size:

# Clone and check size
git clone [url] /tmp/test-repo
du -sh /tmp/test-repo

# If >500MB, use focused approach

2. Focus on documentation only:

repomix --include "docs/**,README.md,*.md" --output docs.xml

3. Exclude large files:

repomix --exclude "*.png,*.jpg,*.pdf,*.zip,dist/**,build/**,node_modules/**" --output repomix-output.xml

4. Use shallow clone:

git clone --depth 1 [url] /tmp/docs-analysis
cd /tmp/docs-analysis
repomix --output repomix-output.xml

5. Alternative: Explorer agents

If Repomix fails completely:
1. Read README.md directly
2. List /docs directory structure
3. Launch Explorer agents for key files
4. Read specific documentation files

6. Check system resources:

# Check disk space
df -h /tmp

# Check available memory
free -h

# Kill if hung
pkill -9 repomix

Common Causes

• Repository too large (>1GB)
• Many binary files (images, videos)
• Large commit history
• Insufficient disk space
• Memory constraints
• Slow network connection
• Repository has submodules

Size Guidelines

Multiple Conflicting Sources

Symptoms

• Different installation instructions
• Conflicting API signatures
• Contradictory recommendations
• Version mismatches
• Breaking changes not documented

Resolution Steps

1. Check version of each source:

- Note documentation version number
- Check last-updated date
- Check URL for version indicator (v1/, v2/)
- Look for version selector on page

2. Prioritize sources:

Priority order:
1. Official docs (latest version)
2. Official docs (specified version)
3. Package registry (verified)
4. Official repository README
5. Community tutorials (recent)
6. Stack Overflow (recent, high votes)
7. Blog posts (date-verified)

3. Present both with context:

## Installation (v1.x - Legacy)
[old method]
Source: [link] (Last updated: [date])

## Installation (v2.x - Current)
[new method]
Source: [link] (Last updated: [date])

⚠️ Note: v2.x is recommended for new projects.
Migration guide: [link]

4. Cross-reference:

• Check if conflict is intentional (breaking change)
• Look for migration guides
• Check changelog/release notes
• Verify in GitHub issues/discussions

5. Document discrepancy:

## ⚠️ Conflicting Information Found

**Source 1** (official docs): Method A
**Source 2** (repository): Method B

**Analysis**: Source 1 reflects v2.x API. Source 2 README
not yet updated. Confirmed via changelog [link].

**Recommendation**: Use Method A (official docs).

Version Identification

Check these locations:

- URL path: /docs/v2/...
- Page header/footer
- Version selector dropdown
- Git branch/tag
- Package.json or equivalent
- CHANGELOG.md date correlation

Rate Limiting

Symptoms

• 429 Too Many Requests
• 403 Forbidden (temporary)
• Slow responses
• Connection refused
• "Rate limit exceeded" message

Solutions

1. Add delays between requests:

# Add 2-second delay
sleep 2

2. Use alternative sources:

Priority fallback chain:
GitHub → Official docs → Package registry → Repository → Archive

3. Batch operations:

Instead of:
- WebFetch URL 1
- WebFetch URL 2
- WebFetch URL 3

Use:
- Launch 3 Explorer agents (single batch)

4. Cache aggressively:

- Reuse fetched content within session
- Don't re-fetch same URLs
- Store repomix output for reuse
- Note fetch time, reuse if <1 hour old

5. Check rate limit headers:

If available:
- X-RateLimit-Remaining
- X-RateLimit-Reset
- Retry-After

6. Respect robots.txt:

# Check before aggressive crawling
curl https://example.com/robots.txt

Rate Limit Recovery

GitHub API (if applicable):

- Anonymous: 60 requests/hour
- Authenticated: 5000 requests/hour
- Wait period: 1 hour from first request

General approach:

1. Detect rate limit (429 or slow responses)
2. Switch to alternative source immediately
3. Don't retry same endpoint repeatedly
4. Note in report: "Rate limit encountered, used [alternative]"

Network Timeouts

Symptoms

• Request hangs indefinitely
• Connection timeout error
• No response received
• Partial content received

Solutions

1. Set explicit timeouts:

WebSearch: 30 seconds max
WebFetch: 60 seconds max
Repository clone: 5 minutes max
Repomix processing: 10 minutes max

2. Retry with timeout:

1st attempt: 60 seconds
2nd attempt: 90 seconds (if needed)
3rd attempt: Switch to alternative method

3. Check network connectivity:

# Test basic connectivity
ping -c 3 8.8.8.8

# Test DNS resolution
nslookup docs.example.com

# Test specific host
curl -I https://docs.example.com

4. Use alternative endpoints:

If main site times out:
- Try CDN version
- Try regional mirror
- Try cached version (Google Cache, Archive.org)

5. Fall back gracefully:

Main docs timeout → Repository → Package registry → Research

Incomplete Documentation

Symptoms

• Documentation stub pages
• "Coming soon" sections
• Broken links (404)
• Missing API reference
• Outdated examples

Handling Strategy

1. Identify gaps:

## Documentation Status

✅ Available:
- Installation guide
- Basic usage examples

⚠️ Incomplete:
- Advanced features (stub page)
- API reference (404 links)

❌ Missing:
- Migration guide
- Performance optimization

2. Supplement from repository:

- Check /examples directory
- Read test files for usage
- Analyze TypeScript definitions
- Check CHANGELOG for features

3. Use community sources:

- Recent Stack Overflow answers
- GitHub discussions
- Blog posts from maintainers
- Video tutorials

4. Note limitations clearly:

⚠️ **Documentation Limitations**

Official docs incomplete (as of [date]).
The following information inferred from:
- Repository examples
- TypeScript definitions
- Community discussions

May not reflect official recommendations.

Authentication/Access Issues

Symptoms

• Private repository
• Login required
• Organization-only access
• Documentation behind paywall

Solutions

1. For private repositories:

- Note: "Repository is private"
- Check for public mirror
- Look for public documentation site
- Search package registry for info

2. For paywalled docs:

- Check for free tier/trial
- Look for open-source alternative
- Search for community mirrors
- Use package registry info instead

3. Document access limitation:

## ⚠️ Access Limitation

Official repository is private. This report based on:
- Public documentation site: [url]
- Package registry info: [url]
- Community resources: [urls]

May not include internal implementation details.

Error Handling Best Practices

General Principles

1. Fail fast: Don't retry same method repeatedly 2. Fall back: Have alternative strategies ready 3. Document: Note what failed and why 4. Inform user: Clear about limitations 5. Partial success: Deliver what you can find

Error Reporting Template

## ⚠️ Discovery Issues Encountered

**Primary method**: [method] - [reason for failure]
**Fallback used**: [alternative method]
**Information completeness**: [percentage or description]

**What was found**:
- [list available information]

**What is missing**:
- [list gaps]

**Recommended action**:
- [how user can get missing info]

Recovery Decision Tree

Error encountered
  ↓
Is there an obvious alternative?
  YES → Try alternative immediately
  NO → Continue below
  ↓
Have we tried all primary methods?
  NO → Try next method in sequence
  YES → Continue below
  ↓
Is partial information useful?
  YES → Deliver partial results with notes
  NO → Inform user, request guidance

Limitations & Success Criteria

Understanding boundaries and measuring effectiveness of documentation discovery.

context7.com Limitations

Not All Libraries Indexed

Limitation:

• context7.com doesn't index every repository/website
• Very new libraries may not be available yet
• Private repositories not accessible
• Some niche libraries missing

Impact:

• Need fallback to official llms.txt or repository analysis
• May add 10-20 seconds to discovery time
• Requires manual search for obscure libraries

Workarounds:

1. Try context7.com first (always)
2. If 404, fall back to official llms.txt search
3. If still not found, use repository analysis
4. Note in report which method was used

Example:

Tried: https://context7.com/org/new-lib/llms.txt → 404
Fallback: WebSearch for "new-lib llms.txt" → Found
Used: Official llms.txt from website

Topic Filtering Accuracy

Limitation:

• ?topic= parameter relies on keyword matching
• May miss relevant content with different terminology
• May include tangentially related content
• Quality depends on context7 indexing

Impact:

• Occasionally need to review base llms.txt without topic filter
• May miss some relevant documentation

Workarounds:

- Try multiple topic keywords
- Fall back to full llms.txt if topic search insufficient
- Use broader terms for better coverage

Cannot Handle

Password-Protected Documentation

Limitation:

• No access to authentication-required content
• Cannot log in to platforms
• No credential management
• Cannot access organization-internal docs

Impact:

• Enterprise documentation inaccessible
• Premium content unavailable
• Private beta docs unreachable
• Internal wikis not readable

Workarounds:

- Ask user for public alternatives
- Search for public subset of docs
- Use publicly available README/marketing
- Check if trial/demo access available
- Note limitation in report

Report template:

⚠️ **Access Limitation**

Documentation requires authentication.

**What we can access**:
- Public README: [url]
- Package registry info: [url]
- Marketing site: [url]

**Cannot access**:
- Full documentation (requires login)
- Internal guides
- Premium content

**Recommendation**: Contact vendor for access or check if public docs available.

Rate-Limited APIs

Limitation:

• No API credentials for authenticated access
• Subject to anonymous rate limits
• Cannot request increased limits
• No retry with authentication

Impact:

• Limited requests per hour (e.g., GitHub: 60/hour anonymous)
• May hit limits during comprehensive search
• Slower fallback required
• Incomplete coverage possible

Workarounds:

- Add delays between requests
- Use alternative sources (cached, mirrors)
- Prioritize critical pages
- Use Researcher agents instead of API
- Switch to repository analysis

Detection:

Symptoms:
- 429 Too Many Requests
- X-RateLimit-Remaining: 0
- Slow or refused connections

Response:
- Immediately switch to alternative method
- Don't retry same endpoint
- Note in report which method used

Real-Time Documentation

Limitation:

• Uses snapshot at time of access
• Cannot monitor for updates
• No real-time synchronization
• May miss very recent changes

Impact:

• Documentation updated minutes ago may not be reflected
• Breaking changes announced today might be missed
• Latest release notes may not be current
• Version just released may not be documented

Workarounds:

- Note access date in report
- Recommend user verify if critical
- Check last-modified headers
- Compare with release dates
- Suggest official site for latest

Report template:

ℹ️ **Snapshot Information**

Documentation retrieved: 2025-10-26 14:30 UTC

**Last-Modified** (if available):
- Main docs: 2025-10-24
- API reference: 2025-10-22

**Note**: For real-time updates, check official site: [url]

Interactive Documentation

Limitation:

• Cannot run interactive examples
• Cannot execute code playgrounds
• No ability to test API calls
• Cannot verify functionality

Impact:

• Cannot confirm examples work
• Cannot test edge cases
• Cannot validate API responses
• Cannot verify performance claims

Workarounds:

- Provide code examples as-is
- Note: "Example provided, not tested"
- Recommend user run examples
- Link to interactive playground if available
- Include caveats about untested code

Report template:

## Example Usage

Example from official docs (not tested)

import library result = library.do_thing()

⚠️ **Note**: Example provided from documentation but not executed.
Please test in your environment.

**Interactive playground**: [url if available]

Video-Only Documentation

Limitation:

• Cannot process video content directly
• Limited transcript access
• Cannot extract code from video
• Cannot parse visual diagrams

Impact:

• Video tutorials not usable
• YouTube courses inaccessible
• Screencasts not processable
• Visual walkthroughs unavailable

Workarounds:

- Search for transcript if available
- Look for accompanying blog post
- Find text-based alternative
- Check for community notes
- Use automated captions if available (low quality)

Report template:

ℹ️ **Video Content Detected**

Primary documentation is video-based: [url]

**Alternatives found**:
- Blog post summary: [url]
- Community notes: [url]

**Cannot extract**:
- Detailed walkthrough from video
- Visual examples
- Demonstration steps

**Recommendation**: Watch video directly for visual content.

May Struggle With

Very Large Repositories (>1GB)

Challenge:

• Repomix may fail or hang
• Clone takes very long time
• Processing exceeds memory limits
• Output file too large to read

Success rate: ~30% for >1GB repos

Mitigation:

1. Try shallow clone: git clone --depth 1
2. Focus on docs only: repomix --include "docs/**"
3. Exclude binaries: --exclude "*.png,*.jpg,dist/**"
4. If fails: Use Explorer agents on specific files
5. Note limitation in report

When to skip:

Repository size indicators:
- Git clone shows >1GB download
- Contains large binaries (ml models, datasets)
- Has extensive history (>10k commits)
- Many multimedia files

→ Skip Repomix, use targeted exploration

Documentation in Images/PDFs

Challenge:

• Cannot reliably extract text from images
• PDF parsing limited
• Formatting often lost
• Code snippets may be corrupted

Success rate: ~50% quality for PDFs, ~10% for images

Mitigation:

1. Search for text alternative
2. Try OCR if critical (low quality)
3. Provide image URL instead
4. Note content not extractable
5. Recommend manual review

Report template:

⚠️ **Image-Based Documentation**

Primary documentation in PDF/images: [url]

**Extraction quality**: Limited
**Recommendation**: Download and review manually

**Text alternatives found**:
- [any alternatives]

Non-English Documentation

Challenge:

• No automatic translation
• May miss context/nuance
• Technical terms may not translate well
• Examples may be language-specific

Success rate: Variable (depends on user needs)

Mitigation:

1. Note language in report
2. Offer key section translation if user requests
3. Search for English version
4. Check if bilingual docs exist
5. Provide original with language note

Report template:

ℹ️ **Language Notice**

Primary documentation in: Japanese

**English availability**:
- Partial translation: [url]
- Community translation: [url]
- No official English version found

**Recommendation**: Use translation tool or request community help.

Scattered Documentation

Challenge:

• Multiple sites/repositories
• Inconsistent structure
• Conflicting information
• No central source

Success rate: ~60% coverage

Mitigation:

1. Use Researcher agents
2. Prioritize official sources
3. Cross-reference findings
4. Note conflicts clearly
5. Take longer but be thorough

Report template:

ℹ️ **Fragmented Documentation**

Information found across multiple sources:

**Official** (incomplete):
- Website: [url]
- Package registry: [url]

**Community** (supplementary):
- Stack Overflow: [url]
- Tutorial: [url]

**Note**: No centralized documentation. Information aggregated from
multiple sources. Conflicts resolved by prioritizing official sources.

Deprecated/Legacy Libraries

Challenge:

• Documentation removed or archived
• Only old versions available
• Outdated information
• No current maintenance

Success rate: ~40% for fully deprecated libraries

Mitigation:

1. Use Internet Archive (Wayback Machine)
2. Search GitHub repository history
3. Check package registry for old README
4. Look for fork with docs
5. Note legacy status clearly

Report template:

⚠️ **Legacy Library**

**Status**: Deprecated as of [date]
**Last update**: [date]

**Documentation sources**:
- Archived docs (via Wayback): [url]
- Repository (last commit [date]): [url]

**Recommendation**: Consider modern alternative: [suggestion]

**Migration path**: [if available]

Success Criteria

1. Finds Relevant Information

Measured by:

• [ ] Answers user's specific question
• [ ] Covers requested topics
• [ ] Appropriate depth/detail
• [ ] Includes practical examples
• [ ] Links to additional resources

Quality levels:

Excellent (100%):

- All requested topics covered
- Examples for each major concept
- Clear, comprehensive information
- Official source, current version
- No gaps or limitations

Good (80-99%):

- Most requested topics covered
- Examples for core concepts
- Information mostly complete
- Official source, some gaps noted
- Minor limitations

Acceptable (60-79%):

- Core topics covered
- Some examples present
- Information somewhat complete
- Mix of official/community sources
- Some gaps noted

Poor (<60%):

- Only partial coverage
- Few or no examples
- Significant gaps
- Mostly unofficial sources
- Many limitations

2. Uses Most Efficient Method

Measured by:

• [ ] Started with llms.txt
• [ ] Used parallel agents appropriately
• [ ] Avoided unnecessary operations
• [ ] Completed in reasonable time
• [ ] Fell back efficiently when needed

Efficiency score:

Optimal:

- Found llms.txt immediately
- Parallel agents for all URLs
- Single batch processing
- Completed in <2 minutes
- No wasted operations

Good:

- Found llms.txt after 1-2 tries
- Mostly parallel processing
- Minimal sequential operations
- Completed in <5 minutes
- One minor inefficiency

Acceptable:

- Fell back to repository after llms.txt search
- Mix of parallel and sequential
- Some redundant operations
- Completed in <10 minutes
- A few inefficiencies

Poor:

- Didn't try llms.txt first
- Mostly sequential processing
- Many redundant operations
- Took >10 minutes
- Multiple inefficiencies

3. Completes in Reasonable Time

Target times:

Factors affecting time:

• Documentation structure (well-organized vs scattered)
• Source availability (llms.txt vs research)
• Content volume (few pages vs many)
• Network conditions (fast vs slow)
• Complexity (simple vs comprehensive)

4. Provides Clear Source Attribution

Measured by:

• [ ] Lists all sources used
• [ ] Notes method employed
• [ ] Includes URLs/references
• [ ] Identifies official vs community
• [ ] Credits authors when relevant

Quality template:

Excellent:

## Sources

**Primary method**: llms.txt
**URL**: https://docs.library.com/llms.txt

**Documentation retrieved**:
1. Getting Started (official): [url]
2. API Reference (official): [url]
3. Examples (official): [url]

**Additional sources**:
- Repository: https://github.com/org/library
- Package registry: https://npmjs.com/package/library

**Method**: Parallel exploration with 3 agents
**Date**: 2025-10-26 14:30 UTC

5. Identifies Version/Date

Measured by:

• [ ] Documentation version noted
• [ ] Last-updated date included
• [ ] Matches user's version requirement
• [ ] Flags if version mismatch
• [ ] Notes if version unclear

Best practice:

## Version Information

**Documentation version**: v3.2.1
**Last updated**: 2025-10-20
**Retrieved**: 2025-10-26

**User requested**: v3.x ✓ Match

**Note**: This is the latest stable version as of retrieval date.

6. Notes Limitations/Gaps

Measured by:

• [ ] Missing information identified
• [ ] Incomplete sections noted
• [ ] Known issues mentioned
• [ ] Alternatives suggested
• [ ] Workarounds provided

Good practice:

## ⚠️ Limitations

**Incomplete documentation**:
- Advanced features section (stub page)
- Migration guide (404 error)

**Not available**:
- Video tutorials mentioned but not accessible
- Interactive examples require login

**Workarounds**:
- Advanced features: See examples in repository
- Migration: Check CHANGELOG.md for breaking changes

**Alternatives**:
- Community tutorial: [url]
- Stack Overflow: [url]

7. Well-Organized Output

Measured by:

• [ ] Clear structure
• [ ] Logical flow
• [ ] Easy to scan
• [ ] Actionable information
• [ ] Proper formatting

Structure template:

# Documentation for [Library] [Version]

## Overview
[Brief description]

## Source
[Attribution]

## Installation
[Step-by-step]

## Quick Start
[Minimal example]

## Core Concepts
[Main ideas]

## API Reference
[Key methods]

## Examples
[Practical usage]

## Additional Resources
[Links]

## Limitations
[Any gaps]

Quality Checklist

Before Presenting Report

Content quality:

• [ ] Information is accurate
• [ ] Sources are official (or noted as unofficial)
• [ ] Version matches request
• [ ] Examples are clear
• [ ] No obvious errors

Completeness:

• [ ] All key topics covered
• [ ] Installation instructions present
• [ ] Usage examples included
• [ ] Configuration documented
• [ ] Troubleshooting information available

Organization:

• [ ] Logical flow
• [ ] Clear headings
• [ ] Proper code formatting
• [ ] Links working (spot check)
• [ ] Easy to scan

Attribution:

• [ ] Sources listed
• [ ] Method documented
• [ ] Version identified
• [ ] Date noted
• [ ] Limitations disclosed

Usability:

• [ ] Actionable information
• [ ] Copy-paste ready examples
• [ ] Next steps clear
• [ ] Resources for deep dive
• [ ] Known issues noted

Performance Metrics

Time-to-Value

Measures: How quickly user gets useful information

Targets:

First useful info: <30 seconds
Core coverage: <2 minutes
Complete report: <5 minutes

Tracking:

Start → llms.txt found → First URL fetched → Critical info extracted
  15s        30s               45s                  60s

User can act on info at 60s mark
(even if full report takes 5 minutes)

Coverage Completeness

Measures: Percentage of user needs met

Calculation:

User needs 5 topics:
- Installation ✓
- Basic usage ✓
- API reference ✓
- Configuration ✓
- Examples ✗ (not found)

Coverage: 4/5 = 80%

Targets:

Excellent: 90-100%
Good: 75-89%
Acceptable: 60-74%
Poor: <60%

Source Quality

Measures: Reliability of sources used

Scoring:

Official docs: 100 points
Official repository: 80 points
Package registry: 60 points
Recent community (verified): 40 points
Old community (unverified): 20 points

Target: Average >70 points

User Satisfaction Indicators

Positive signals:

- User proceeds immediately with info
- No follow-up questions needed
- User says "perfect" or "thanks"
- User marks conversation complete

Negative signals:

- User asks same question differently
- User requests more details
- User says "incomplete" or "not what I needed"
- User abandons task

Continuous Improvement

Learn from Failures

When documentation discovery struggles:

1. Document the issue

   - What was attempted
   - What failed
   - Why it failed
   - How it was resolved (if at all)

2. Identify patterns

   - Is this library-specific?
   - Is this ecosystem-specific?
   - Is this a general limitation?

3. Update strategies

   - Add workaround to playbook
   - Update fallback sequence
   - Note limitation in documentation

Measure and Optimize

Track these metrics:

- Average time to complete
- Coverage percentage
- Source quality score
- User satisfaction
- Failure rate by method

Optimize based on data:

- Which method succeeds most often?
- Which ecosystems need special handling?
- Where are time bottlenecks?
- What causes most failures?

Performance Optimization

Strategies and techniques for maximizing speed and efficiency in documentation discovery.

Core Principles

0. Use context7.com for Instant llms.txt Access

Fastest Approach:

Direct URL construction instead of searching:

Traditional: WebSearch (15-30s) → WebFetch (5-10s) = 20-40s
context7.com: Direct WebFetch (5-10s) = 5-10s

Speed improvement: 2-4x faster

Benefits:

• No search required (instant URL construction)
• Consistent URL patterns
• Reliable availability
• Topic filtering for targeted results

Examples:

GitHub repo:
https://context7.com/vercel/next.js/llms.txt
→ Instant, no search needed

Website:
https://context7.com/websites/imgix/llms.txt
→ Instant, no search needed

Topic-specific:
https://context7.com/shadcn-ui/ui/llms.txt?topic=date
→ Filtered results, even faster

Performance Impact:

Without context7.com:
1. WebSearch for llms.txt: 15s
2. WebFetch llms.txt: 5s
3. Launch agents: 5s
Total: 25s

With context7.com:
1. Direct WebFetch: 5s
2. Launch agents: 5s
Total: 10s (2.5x faster!)

With context7.com + topic:
1. Direct WebFetch (filtered): 3s
2. Process focused results: 2s
Total: 5s (5x faster!)

1. Minimize Sequential Operations

The Problem:

Sequential operations add up linearly:

Total Time = Op1 + Op2 + Op3 + ... + OpN

Example:

Fetch URL 1: 5 seconds
Fetch URL 2: 5 seconds
Fetch URL 3: 5 seconds
Total: 15 seconds

The Solution:

Parallel operations complete in max time of slowest:

Total Time = max(Op1, Op2, Op3, ..., OpN)

Example:

Launch 3 agents simultaneously
All complete in: ~5 seconds
Total: 5 seconds (3x faster!)

2. Batch Related Operations

Benefits:

• Fewer context switches
• Better resource utilization
• Easier to track
• More efficient aggregation

Grouping Strategies:

By topic:

Agent 1: All authentication-related docs
Agent 2: All database-related docs
Agent 3: All API-related docs

By content type:

Agent 1: All tutorials
Agent 2: All reference docs
Agent 3: All examples

By priority:

Phase 1 (critical): Getting started, installation, core concepts
Phase 2 (important): Guides, API reference, configuration
Phase 3 (optional): Advanced topics, internals, optimization

3. Smart Caching

What to cache:

• Repomix output (expensive to generate)
• llms.txt content (static)
• Repository structure (rarely changes)
• Documentation URLs (reference list)

When to refresh:

• User requests specific version
• Documentation updated (check last-modified)
• Cache older than session
• User explicitly requests fresh data

4. Early Termination

When to stop:

✓ User's core needs met
✓ Critical information found
✓ Time limit approaching
✓ Diminishing returns (90% coverage achieved)

How to decide:

After Phase 1 (critical docs):
- Review what was found
- Check against user request
- If 80%+ covered → deliver now
- Offer to fetch more if needed

Performance Patterns

Pattern 1: Parallel Exploration

Scenario: llms.txt contains 10 URLs

Slow approach (sequential):

Time: 10 URLs × 5 seconds = 50 seconds

Step 1: Fetch URL 1 (5s)
Step 2: Fetch URL 2 (5s)
Step 3: Fetch URL 3 (5s)
...
Step 10: Fetch URL 10 (5s)

Fast approach (parallel):

Time: ~5-10 seconds total

Step 1: Launch 5 Explorer agents (simultaneous)
  Agent 1: URLs 1-2
  Agent 2: URLs 3-4
  Agent 3: URLs 5-6
  Agent 4: URLs 7-8
  Agent 5: URLs 9-10

Step 2: Wait for all (max time: ~5-10s)
Step 3: Aggregate results

Speedup: 5-10x faster

Pattern 2: Lazy Loading

Scenario: Documentation has 30+ pages

Slow approach (fetch everything):

Time: 30 URLs × 5 seconds ÷ 5 agents = 30 seconds

Fetch all 30 pages upfront
User only needs 5 of them
Wasted: 25 pages × 5 seconds ÷ 5 = 25 seconds

Fast approach (priority loading):

Time: 10 URLs × 5 seconds ÷ 5 agents = 10 seconds

Phase 1: Fetch critical 10 pages
Review: Does this cover user's needs?
If yes: Stop here (saved 20 seconds)
If no: Fetch additional as needed

Speedup: Up to 3x faster for typical use cases

Pattern 3: Smart Fallbacks

Scenario: llms.txt not found

Slow approach (exhaustive search):

Time: ~5 minutes

Try: docs.library.com/llms.txt (30s timeout)
Try: library.dev/llms.txt (30s timeout)
Try: library.io/llms.txt (30s timeout)
Try: library.org/llms.txt (30s timeout)
Try: www.library.com/llms.txt (30s timeout)
Then: Fall back to repository

Fast approach (quick fallback):

Time: ~1 minute

Try: docs.library.com/llms.txt (15s)
Try: library.dev/llms.txt (15s)
Not found → Immediately try repository (30s)

Speedup: 5x faster

Pattern 4: Incremental Results

Scenario: Large documentation set

Slow approach (all-or-nothing):

Time: 5 minutes until first result

Fetch all documentation
Aggregate everything
Present complete report
User waits 5 minutes

Fast approach (streaming):

Time: 30 seconds to first result

Phase 1: Fetch critical docs (30s)
Present: Initial findings
Phase 2: Fetch important docs (60s)
Update: Additional findings
Phase 3: Fetch supplementary (90s)
Final: Complete report

Benefit: User gets value immediately, can stop early if satisfied

Optimization Techniques

Technique 1: Workload Balancing

Problem: Uneven distribution causes bottlenecks

Bad distribution:
Agent 1: 1 URL (small) → finishes in 5s
Agent 2: 10 URLs (large) → finishes in 50s
Total: 50s (bottlenecked by Agent 2)

Solution: Balance by estimated size

Good distribution:
Agent 1: 3 URLs (medium pages) → ~15s
Agent 2: 3 URLs (medium pages) → ~15s
Agent 3: 3 URLs (medium pages) → ~15s
Agent 4: 1 URL (large page) → ~15s
Total: ~15s (balanced)

Technique 2: Request Coalescing

Problem: Redundant requests slow things down

Bad:
Agent 1: Fetch README.md
Agent 2: Fetch README.md (duplicate!)
Agent 3: Fetch README.md (duplicate!)
Wasted: 2 redundant fetches

Solution: Deduplicate before fetching

Good:
Pre-processing: Identify unique URLs
Agent 1: Fetch README.md (once)
Agent 2: Fetch INSTALL.md
Agent 3: Fetch API.md
Share: README.md content across agents if needed

Technique 3: Timeout Tuning

Problem: Default timeouts too conservative

Slow:
WebFetch timeout: 120s (too long for fast sites)
If site is down: Wait 120s before failing

Solution: Adaptive timeouts

Fast:
Known fast sites (official docs): 30s timeout
Unknown sites: 60s timeout
Large repos: 120s timeout
If timeout hit: Immediately try alternative

Technique 4: Selective Fetching

Problem: Fetching irrelevant content

Wasteful:
Fetch: Installation guide ✓ (needed)
Fetch: API reference ✓ (needed)
Fetch: Internal architecture ✗ (not needed for basic usage)
Fetch: Contributing guide ✗ (not needed)
Fetch: Changelog ✗ (not needed)

Solution: Filter by user needs

Efficient:
User need: "How to get started"
Fetch only: Installation, basic usage, examples
Skip: Advanced topics, internals, contribution
Speedup: 50% less fetching

Performance Benchmarks

Target Times

Real-World Examples

Fast case (Next.js with llms.txt):

00:00 - Start
00:05 - Found llms.txt
00:10 - Fetched content (12 URLs)
00:15 - Launched 4 agents
00:45 - All agents complete
00:55 - Report ready
Total: 55 seconds ✓

Medium case (Repository without llms.txt):

00:00 - Start
00:15 - llms.txt not found
00:20 - Found repository
00:30 - Cloned repository
02:00 - Repomix complete
02:30 - Analyzed output
02:45 - Report ready
Total: 2m 45s ✓

Slow case (Scattered documentation):

00:00 - Start
00:30 - llms.txt not found
00:45 - Repository not found
01:00 - Launched 4 Researcher agents
05:00 - All research complete
06:00 - Aggregated findings
06:30 - Report ready
Total: 6m 30s (acceptable for research)

Common Performance Issues

Issue 1: Too Many Agents

Symptom: Slower than sequential

Problem:
Launched 15 agents for 15 URLs
Overhead: Agent initialization, coordination
Result: Slower than 5 agents with 3 URLs each

Solution:

Max 7 agents per batch
Group URLs sensibly
Use phases for large sets

Issue 2: Blocking Operations

Symptom: Agents waiting unnecessarily

Problem:
Agent 1: Fetch URL, wait for Agent 2
Agent 2: Fetch URL, wait for Agent 3
Agent 3: Fetch URL
Result: Sequential instead of parallel

Solution:

Launch all agents independently
No dependencies between agents
Aggregate after all complete

Issue 3: Redundant Fetching

Symptom: Same content fetched multiple times

Problem:
Phase 1: Fetch installation guide
Phase 2: Fetch installation guide again
Result: Wasted time

Solution:

Cache fetched content
Check cache before fetching
Reuse within session

Issue 4: Late Bailout

Symptom: Continuing when should stop

Problem:
Found 90% of needed info after 1 minute
Spent 4 more minutes on remaining 10%
Result: 5x time for marginal gain

Solution:

Check progress after critical phase
If 80%+ covered → offer to stop
Only continue if user wants comprehensive

Performance Monitoring

Key Metrics

Track these times:

- llms.txt discovery: Target <30s
- Repository clone: Target <60s
- Repomix processing: Target <2min
- Agent exploration: Target <60s
- Total time: Target <3min for typical case

Performance Report Template

## Performance Summary

**Total time**: 1m 25s
**Method**: llms.txt + parallel exploration

**Breakdown**:
- Discovery: 15s (llms.txt search & fetch)
- Exploration: 50s (4 agents, 12 URLs)
- Aggregation: 20s (synthesis & formatting)

**Efficiency**: 8.5x faster than sequential
(12 URLs × 5s = 60s sequential, actual: 50s parallel)

When to Optimize Further

Optimize if:

• [ ] Total time >2x target
• [ ] User explicitly requests "fast"
• [ ] Repeated similar queries (cache benefit)
• [ ] Large documentation set (>20 URLs)

Don't over-optimize if:

• [ ] Already meeting targets
• [ ] One-time query
• [ ] User values completeness over speed
• [ ] Research requires thoroughness

Quick Optimization Checklist

Before Starting

• [ ] Check if content already cached
• [ ] Identify fastest method for this case
• [ ] Plan for parallel execution
• [ ] Set appropriate timeouts

During Execution

• [ ] Launch agents in parallel (not sequential)
• [ ] Use single message for multiple agents
• [ ] Monitor for bottlenecks
• [ ] Be ready to terminate early

After First Phase

• [ ] Assess coverage achieved
• [ ] Determine if user needs met
• [ ] Decide: continue or deliver now
• [ ] Cache results for potential reuse

Optimization Decision Tree

Need documentation?
  ↓
Check cache
  ↓
HIT → Use cached (0s) ✓
MISS → Continue
  ↓
llms.txt available?
  ↓
YES → Parallel agents (30-60s) ✓
NO → Continue
  ↓
Repository available?
  ↓
YES → Repomix (2-5min)
NO → Research (3-7min)
  ↓
After Phase 1:
80%+ coverage?
  ↓
YES → Deliver now (save time) ✓
NO → Continue to Phase 2

Tool Selection Guide

Complete reference for choosing and using the right tools for documentation discovery.

context7.com (PRIORITY)

Use FIRST for all llms.txt lookups

Patterns:

GitHub repositories:

Pattern: https://context7.com/{org}/{repo}/llms.txt
Examples:
- https://github.com/vercel/next.js → https://context7.com/vercel/next.js/llms.txt
- https://github.com/shadcn-ui/ui → https://context7.com/shadcn-ui/ui/llms.txt

Websites:

Pattern: https://context7.com/websites/{normalized-path}/llms.txt
Examples:
- https://docs.imgix.com/ → https://context7.com/websites/imgix/llms.txt
- https://ffmpeg.org/doxygen/8.0/ → https://context7.com/websites/ffmpeg_doxygen_8_0/llms.txt

Topic-specific searches:

Pattern: https://context7.com/{path}/llms.txt?topic={query}
Examples:
- https://context7.com/shadcn-ui/ui/llms.txt?topic=date
- https://context7.com/vercel/next.js/llms.txt?topic=cache
- https://context7.com/websites/ffmpeg_doxygen_8_0/llms.txt?topic=compress

Benefits:

• Comprehensive aggregator of documentation
• Up-to-date content
• Topic filtering for targeted results
• Consistent format across libraries
• Reduces search time

When to use:

• ALWAYS try context7.com first before WebSearch
• Use topic parameter when user asks about specific feature
• Fall back to WebSearch only if context7.com returns 404

WebSearch

Use when:

• context7.com unavailable or returns 404
• Finding GitHub repository URLs
• Locating official documentation sites
• Identifying package registries
• Searching for specific versions

Best practices:

• Try context7.com FIRST
• Include domain in query: site:docs.example.com
• Specify version when needed: v2.0 llms.txt
• Use official terms: "official repository" "documentation"
• Check multiple domains if first fails

Example queries:

Good: "Next.js llms.txt site:nextjs.org"
Good: "React v18 documentation site:react.dev"
Good: "Vue 3 official github repository"

Avoid: "how to use react" (too vague)
Avoid: "best react tutorial" (not official)

WebFetch

Use when:

• Reading llms.txt content
• Accessing single documentation pages
• Retrieving specific URLs
• Checking documentation structure
• Verifying content availability

Best practices:

• Use specific prompt: "Extract all documentation URLs"
• Handle redirects properly
• Check for rate limiting
• Verify content is complete
• Note last-modified dates when available

Limitations:

• Single URL at a time (use Explorer for multiple)
• May timeout on very large pages
• Cannot handle dynamic content
• No JavaScript execution

Task Tool with Explore Subagent

Use when:

• Multiple URLs to read (3+)
• Need parallel exploration
• Comprehensive documentation coverage
• Time-sensitive requests
• Large documentation sets

Best practices:

• Launch all agents in single message
• Distribute workload evenly
• Group related URLs per agent
• Maximum 7 agents per batch
• Provide clear extraction goals

Example prompt:

"Read the following URLs and extract:
1. Installation instructions
2. Core API methods
3. Configuration options
4. Common usage examples

URLs:
- [url1]
- [url2]
- [url3]"

Task Tool with Researcher Subagent

Use when:

• No structured documentation found
• Need diverse information sources
• Community knowledge required
• Scattered documentation
• Comparative analysis needed

Best practices:

• Assign specific research areas per agent
• Request source verification
• Ask for date/version information
• Prioritize official sources
• Cross-reference findings

Example prompt:

"Research [library] focusing on:
1. Official installation methods
2. Common usage patterns
3. Known limitations or issues
4. Community best practices

Prioritize official sources and note version/date for all findings."

Repomix

Use when:

• GitHub repository available
• Need complete codebase analysis
• Documentation scattered in repository
• Want to analyze code structure
• API documentation in code comments

Installation:

# Check if installed
which repomix

# Install globally if needed
npm install -g repomix

# Verify installation
repomix --version

Usage:

# Basic usage
git clone [repo-url] /tmp/docs-analysis
cd /tmp/docs-analysis
repomix --output repomix-output.xml

# Focus on specific directory
repomix --include "docs/**" --output docs-only.xml

# Exclude large files
repomix --exclude "*.png,*.jpg,*.pdf" --output repomix-output.xml

When Repomix may fail:

• Repository > 1GB (too large)
• Requires authentication (private repo)
• Slow network connection
• Limited disk space
• Binary-heavy repository

Alternatives if Repomix fails:

# Option 1: Focus on docs directory only
repomix --include "docs/**,README.md" --output docs.xml

# Option 2: Use Explorer agents to read specific files
# Launch agents to read key documentation files directly

# Option 3: Manual repository exploration
# Read README, then explore /docs directory structure

Tool Selection Decision Tree

Need documentation?
  ↓
Try context7.com first
  ↓
Know GitHub org/repo?
  YES → https://context7.com/{org}/{repo}/llms.txt
  NO → Continue
  ↓
Know website URL?
  YES → https://context7.com/websites/{normalized-path}/llms.txt
  NO → Continue
  ↓
Specific topic/feature?
  YES → Add ?topic={query} parameter
  NO → Use base llms.txt URL
  ↓
context7.com found?
  YES → Process llms.txt URLs (go to URL count check)
  NO → Continue
  ↓
Fallback: WebSearch for llms.txt
  ↓
Single URL?
  YES → WebFetch
  NO → Continue
  ↓
1-3 URLs?
  YES → Single Explorer agent
  NO → Continue
  ↓
4+ URLs?
  YES → Multiple Explorer agents (3-7)
  NO → Continue
  ↓
Need repository analysis?
  YES → Repomix (if available)
  NO → Continue
  ↓
No structured docs?
  YES → Researcher agents

Quick Reference

Detailed Workflows & Examples

This document provides comprehensive workflow examples for the docs-seeker skill.

Parallel Exploration Strategy

When to Use Multiple Agents

Deploy parallel agents when:

• llms.txt contains more than 3 URLs
• Repository has multiple documentation directories
• Need to check multiple versions
• Comprehensive coverage required

How to Launch Parallel Agents

Use Task tool with Explore subagent:

Example for 5 URLs:
1. Launch all 5 Explore agents in single message
2. Each agent gets specific URLs to read
3. Each agent extracts relevant information
4. Wait for all agents to complete
5. Aggregate results

Agent Distribution Guidelines

Small documentation sets (1-3 URLs):

• Deploy 2 Explore agents to handle all URLs
• Simple, straightforward extraction
• Fastest for small amounts

Medium documentation sets (4-10 URLs):

• Deploy 3-6 Explore agents
• Balance workload evenly
• Group related URLs together

Large documentation sets (11+ URLs):

• Deploy 7-15 Explore agents (max)
• Prioritize most relevant URLs first
• Group related URLs together
• Balance workload evenly
• Avoid over-parallelization

Best Distribution Practices

1. Group related content: Keep related URLs with same agent 2. Balance workload: Distribute URLs evenly by estimated size 3. Prioritize critical docs: Assign core docs first 4. Avoid over-parallelization: Max 7 agents to avoid overwhelming 5. Sequential batches: For 15+ URLs, use two sequential batches

Workflow Examples

Example 1: Library with llms.txt (Simple)

Scenario: User requests documentation for Astro

Step 1: Initial Search (PRIORITIZE context7.com)
→ Try context7.com first: https://context7.com/withastro/astro/llms.txt
→ WebFetch: Read llms.txt content
→ Result: Contains 8+ documentation URLs (success!)

Alternative if context7.com fails:
→ WebSearch: "Astro llms.txt site:docs.astro.build"
→ Result: https://docs.astro.build/llms.txt found

Step 2: Process llms.txt
→ Already fetched in Step 1
→ Result: Contains 8 documentation URLs

Step 3: Parallel Exploration
→ Launch 3 Explorer agents simultaneously:

  Agent 1 (URLs 1-3):
  - https://docs.astro.build/en/getting-started/
  - https://docs.astro.build/en/install/
  - https://docs.astro.build/en/editor-setup/

  Agent 2 (URLs 4-6):
  - https://docs.astro.build/en/core-concepts/project-structure/
  - https://docs.astro.build/en/core-concepts/astro-components/
  - https://docs.astro.build/en/core-concepts/layouts/

  Agent 3 (URLs 7-8):
  - https://docs.astro.build/en/guides/configuring-astro/
  - https://docs.astro.build/en/reference/configuration-reference/

Step 4: Aggregate Findings
→ Collect results from all 3 agents
→ Synthesize into cohesive documentation

Step 5: Present Report
→ Format using standard output structure
→ Include source attribution
→ Note any gaps or limitations

Example 2: Library without llms.txt on context7 (Repository Analysis)

Scenario: User requests documentation for obscure library

Step 1: Try context7.com first
→ Attempt: https://context7.com/org/library-name/llms.txt
→ Result: Not found (404)

Step 2: Find GitHub Repository
→ WebSearch: "[library-name] github repository"
→ Result: https://github.com/org/library-name

Step 2a: Try context7.com with GitHub info
→ Attempt: https://context7.com/org/library-name/llms.txt
→ Result: Still not found

Step 3: Verify Repository
→ Check if it's official/active
→ Note star count, last update, license

Step 4: Check Repomix Installation
→ Bash: which repomix || npm install -g repomix

Step 5: Clone and Process Repository
→ Bash: git clone https://github.com/org/library-name /tmp/docs-analysis
→ Bash: cd /tmp/docs-analysis && repomix --output repomix-output.xml

Step 6: Analyze Repomix Output
→ Read: /tmp/docs-analysis/repomix-output.xml
→ Extract sections: README, docs/, examples/, CONTRIBUTING.md

Step 7: Present Findings
→ Format extracted documentation
→ Highlight key sections: installation, usage, API, examples
→ Note repository health: stars, activity, issues

Example 3: Topic-Specific Search (context7.com feature)

Scenario: User asks "How do I use the date picker in shadcn/ui?"

Step 1: Identify library and topic
→ Library: shadcn/ui
→ Topic: date picker

Step 2: Construct context7.com URL with topic parameter
→ URL: https://context7.com/shadcn-ui/ui/llms.txt?topic=date
→ WebFetch: Read filtered content
→ Result: Returns ONLY date-related documentation (highly targeted!)

Step 3: Present Findings
→ Focused documentation on date picker component
→ Installation instructions
→ Usage examples
→ API reference for date components
→ Much faster than reading entire documentation

Benefits of topic search:
- Reduces context usage (only relevant docs loaded)
- Faster results (no need to filter manually)
- More accurate (context7 filters for you)

Example 4: Multiple Versions Comparison

Scenario: User wants to compare v1 and v2 documentation

Step 1: Identify Version Requirements
→ User needs: v1.x and v2.x comparison
→ Primary focus: migration path and breaking changes

Step 2: Search Both Versions
→ WebSearch: "[library] v1 llms.txt"
→ WebSearch: "[library] v2 llms.txt"

Step 3: Launch Parallel Version Analysis
→ Deploy two sets of Explorer agents:

  Set A - v1 Documentation (3 agents):
  Agent 1: Core concepts v1
  Agent 2: API reference v1
  Agent 3: Examples v1

  Set B - v2 Documentation (3 agents):
  Agent 4: Core concepts v2
  Agent 5: API reference v2
  Agent 6: Examples v2

Step 4: Compare Findings
→ Analyze differences in:
  - Core concepts changes
  - API modifications
  - Breaking changes
  - New features in v2
  - Deprecated features from v1

Step 5: Present Side-by-Side Analysis
→ Migration guide format:
  - What changed
  - What's new
  - What's deprecated
  - Migration steps
  - Code examples (before/after)

Example 4: No Official Documentation (Research Fallback)

Scenario: Library with scattered documentation

Step 1: Exhaust Structured Sources
→ WebSearch: llms.txt (not found)
→ WebSearch: GitHub repo (not found or no docs)
→ WebSearch: Official website (minimal content)

Step 2: Deploy Researcher Agents
→ Launch 4 Researcher agents in parallel:

  Researcher 1: Official sources
  - Package registry page (npm, PyPI, etc.)
  - Official website
  - Release notes

  Researcher 2: Tutorial content
  - Blog posts
  - Getting started guides
  - Video tutorials

  Researcher 3: Community resources
  - Stack Overflow discussions
  - Reddit threads
  - GitHub issues/discussions

  Researcher 4: API & reference
  - Auto-generated docs
  - Code examples in wild
  - Community examples

Step 3: Aggregate Diverse Sources
→ Collect findings from all researchers
→ Cross-reference information
→ Identify consistent patterns
→ Note conflicting information

Step 4: Present Consolidated Report
→ Structure findings:
  - Overview (from multiple sources)
  - Installation (verified approach)
  - Basic usage (community examples)
  - Common patterns (from discussions)
  - Known issues (from GitHub/SO)
  - Caveats about source quality

Example 5: Large Documentation Set (Two-Phase)

Scenario: Framework with 20+ documentation pages

Step 1: Analyze Documentation Structure
→ WebFetch: llms.txt
→ Result: Contains 24 URLs across multiple categories

Step 2: Prioritize URLs
→ Categorize by importance:
  - Critical (8): Getting started, core concepts, API
  - Important (10): Guides, integrations, examples
  - Supplementary (6): Advanced topics, internals

Step 3: Phase 1 - Critical Documentation
→ Launch 5 Explorer agents:
  Agent 1: URLs 1-2 (Getting started)
  Agent 2: URLs 3-4 (Installation & setup)
  Agent 3: URLs 5-6 (Core concepts)
  Agent 4: URLs 7-8 (Basic API)
  Agent 5: URL 9 (Configuration)

→ Wait for completion
→ Quick review of coverage

Step 4: Phase 2 - Important Documentation
→ Launch 5 Explorer agents:
  Agent 6: URLs 10-11 (Routing guide)
  Agent 7: URLs 12-13 (Data fetching)
  Agent 8: URLs 14-15 (Authentication)
  Agent 9: URLs 16-17 (Deployment)
  Agent 10: URLs 18-19 (Integrations)

Step 5: Evaluate Need for Phase 3
→ Assess user needs
→ If supplementary topics required:
  - Launch final batch for advanced topics
→ If basics sufficient:
  - Note additional resources in report

Step 6: Comprehensive Report
→ Synthesize all phases
→ Organize by topic
→ Cross-reference related sections
→ Highlight critical workflows

Performance Optimization Strategies

Minimize Sequential Operations

Bad approach:

1. Read URL 1 with WebFetch
2. Wait for result
3. Read URL 2 with WebFetch
4. Wait for result
5. Read URL 3 with WebFetch
6. Wait for result
Time: 3x single URL fetch time

Good approach:

1. Launch 3 Explorer agents simultaneously
2. Each reads one URL
3. All complete in parallel
4. Aggregate results
Time: ~1x single URL fetch time

Batch Related Operations

Group by topic:

Agent 1: Authentication (login.md, oauth.md, sessions.md)
Agent 2: Database (models.md, queries.md, migrations.md)
Agent 3: API (routes.md, middleware.md, validation.md)

Group by content type:

Agent 1: Tutorials (getting-started.md, quickstart.md)
Agent 2: Reference (api-ref.md, config-ref.md)
Agent 3: Guides (best-practices.md, troubleshooting.md)

Use Caching Effectively

Repository analysis:

1. First request: Clone + Repomix (slow)
2. Save repomix-output.xml
3. Subsequent requests: Reuse saved output (fast)
4. Refresh only if repository updated

llms.txt content:

1. First fetch: WebFetch llms.txt
2. Store URL list in session
3. Reuse for follow-up questions
4. Re-fetch only if user changes version

Fail Fast Strategy

Set timeouts:

1. WebSearch: 30 seconds max
2. WebFetch: 60 seconds max
3. Repository clone: 5 minutes max
4. Repomix processing: 10 minutes max

Quick fallback:

1. Try llms.txt (30 sec timeout)
2. If fails → immediately try repository
3. If fails → immediately launch researchers
4. Don't retry failed methods

Common Pitfalls & Solutions

Pitfall 1: Over-Parallelization

Problem: Launching 15 agents at once Impact: Slow, overwhelming, hard to track Solution: Max 7 agents per batch, use phases for large sets

Pitfall 2: Unbalanced Workload

Problem: Agent 1 gets 1 URL, Agent 2 gets 10 URLs Impact: Agent 1 finishes fast, Agent 2 bottleneck Solution: Distribute evenly or by estimated size

Pitfall 3: Ignoring Errors

Problem: Agent fails, continue without checking Impact: Incomplete documentation, missing sections Solution: Check all agent outputs, retry or note failures

Pitfall 4: Poor Aggregation

Problem: Concatenating agent outputs without synthesis Impact: Redundant, disorganized information Solution: Synthesize findings, organize by topic, deduplicate

Pitfall 5: Not Verifying Sources

Problem: Using first result without verification Impact: Outdated or unofficial documentation Solution: Check official status, version, date

Decision Trees

Choosing Documentation Strategy

Start
  ↓
Does llms.txt exist?
  ↓
YES → How many URLs?
  ↓
  1-3 URLs → Single WebFetch/Explorer
  4+ URLs → Parallel Explorers
  ↓
NO → Is there GitHub repo?
  ↓
  YES → Is Repomix feasible?
    ↓
    YES → Use Repomix
    NO → Manual exploration with Explorers
  ↓
  NO → Deploy Researcher agents

Choosing Agent Count

URL Count < 3
  ↓
Single Explorer
  ↓
URL Count 4-10
  ↓
3-5 Explorers
  ↓
URL Count 11-20
  ↓
5-7 Explorers (or two phases)
  ↓
URL Count > 20
  ↓
Two-phase approach:
  Phase 1: 5 agents (critical)
  Phase 2: 5 agents (important)

Advanced Scenarios

Scenario: Multi-Language Documentation

Challenge: Documentation in multiple languages

Approach: 1. Identify target language from user 2. Search for language-specific llms.txt 3. If not found, search for English version 4. Note language limitations in report 5. Offer to translate key sections if needed

Scenario: Framework with Plugins

Challenge: Core framework + 50 plugin docs

Approach: 1. Focus on core framework first 2. Ask user which plugins they need 3. Launch targeted search for specific plugins 4. Avoid trying to document everything 5. Note available plugins in report

Scenario: Documentation Under Construction

Challenge: New release with incomplete docs

Approach: 1. Note documentation status upfront 2. Combine available docs with repository analysis 3. Check GitHub issues for documentation requests 4. Provide code examples from tests/examples 5. Clearly mark sections as "inferred from code"

Scenario: Conflicting Information

Challenge: Multiple sources with different approaches

Approach: 1. Identify primary official source 2. Note version differences between sources 3. Present both approaches with context 4. Recommend official/latest approach 5. Explain why conflict exists (e.g., version change)