docs-retrieval

Documentation Retrieval Skill

This skill enables efficient retrieval of documentation context from the hierarchical documentation system.

Variables

| Variable | Default | Description | |----------|---------|-------------| | MAX_TOKENS | 2000 | Target token budget for context loading | | LOAD_FULL_CONTEXT | false | Use full-context.md instead of targeted pages | | LOCAL_FIRST | true | Check ai-docs before web search |

Instructions

MANDATORY - Always check local documentation before web searches.

• Start with _index.toon files for navigation
• Load targeted page summaries, not full contexts
• Consolidate multi-library context using the format below
• Pass pre-loaded context to sub-agents

Red Flags - STOP and Reconsider

If you're about to:

• Load full-context.md for a simple question
• Web search without checking local docs first
• Let sub-agents navigate from scratch instead of passing context
• Load all libraries "just in case"

STOP -> Use targeted retrieval patterns below -> Then proceed

Workflow

1. [ ] CHECKPOINT: Have you identified what libraries you need?
2. [ ] Check ai-docs/libraries/_index.toon for available docs
3. [ ] Navigate to specific library _index.toon
4. [ ] Identify relevant pages from index
5. [ ] Load only the page summaries you need
6. [ ] CHECKPOINT: Are you within token budget?

Cookbook

Direct Navigation

• IF: You know the library and topic
• THEN: Read cookbook/direct-navigation.md
• RESULT: Fastest path to specific information

Keyword Search

• IF: Uncertain which library has what you need
• THEN: Read cookbook/keyword-search.md
• RESULT: Find relevant docs by matching keywords

Multi-Library Gathering

• IF: Task involves multiple libraries
• THEN: Read cookbook/multi-library.md
• RESULT: Consolidated context from multiple sources

Full Context Loading

• IF: Need comprehensive understanding (migrations, tutorials)
• THEN: Read cookbook/full-context.md
• WARNING: High token cost (5,000-15,000 tokens)

When to Use This Skill

• Before implementing features involving external libraries
• When debugging errors from external dependencies
• When spawning sub-agents that need library context
• When uncertain about API syntax or behavior

Retrieval Patterns

Pattern 1: Direct Navigation (Know What You Need)

When you know the library and topic:

1. @ai-docs/libraries/{library}/_index.toon
   -> Read overview and common_tasks

2. Find matching task or section
   -> Note the page path

3. @ai-docs/libraries/{library}/{section}/pages/{page}.toon
   -> Get detailed summary with gotchas and patterns

Example: Need BAML retry configuration

1. @ai-docs/libraries/baml/_index.toon
   -> common_tasks: "Handle errors gracefully" -> guide/error-handling

2. @ai-docs/libraries/baml/guide/pages/error-handling.toon
   -> RetryPolicy syntax, gotchas about timeouts

Pattern 2: Keyword Search (Uncertain What Exists)

When you're not sure which library or page:

1. @ai-docs/libraries/_index.toon
   -> Scan library descriptions and keywords

2. Match your need against keywords
   -> Identify candidate libraries

3. For each candidate:
   -> @ai-docs/libraries/{lib}/_index.toon
   -> Check if relevant content exists

4. Load specific pages from best match

Example: Need "structured output parsing"

1. @ai-docs/libraries/_index.toon
   -> BAML: "Structured LLM outputs with type safety" [match]
   -> MCP: "Tool integration protocol" [no match]

2. @ai-docs/libraries/baml/_index.toon
   -> Confirms: type system, parsing, validation

3. Load relevant BAML pages

Pattern 3: Multi-Library Gathering (Complex Tasks)

When task involves multiple libraries:

1. List all libraries involved in task

2. For each library:
   -> Load _index.toon
   -> Identify relevant pages
   -> Load page summaries

3. Consolidate into single context block

4. OR: Spawn docs-context-gatherer agent

Pattern 4: Full Context (Deep Work)

When you need comprehensive understanding:

@ai-docs/libraries/{library}/full-context.md

Use sparingly - this loads everything (~5,000-15,000 tokens)

Appropriate for:

• Major migrations
• Writing tutorials
• Architectural decisions
• First-time deep learning

Context Consolidation Format

When gathering context from multiple pages, consolidate as:

## Documentation Context

### {Library}: {Topic}
**Purpose**: {1-2 sentence purpose}
**Key Points**:
- {concept 1}
- {concept 2}
**Gotchas**:
- {warning 1}
- {warning 2}
**Pattern**:

{minimal code example}

### {Library}: {Another Topic}
...

---
Sources: {list of page paths loaded}
Tokens: ~{estimate}

Budget Management

Token Estimates by File Type

| File Type | Typical Size | |-----------|--------------| | _index.toon (category) | 100-150 tokens | | _index.toon (library) | 150-250 tokens | | _index.toon (section) | 100-200 tokens | | pages/*.toon | 250-450 tokens | | full-context.md | 5,000-15,000 tokens |

Budget Guidelines

| Task Type | Target Budget | Loading Strategy | |-----------|---------------|------------------| | Quick fix | 300-500 | 1 page summary | | Single feature | 800-1,200 | 2-3 page summaries | | Integration | 1,500-2,500 | Library index + 4-6 pages | | Multi-library | 2,000-4,000 | Multiple library indexes + key pages | | Full context | 5,000+ | full-context.md |

Efficiency Tips

1. Index files are cheap navigation - Read them freely
2. Page summaries are high-signal - Designed for this purpose
3. Gotchas prevent expensive mistakes - Always worth loading
4. Code patterns are copy-paste ready - High value per token
5. full-context.md is last resort - Use targeted loading first

Common Retrieval Scenarios

Scenario: Implementing a Feature

1. Identify: What libraries does this feature use?
2. Navigate: Find relevant pages in each library
3. Load: Page summaries for implementation guidance
4. Note: Gotchas before writing code
5. Proceed: Implement with context loaded

Scenario: Debugging an Error

1. Identify: Which library produced the error?
2. Search: Error-related pages in that library
3. Load: Error handling and troubleshooting pages
4. Check: Known gotchas that might explain the issue
5. Proceed: Debug with context

Scenario: Spawning Sub-Agent

1. Analyze: What docs will sub-agent need?
2. Gather: Load relevant pages NOW
3. Consolidate: Format as context block
4. Include: Add to sub-agent spawn prompt
5. Spawn: Sub-agent has pre-loaded context

Scenario: Uncertain Which Library

1. Start: @ai-docs/libraries/_index.toon
2. Scan: Library descriptions and keywords
3. Match: Find libraries relevant to your need
4. Explore: Check promising library indexes
5. Load: Pages from best matching library

Scenario: AI Tool Documentation

When you need information about AI tools (Claude Code, BAML, MCP, TOON, etc.):

1. Check local ai-docs FIRST:
   @ai-docs/libraries/claude-code/_index.toon
   @ai-docs/libraries/baml/_index.toon
   @ai-docs/libraries/toon/_index.toon

2. Navigate using same patterns as any library:
   -> Find section in _index.toon
   -> Load relevant page summaries
   -> Use full-context.md for comprehensive needs

3. Fall back to web search/fetch when:
   - Local docs don't cover the specific topic
   - Need time-sensitive info (release dates, latest versions)
   - Local docs are insufficient after checking
   - User explicitly requests current web information

Why local first:

• Faster (no network round-trip)
• Curated context (TOON format optimized for LLMs)
• Gotchas pre-extracted
• Token-efficient vs. full web pages

When to web search:

• Topic not found after checking local index
• Need current/live information
• User explicitly asks for latest from web

Anti-Patterns

Don't: Load full-context.md for Simple Questions

Bad: Load 15K tokens to answer "