Claude Code Plugins

Community-maintained marketplace

Feedback

|

Install Skill

1Download skill
2Enable skills in Claude

Open claude.ai/settings/capabilities and find the "Skills" section

3Upload to Claude

Click "Upload skill" and select the downloaded ZIP file

Note: Please verify skill by going through its instructions before using it.

SKILL.md

name prepare-docs
description Use when user requests automated documentation preparation. Triggers include: - Korean: "문서 준비해줘", "문서화 준비", "문서 요구사항 작성", "자동 문서화 준비" - English: "prepare documentation", "setup doc requirements", "prepare for automated docs" - Context: User wants to prepare all requirements for automated documentation generation This skill discovers sources, defines structure, and saves complete requirements to file for use with write-docs skill (automated generation).

Documentation Preparation Skill (Automated Workflow - Step 1)

Overview

This skill prepares complete documentation requirements for automated generation. It:

  1. Discovers all sources - Core sources + related files (tests, types, examples)
  2. Defines document structure - Based on doc type and project templates
  3. Builds content map - What to document from where
  4. Saves requirements file - Complete specification for write-docs skill

When to use this skill:

  • User says "문서 준비해줘" or "prepare documentation"
  • Starting automated documentation workflow
  • User wants hands-off doc generation after initial setup

⚠️ IMPORTANT - Automated Workflow:

  • Interactive setup - Steps 1-3 ask user questions to define scope
  • Automated discovery - Step 4 automatically finds related sources and builds content map
  • Saves requirements to file - Always outputs docs/doc-requirements.md

Workflow: 5-Step Preparation Process

Step 1: Collect Source Links

Questions to ask:

  1. "어떤 소스를 분석할까요?" (What sources should I analyze?)

    • GitHub repositories (URLs or local paths)
    • Web pages (documentation sites, blog posts)
    • Local files (code files, markdown docs, PDFs)
    • Google Drive documents (check for MCP server)
    • Notion pages (check for MCP server)
  2. "중점적으로 분석할 키워드나 토픽이 있나요?" (Any specific keywords or topics to focus on?)


Step 2: Select Document Type

Ask: "어떤 문서를 만들까요?" (What type of documentation should I create?)

Options:

  1. API Reference

    • Audience: Developers integrating with the code
    • Focus: Functions, classes, parameters, return values, examples
    • Structure: Organized by module/class/function
  2. System Overview

    • Audience: Engineers understanding architecture
    • Focus: Components, data flow, design principles, communication patterns
    • Structure: Top-down from high-level to implementation details
  3. Tutorial

    • Audience: Users learning how to use the system
    • Focus: Step-by-step instructions, practical examples, common pitfalls
    • Structure: Sequential steps with verification checkpoints
  4. Custom

    • User specifies their own document type and purpose

Step 3: Define Document Structure

Based on document type selected in Step 2, present default sections and ask user to customize.

Workflow:

  1. Show default sections for selected document type (see below)

  2. Ask user: "기본 구조에서 제외할 항목이 있나요?" (Any sections to remove from default structure?)

    • User can specify sections to exclude
    • Example: "Performance Characteristics 제외"
  3. Ask user: "추가로 포함할 항목이 있나요?" (Any additional sections to include?)

    • User can add custom sections
    • Examples: Migration Guide, Troubleshooting, FAQ, Benchmarks, Comparison with Alternatives
  4. Confirm final structure - Show final section list before proceeding to Step 4

Note: If user mentions a project template, use that instead of default structure.


Default sections by document type:

For API Reference:

  • Overview (purpose of this module/API)
  • Core Concepts (key abstractions)
  • Functions (organized by category)
  • Types (data structures)
  • Constants
  • Usage Patterns
  • Limitations
  • Related APIs

For System Overview:

  • Introduction (purpose, use cases)
  • Architecture (high-level design)
  • Components (individual parts + responsibilities)
  • Data Flow (how information moves)
  • State Management
  • Communication Patterns
  • Configuration
  • Error Handling
  • Performance Characteristics

For Tutorial:

  • Introduction (what you'll learn, time estimate)
  • Prerequisites (required knowledge, tools)
  • Step 1, 2, 3... (sequential instructions)
  • Testing Your Implementation
  • Common Pitfalls
  • Next Steps

Note: Order sections logically (general → specific) in final structure.


Step 4: Content Discovery and Source Expansion

Purpose: Start from core sources and actively discover all related sources needed for comprehensive documentation.

Workflow:

  1. Analyze core sources (from Step 1)

    • Identify documentable items: functions, types, classes, concepts
    • Note primary information available
  2. For each item, discover related sources:

    For Functions/Methods:

    • ✅ Test files (usage examples, edge cases)
    • ✅ Type definitions (parameter types, return types)
    • ✅ Related functions (callers, callees)
    • ✅ Documentation comments (inline docs)
    • ✅ Error handling (related error types)

    For Types/Classes:

    • ✅ Constructors and factory functions
    • ✅ Methods and properties
    • ✅ Usage examples in tests
    • ✅ Related types (inheritance, composition)
    • ✅ Serialization/deserialization code

    For Concepts/Modules:

    • ✅ Design documents (docs/, wiki, ADRs)
    • ✅ README files
    • ✅ Example code (examples/, demos/)
    • ✅ Related modules (imports, dependencies)
  3. Build content map:

    ## Content Map
    
    ### Function: parse()
    **Primary source:** src/parser.rs:45-60
    **Related sources found:**
    - tests/parser_test.rs:100-150 (usage examples, edge cases)
    - src/types.rs:10-25 (Token type definition)
    - docs/design.md:30-45 (recursive descent explanation)
    - examples/basic_usage.rs:20-35 (real-world example)
    
    **Coverage:** ✅ Complete (signature, behavior, examples, design rationale)
    
    ### Type: Token
    **Primary source:** src/types.rs:10-25
    **Related sources found:**
    - src/lexer.rs:80-120 (Token creation)
    - tests/token_test.rs:50-80 (all variants tested)
    
    **Coverage:** ✅ Complete (definition, creation, usage)
    
    ### Concept: Error Recovery
    **Primary source:** src/parser.rs:300-350
    **Related sources found:**
    - (none found in tests/)
    - (no design docs found)
    
    **Coverage:** ⚠️ Partial (implementation exists, no design rationale or examples)
    

Example Workflow:

User provides: src/parser.rs

Claude discovers:
1. Functions: parse(), tokenize(), validate()
2. For parse():
   → Search tests/ for "parse" → Found tests/parser_test.rs
   → Check imports in test file → Found src/types.rs (Token definition)
   → Search docs/ for "parse" → Found docs/design.md
   → Check examples/ → Found examples/basic_usage.rs
3. For Token type:
   → Search for "Token" in codebase → Found src/lexer.rs (creation)
   → Found tests/token_test.rs (all variants)
4. For validate():
   → Search tests/ for "validate" → Found tests/validator_test.rs
   ...

Result: Comprehensive source list discovered automatically

⚠️ Trust all sources - Don't judge quality or note contradictions. If contradictions exist, write-docs will handle them during generation.


Step 5: Save Requirements File

Always save to: docs/doc-requirements.md

File contains:

  • Document type and target audience
  • All discovered sources (core + related)
  • Document structure with sections
  • Content map (what to document from where)

File format:

# Documentation Requirements

**Created**: 2025-10-22
**Document Type**: API Reference
**Target Audience**: Developers integrating the parser

## Discovered Sources

### Core Sources (user-provided):
- src/parser.rs

### Related Sources (discovered):
- tests/parser_test.rs (usage examples)
- src/types.rs (type definitions)
- docs/design.md (design rationale)
- examples/basic_usage.rs (real-world usage)
- src/lexer.rs (related functionality)
- tests/token_test.rs (type examples)

Total: 7 sources

## Document Structure

- [ ] Introduction
- [ ] Core Concepts
- [ ] Functions (parse, tokenize, validate)
- [ ] Types (Token, AST, ParseError)
- [ ] Usage Patterns
- [ ] Limitations

## Content Map

### Function: parse()
**Primary source:** src/parser.rs:45-60
**Related sources:**
- tests/parser_test.rs:100-150 (usage examples)
- src/types.rs:10-25 (Token type)
- docs/design.md:30-45 (design rationale)

### Type: Token
**Primary source:** src/types.rs:10-25
**Related sources:**
- src/lexer.rs:80-120 (construction)
- tests/token_test.rs:50-80 (examples)

After saving, confirm to user:

"문서 요구사항을 docs/doc-requirements.md에 저장했습니다. 'write-docs 실행해줘'를 입력하면 자동으로 문서를 생성합니다."


Tips for Effective Preparation

  1. Ask before assuming - If user's intent is unclear during initial setup, ask clarifying questions
  2. Suggest recommendations - "parser 모듈이니까 API Reference가 적합해 보입니다"
  3. Document discovery thoroughly - Build comprehensive content map showing all discovered sources for each documentable item

Conclusion

After completing this preparation workflow, you should have:

✅ All sources discovered (core + related) ✅ Document type and structure defined ✅ Content map showing what to document from where ✅ Requirements file saved to docs/doc-requirements.md