Andrew Yim
dbfaa883cd
- Update skill count from 38 to 50 across all docs - Add skills 19-28, 31-32 to README SEO table - Remove "Future SEO Skills (19-28 reserved)" placeholder - Update AGENTS.md SEO section with slash commands and Ahrefs integration notes - Update directory listing from "11-30" to "11-32" Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
244 lines
8.6 KiB
Markdown
244 lines
8.6 KiB
Markdown
# AGENTS.md
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This file documents how to effectively use Claude Code's specialized agents (via the Task tool) when working with this skills repository.
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## Agent Types for Skills Development
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### Explore Agent
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**Best for:** Understanding skill structure, finding patterns, researching existing implementations
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```
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Use Task tool with subagent_type=Explore for:
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- "How is the SEO technical audit skill structured?"
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- "Find all skills that use Python scripts"
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- "What MCP tools are commonly used in desktop skills?"
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- "Compare the structure of GTM audit vs GTM manager"
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```
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**When to use:**
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- Codebase exploration before creating new skills
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- Finding patterns across multiple skills
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- Understanding how existing features are implemented
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### Plan Agent
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**Best for:** Designing new skills, planning refactors, architectural decisions
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```
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Use Task tool with subagent_type=Plan for:
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- "Plan a new skill for Google Analytics 4 audit"
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- "Design the structure for a multi-step SEO workflow"
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- "Plan the refactoring of notion-organizer to support batch operations"
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```
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**When to use:**
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- Before creating a new skill (design first)
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- When refactoring affects multiple files
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- For complex feature implementations
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### General-Purpose Agent
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**Best for:** Multi-step tasks that combine research and action
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```
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Use Task tool with subagent_type=general-purpose for:
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- "Create a new skill for PDF generation following the existing patterns"
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- "Audit all Jamie skills for consistent branding guidelines"
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- "Update all SEO skills to use a shared utility module"
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```
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**When to use:**
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- Complex tasks requiring both exploration and implementation
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- Tasks spanning multiple skills or directories
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### Bash Agent
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**Best for:** Git operations, running scripts, file system tasks
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```
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Use Task tool with subagent_type=Bash for:
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- "Run the skill validation script on all custom skills"
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- "Create git commits for each modified skill separately"
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- "Execute the token analyzer on all SKILL.md files"
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```
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**When to use:**
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- Running Python scripts in the skills
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- Git operations (commits, branches, diffs)
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- Batch file operations
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## Skill-Specific Agent Recommendations
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### Creating New Skills
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| Task | Recommended Agent | Notes |
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|------|-------------------|-------|
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| Research existing patterns | Explore | Find similar skills first |
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| Design skill structure | Plan | Define scope before coding |
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| Generate boilerplate | general-purpose | Use init_skill.py template |
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| Write SKILL.md/CLAUDE.md | Direct (no agent) | Simple file writing |
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| Implement scripts | Direct (no agent) | Write Python/Bash directly |
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| Validate skill | Bash | Run validation scripts |
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### Auditing & Maintenance
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| Task | Recommended Agent | Notes |
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|------|-------------------|-------|
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| Audit skill completion | Explore | Check for missing files |
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| Update multiple skills | general-purpose | Batch operations |
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| Refactor shared code | Plan + general-purpose | Plan first, then execute |
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| Test skill scripts | Bash | Run tests and verify |
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### Documentation
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| Task | Recommended Agent | Notes |
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|------|-------------------|-------|
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| Generate skill summaries | Explore | Gather info from all skills |
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| Update CLAUDE.md | Direct (no agent) | Simple documentation |
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| Create usage examples | Explore + Direct | Research then document |
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## Parallel Agent Execution
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For independent tasks, launch multiple agents simultaneously:
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```
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# Good: These tasks are independent
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Task 1: Explore - "Find all skills missing requirements.txt"
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Task 2: Explore - "List all skills with desktop/SKILL.md"
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Task 3: Bash - "Count lines of Python code per skill"
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# Bad: These depend on each other
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Task 1: Plan - "Design the new skill structure"
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Task 2: general-purpose - "Implement the planned skill" # Needs Task 1 result
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```
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## Domain-Specific Routing
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### SEO Skills (11-32)
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- Use **Explore** to understand existing SEO script patterns
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- Python scripts in these skills follow `base_client.py` patterns (RateLimiter, ConfigManager, BaseAsyncClient)
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- `11-seo-comprehensive-audit` orchestrates skills 12-18 for unified audits
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- Skills 19-28 provide advanced SEO capabilities (keyword strategy, SERP analysis, position tracking, link building, content strategy, e-commerce, KPI framework, international SEO, AI visibility, knowledge graph)
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- Skills 31-32 cover competitor intelligence and crawl budget optimization
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- All SEO skills integrate with Ahrefs MCP tools and output to the Notion SEO Audit Log database
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- Slash commands available: `/seo-keyword-strategy`, `/seo-serp-analysis`, `/seo-position-tracking`, `/seo-link-building`, `/seo-content-strategy`, `/seo-ecommerce`, `/seo-kpi-framework`, `/seo-international`, `/seo-ai-visibility`, `/seo-knowledge-graph`, `/seo-competitor-intel`, `/seo-crawl-budget`
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### GTM Skills (60-69)
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- Use **gtm-manager** agent for GTM-specific debugging
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- Requires Chrome GTM Debug profile for live testing
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- Scripts interact with GTM API and dataLayer
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### Jamie Clinic Skills (40-49)
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- Brand compliance is critical - check `references/` for guidelines
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- Korean language content - verify encoding in scripts
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- Instagram/YouTube skills may need API credentials
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### Notion Skills (31-39)
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- Use Notion MCP tools (`mcp__plugin_Notion_notion__*`) directly
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- Skills export data to Working with AI database
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- Check schema compatibility before creating pages
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### NotebookLM Skills (50-59)
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- Use `notebooklm` CLI for all operations (installed via `pip install notebooklm-py`)
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- Requires authentication: `notebooklm login` (browser-based Google OAuth)
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- Four specialized skills for different workflows:
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| Skill | Purpose | Key Commands |
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|-------|---------|--------------|
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| 50-notebooklm-agent | Q&A with citations | `notebooklm ask "question" --json` |
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| 51-notebooklm-automation | Notebook management | `notebooklm create`, `source add`, `list` |
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| 52-notebooklm-studio | Content generation | `notebooklm generate audio/video/quiz` |
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| 53-notebooklm-research | Research workflows | `notebooklm source add-research "topic"` |
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**Long-running operations:** Use subagent pattern for generation/research:
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```
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# Start generation (non-blocking)
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notebooklm generate audio "instructions" --json
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# Spawn subagent to wait and download
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Task(
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prompt="Wait for artifact {id} then download: notebooklm artifact wait {id} && notebooklm download audio ./output.mp3",
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subagent_type="general-purpose"
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)
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```
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**Parallel workflows:** Set `NOTEBOOKLM_HOME` per agent to avoid context conflicts.
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### Reference Curator Skills (90-99)
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- Use **reference-curator-pipeline** for full automated curation workflows
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- Runs as background task, coordinates all 6 skills in sequence
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- Handles QA loops automatically (max 3 refactor, 2 deep_research iterations)
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- Supports three input modes: topic (full pipeline), URLs (skip discovery), manifest (resume)
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```
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# Full pipeline from topic
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/reference-curator-pipeline "Claude Code best practices" --max-sources 5
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# Direct URL crawling (skip discovery)
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/reference-curator-pipeline https://docs.anthropic.com/en/docs/prompt-caching
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# Resume from manifest
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/reference-curator-pipeline ./manifest.json --auto-approve
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```
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Individual skills can still be run separately:
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- `/reference-discovery` - Search and validate sources
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- `/web-crawler` - Crawl URLs with auto-backend selection
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- `/content-repository` - Manage stored documents
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- `/content-distiller` - Summarize and extract key concepts
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- `/quality-reviewer` - QA scoring and routing
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- `/markdown-exporter` - Export to project files or JSONL
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## Background Agents
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For long-running tasks, use `run_in_background: true`:
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```
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# Good candidates for background execution:
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- Full skill audit across all 50 skills
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- Running Python tests on multiple skills
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- Generating comprehensive documentation
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# Not suitable for background:
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- Interactive debugging
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- Tasks requiring user input
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- Quick file operations
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```
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## Agent Handoff Patterns
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### Research → Implementation
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1. **Explore agent**: Gather context and patterns
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2. **Plan agent**: Design the approach
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3. **Direct implementation**: Write the code
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4. **Bash agent**: Test and validate
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### Bug Fix Workflow
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1. **Explore agent**: Find related code and understand the issue
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2. **Direct implementation**: Fix the bug
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3. **Bash agent**: Run tests to verify
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### New Skill Creation
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1. **Explore agent**: Study 2-3 similar existing skills
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2. **Plan agent**: Design skill scope and structure
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3. **Bash agent**: Run `init_skill.py` to generate boilerplate
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4. **Direct implementation**: Write SKILL.md/CLAUDE.md and scripts
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5. **Bash agent**: Validate and test
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## Notes
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- Always prefer **Explore** for open-ended questions about the codebase
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- Use **Plan** before major changes to get user approval
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- Direct tool use (Read, Edit, Write) is faster for simple operations
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- Agents have full context of the conversation when spawned
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