chore(rebar): Pier_T_rebar 작업 폴더 초기 구성 및 외부 의존 파일 통합
- Code\rebar_pipeline, review_pipeline, pier-program을 정션에서 실제 폴더로 전환 - Code\rhino_mcp 및 CLAUDE_rhino배경.md 추가, .mcp.json은 PYTHONPATH로 새 위치 사용 - pier-program 내부 .git 제거(이력은 backup\pier-program_20261028.bundle에 보관) - worktree 가이드 반영(CLAUDE.local.md, HANDOFF.md) - D:\Rhino-MCP 경로 참조를 새 위치로 치환 Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
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---
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name: ai-agent-code-learning
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classification: capability
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classification-reason: Highly likely to be subsumed by model's native capabilities
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deprecation-risk: high
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description: |
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Antigravity Platform learning — configure and optimize Antigravity Platform settings, tips, and workflows.
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Triggers: learn, setup, ai-agent code, optimize, 학습, 설정, 최적화.
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argument-hint: "[learn|setup|upgrade] [level]"
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agent: ai-agent-code-guide
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allowed-tools:
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- Read
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- Write
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- Edit
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- Glob
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- Grep
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- Bash
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- AskUserQuestion
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user-invocable: true
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imports:
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- ${PLUGIN_ROOT}/templates/shared/naming-conventions.md
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next-skill: null
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pdca-phase: null
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task-template: "[Learn] Antigravity Platform {level}"
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hooks:
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Stop:
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- type: command
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command: "node ${HMCMKIT_PLUGIN_ROOT}/scripts/learning-stop.js"
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timeout: 10000
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---
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# Antigravity Platform Learning Skill
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> Master Antigravity Platform configuration and optimization
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## Actions
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| Action | Description | Example |
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|--------|-------------|---------|
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| `learn` | Start learning guide | `/ai-agent-code-learning learn 1` |
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| `setup` | Auto-generate settings | `/ai-agent-code-learning setup` |
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| `upgrade` | Latest features guide | `/ai-agent-code-learning upgrade` |
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### learn [level]
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Learning content by level:
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- **Level 1**: Basics - Writing agents.md, Using Plan Mode
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- **Level 2**: Automation - Commands, Hooks, Permission management
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- **Level 3**: Specialization - Agents, Skills, MCP integration
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- **Level 4**: Team Optimization - GitHub Action, Team rule standardization
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- **Level 5**: PDCA Methodology - hmcmkit methodology learning
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### setup
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Auto-generate appropriate settings after analyzing current project:
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1. Analyze/generate agents.md
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2. Check .ai-agent/ folder structure
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3. Suggest required configuration files
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### upgrade
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Guide to latest Antigravity Platform features and best practices.
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## Learning Levels
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### Level 1: Basics (15 min)
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```markdown
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## What is agents.md?
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A shared knowledge repository for the team. When AI Agent makes mistakes,
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add rules to prevent the same mistakes from recurring.
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## Example
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# Development Workflow
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## Package Management
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- **Always use `pnpm`** (`npm`, `yarn` prohibited)
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## Coding Conventions
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- Prefer `type`, avoid `interface`
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- **Never use `enum`** → Use string literal unions
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## Prohibited
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- ❌ No console.log (use logger)
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- ❌ No any type
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```
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### Level 2: Automation (30 min)
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```markdown
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## What are Slash Commands?
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Execute repetitive daily tasks with `/command-name`.
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## Command Location
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.ai-agent/commands/{command-name}.md
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## PostToolUse Hook
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Auto-formatting after code modification:
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// .ai-agent/settings.local.json
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{
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"hooks": {
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"PostToolUse": [{
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"matcher": "Write|Edit",
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"hooks": [{
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"type": "command",
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"command": "pnpm format || true"
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}]
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}]
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}
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}
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```
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### Level 3: Specialization (45 min)
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```markdown
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## What are Sub-agents?
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AI agents specialized for specific tasks.
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## What are Skills?
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Domain-specific expert context. AI Agent auto-references when working on related tasks.
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## MCP Integration
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Connect external tools (Slack, GitHub, Jira, etc.) via .mcp.json.
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```
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### Level 4: Team Optimization (1 hour)
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```markdown
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## PR Automation with GitHub Action
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Mention @ai-agent in PR comments to auto-update documentation.
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## Team Rule Standardization
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1. Manage agents.md with Git
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2. Add rules during PR review
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3. Gradually accumulate team knowledge
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## Agent Teams (v1.5.1)
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Parallel PDCA execution with multiple AI agents working simultaneously.
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Requirements:
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HMCMKIT_EXPERIMENTAL_AGENT_TEAMS=1
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Usage:
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/pdca team {feature} Start team mode
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/pdca team status Check teammate progress
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/pdca team cleanup End team session
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Team composition by level:
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Dynamic: 2 teammates (developer, qa)
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Enterprise: 4 teammates (architect, developer, qa, reviewer)
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```
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### Level 5: PDCA Methodology
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```markdown
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## What is PDCA?
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Document-driven development methodology.
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Plan → Design → Do → Check → Act
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## Folder Structure
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docs/
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├── 01-plan/ # Planning
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├── 02-design/ # Design
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├── 03-analysis/ # Analysis
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└── 04-report/ # Reports
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## Learn More
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Use /pdca skill to learn PDCA methodology.
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```
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### Level 6: Advanced Features (v1.5.1)
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```markdown
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## Output Styles
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Customize how AI Agent responds based on your project level.
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Available styles:
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hmcmkit-learning Best for beginners (learning points, TODO markers)
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hmcmkit-pdca-guide Best for PDCA workflows (status badges, checklists)
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hmcmkit-enterprise Best for architects (tradeoff analysis, cost impact)
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Usage:
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/output-style Select interactively
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/output-style hmcmkit-learning Apply directly
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Auto-recommendation:
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Starter → hmcmkit-learning
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Dynamic → hmcmkit-pdca-guide
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Enterprise → hmcmkit-enterprise
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## Agent Memory
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All hmcmkit agents automatically remember context across sessions.
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No configuration needed.
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Memory scopes:
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project 9 agents remember per-project context (.ai-agent/agent-memory/)
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user 2 agents remember cross-project learning (~/.ai-agent/agent-memory/)
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Agents with user-scope memory:
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starter-guide Remembers your learning progress across projects
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pipeline-guide Remembers your pipeline preferences globally
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## Agent Teams
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Parallel PDCA execution for Dynamic and Enterprise projects.
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See Level 4 for details.
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```
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## Output Format
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```
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📚 Antigravity Platform Learning Complete!
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**Current Level**: {level}
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**Learned**: {summary}
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🎯 Next Steps:
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- Continue learning with /ai-agent-code-learning learn {next_level}
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- Auto-generate settings with /ai-agent-code-learning setup
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- Check latest trends with /ai-agent-code-learning upgrade
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```
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## Current Settings Analysis
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Files to analyze:
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- agents.md (root)
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- .ai-agent/settings.local.json
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- .ai-agent/commands/
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- .ai-agent/agents/
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- .ai-agent/skills/
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- .mcp.json
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## Skills 2.0 Hot Reload (CC 2.1.0+)
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CC 2.1.0+ supports hot reload for skill changes. No session restart needed.
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### Hot Reload Scope
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- SKILL.md body changes: Instant reflection
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- Frontmatter field changes: Instant reflection
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- New skill additions: Instant reflection
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### Development Workflow
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1. Edit SKILL.md content and save
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2. Next slash invoke reflects changes immediately
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3. Use `/reload-plugins` for forced refresh
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### Tips
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- Test with `/eval run [skill-name]` after changes
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- Use `classification` frontmatter to categorize skills
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- Monitor with `/loop 5m /pdca status` for ongoing work
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