Pier_v1/.hmcmkit/knowledge/skills/ai-agent-code-learning/SKILL.md

name, classification, classification-reason, deprecation-risk, description, argument-hint, agent, allowed-tools, user-invocable, imports, next-skill, pdca-phase, task-template, hooks

name	classification	classification-reason	deprecation-risk	description	argument-hint	agent	allowed-tools	user-invocable	imports	next-skill	pdca-phase	task-template	hooks
ai-agent-code-learning	capability	Highly likely to be subsumed by model's native capabilities	high	Antigravity Platform learning — configure and optimize Antigravity Platform settings, tips, and workflows. Triggers: learn, setup, ai-agent code, optimize, 학습, 설정, 최적화.	[learn|setup|upgrade] [level]	ai-agent-code-guide	Read Write Edit Glob Grep Bash AskUserQuestion	true	${PLUGIN_ROOT}/templates/shared/naming-conventions.md	null	null	[Learn] Antigravity Platform {level}	Stop type command timeout command node ${HMCMKIT_PLUGIN_ROOT}/scripts/learning-stop.js 10000

Antigravity Platform Learning Skill

Master Antigravity Platform configuration and optimization

Actions

Action	Description	Example
learn	Start learning guide	/ai-agent-code-learning learn 1
setup	Auto-generate settings	/ai-agent-code-learning setup
upgrade	Latest features guide	/ai-agent-code-learning upgrade

learn [level]

Learning content by level:

• Level 1: Basics - Writing agents.md, Using Plan Mode
• Level 2: Automation - Commands, Hooks, Permission management
• Level 3: Specialization - Agents, Skills, MCP integration
• Level 4: Team Optimization - GitHub Action, Team rule standardization
• Level 5: PDCA Methodology - hmcmkit methodology learning

setup

Auto-generate appropriate settings after analyzing current project:

1. Analyze/generate agents.md
2. Check .ai-agent/ folder structure
3. Suggest required configuration files

upgrade

Guide to latest Antigravity Platform features and best practices.

Learning Levels

Level 1: Basics (15 min)

## What is agents.md?

A shared knowledge repository for the team. When AI Agent makes mistakes,
add rules to prevent the same mistakes from recurring.

## Example

# Development Workflow

## Package Management
- **Always use `pnpm`** (`npm`, `yarn` prohibited)

## Coding Conventions
- Prefer `type`, avoid `interface`
- **Never use `enum`** → Use string literal unions

## Prohibited
- ❌ No console.log (use logger)
- ❌ No any type

Level 2: Automation (30 min)

## What are Slash Commands?

Execute repetitive daily tasks with `/command-name`.

## Command Location

.ai-agent/commands/{command-name}.md

## PostToolUse Hook

Auto-formatting after code modification:

// .ai-agent/settings.local.json
{
  "hooks": {
    "PostToolUse": [{
      "matcher": "Write|Edit",
      "hooks": [{
        "type": "command",
        "command": "pnpm format || true"
      }]
    }]
  }
}

Level 3: Specialization (45 min)

## What are Sub-agents?

AI agents specialized for specific tasks.

## What are Skills?

Domain-specific expert context. AI Agent auto-references when working on related tasks.

## MCP Integration

Connect external tools (Slack, GitHub, Jira, etc.) via .mcp.json.

Level 4: Team Optimization (1 hour)

## PR Automation with GitHub Action

Mention @ai-agent in PR comments to auto-update documentation.

## Team Rule Standardization

1. Manage agents.md with Git
2. Add rules during PR review
3. Gradually accumulate team knowledge

## Agent Teams (v1.5.1)

Parallel PDCA execution with multiple AI agents working simultaneously.

Requirements:
  HMCMKIT_EXPERIMENTAL_AGENT_TEAMS=1

Usage:
  /pdca team {feature}     Start team mode
  /pdca team status        Check teammate progress
  /pdca team cleanup       End team session

Team composition by level:
  Dynamic:    2 teammates (developer, qa)
  Enterprise: 4 teammates (architect, developer, qa, reviewer)

Level 5: PDCA Methodology

## What is PDCA?

Document-driven development methodology.

Plan → Design → Do → Check → Act

## Folder Structure

docs/
├── 01-plan/      # Planning
├── 02-design/    # Design
├── 03-analysis/  # Analysis
└── 04-report/    # Reports

## Learn More

Use /pdca skill to learn PDCA methodology.

Level 6: Advanced Features (v1.5.1)

## Output Styles

Customize how AI Agent responds based on your project level.

Available styles:
  hmcmkit-learning     Best for beginners (learning points, TODO markers)
  hmcmkit-pdca-guide   Best for PDCA workflows (status badges, checklists)
  hmcmkit-enterprise   Best for architects (tradeoff analysis, cost impact)

Usage:
  /output-style              Select interactively
  /output-style hmcmkit-learning  Apply directly

Auto-recommendation:
  Starter → hmcmkit-learning
  Dynamic → hmcmkit-pdca-guide
  Enterprise → hmcmkit-enterprise

## Agent Memory

All hmcmkit agents automatically remember context across sessions.
No configuration needed.

Memory scopes:
  project   9 agents remember per-project context (.ai-agent/agent-memory/)
  user      2 agents remember cross-project learning (~/.ai-agent/agent-memory/)

Agents with user-scope memory:
  starter-guide     Remembers your learning progress across projects
  pipeline-guide    Remembers your pipeline preferences globally

## Agent Teams

Parallel PDCA execution for Dynamic and Enterprise projects.
See Level 4 for details.

Output Format

📚 Antigravity Platform Learning Complete!

**Current Level**: {level}
**Learned**: {summary}

🎯 Next Steps:
- Continue learning with /ai-agent-code-learning learn {next_level}
- Auto-generate settings with /ai-agent-code-learning setup
- Check latest trends with /ai-agent-code-learning upgrade

Current Settings Analysis

Files to analyze:

• agents.md (root)
• .ai-agent/settings.local.json
• .ai-agent/commands/
• .ai-agent/agents/
• .ai-agent/skills/
• .mcp.json

Skills 2.0 Hot Reload (CC 2.1.0+)

CC 2.1.0+ supports hot reload for skill changes. No session restart needed.

Hot Reload Scope

• SKILL.md body changes: Instant reflection
• Frontmatter field changes: Instant reflection
• New skill additions: Instant reflection

Development Workflow

1. Edit SKILL.md content and save
2. Next slash invoke reflects changes immediately
3. Use /reload-plugins for forced refresh

Tips

• Test with /eval run [skill-name] after changes
• Use classification frontmatter to categorize skills
• Monitor with /loop 5m /pdca status for ongoing work