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: skill-create
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classification: workflow
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classification-reason: "Interactive skill creation workflow with user prompts, file generation, and eval scaffolding. Multi-step process requiring user decisions."
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deprecation-risk: none
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description: |
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Interactive skill creation workflow for project-local skills.
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Triggers: skill-create, create skill, 스킬 생성, 스킬 만들기.
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argument-hint: "/skill-create [name] | /skill-create from-btw {btw-id}"
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user-invocable: true
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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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---
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# skill-create - Interactive Skill Creation Workflow
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프로젝트 컨텍스트를 분석하여 project-local 스킬을 생성하는 워크플로우.
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## Workflow Steps
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### Step 1: Gather Information
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If `/skill-create` (no args):
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1. Ask user for skill name (kebab-case)
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2. Ask user for skill purpose/description
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3. Ask user for classification: workflow or capability
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If `/skill-create {name}`:
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1. Use provided name
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2. Analyze project context to infer purpose
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3. Ask user to confirm/modify
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If `/skill-create from-btw {btw-id}`:
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1. Read btw suggestion from `.hmcmkit/btw-suggestions.json`
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2. Use suggestion text as seed for skill description
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3. Use suggestion context for project context
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### Step 2: Analyze Project Context
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1. Read `agents.md` for project conventions
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2. Scan project structure:
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- Identify tech stack (Java/Spring, Next.js, Python, etc.)
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- Find relevant patterns in source code
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- Check existing project-local skills for overlap
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3. Determine appropriate:
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- Allowed tools for the skill
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- Trigger keywords (Korean + English)
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- Related files/patterns to reference
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### Step 3: Generate SKILL.md
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Create `.ai-agent/skills/project/{name}/SKILL.md` with:
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```yaml
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---
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name: {name}
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classification: {workflow|capability}
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classification-reason: "{reason based on analysis}"
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deprecation-risk: none
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description: |
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{Generated description}
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Triggers: {trigger commands}
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Keywords: {keywords in Korean and English}
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argument-hint: "{usage hint}"
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user-invocable: true
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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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---
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```
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Body content includes:
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- Purpose and when to use
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- Step-by-step instructions for workflow skills
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- Pattern reference for capability skills
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- Code examples from actual project files
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- Integration points with other skills
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### Step 4: Generate Eval Files (optional)
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Create `evals/{name}/` directory with:
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```
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evals/{name}/
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eval.yaml - Test configuration
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prompt.txt - Test prompt
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expected.txt - Expected behavior description
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```
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**eval.yaml format:**
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```yaml
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name: "{name} skill eval"
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skill: ".ai-agent/skills/project/{name}/SKILL.md"
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tests:
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- name: "basic trigger"
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prompt: "{trigger command}"
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expected:
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- "{expected behavior 1}"
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- "{expected behavior 2}"
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- name: "edge case"
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prompt: "{edge case prompt}"
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expected:
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- "{expected behavior}"
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```
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### Step 5: Confirm and Report
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1. Show generated file paths
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2. Show skill summary (name, classification, triggers)
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3. Suggest: "Test with `/{name}` or review at `.ai-agent/skills/project/{name}/SKILL.md`"
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## Project-Local Skill Location
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All generated skills go to: `.ai-agent/skills/project/{name}/SKILL.md`
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This location:
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- Is git-trackable for team sharing
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- Takes precedence over hmcmkit core skills (2-layer architecture)
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- Is auto-loaded by Antigravity Platform's skill discovery
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## Naming Conventions
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| Pattern | Example | Use When |
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|---------|---------|----------|
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| `{project}-{domain}` | `hunikflow-model` | Domain-specific capability |
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| `{action}-{target}` | `validate-entity` | Action-oriented workflow |
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| `{tool}-{purpose}` | `proxy-guide` | Tool/pattern guide |
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## From-BTW Integration
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When creating from a /btw suggestion:
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1. Read the suggestion's `context.file` to understand what code was being worked on
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2. Read the suggestion's `context.feature` to understand the PDCA feature context
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3. Use suggestion text as the primary description seed
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4. After creation, update the btw suggestion's `promotedTo` field with the new skill name
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