[G1-Sync] Manual knowledge update
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Antigravity Agent
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---
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id: ai-anthropic-skills-patterns
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title: Anthropic Skills — modular agent capability
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category: Coding
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status: draft
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source_trust_level: B
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verification_status: conceptual
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created_at: 2026-05-09
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updated_at: 2026-05-09
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tags: [ai, anthropic, agents, vibe-coding]
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tech_stack: { language: "TS / Markdown", applicable_to: ["AI"] }
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applied_in: []
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aliases: [Anthropic Skills, agent skills, modular capability, prompt module, sub-agent, Claude skills]
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---
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# Anthropic Skills
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> Claude / agent 의 modular capability. **Markdown skill files + structured tools**. Re-usable + composable.
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## 📖 핵심 개념
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- 매 skill = self-contained doc + tool.
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- Loaded on-demand.
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- Modular (mix and match).
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- LLM 가 자체 invoke.
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## 💻 코드 패턴
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### Skill structure
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```
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skills/
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├── pdf-processor/
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│ ├── SKILL.md # 의 description + 사용법
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│ ├── helpers.ts # tool implementation
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│ └── examples/ # sample input
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├── data-analyst/
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│ └── SKILL.md
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└── code-reviewer/
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└── SKILL.md
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```
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### SKILL.md
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```markdown
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---
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name: PDF Processor
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description: Extract text and structure from PDF files
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---
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# PDF Processor
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When user asks about PDF processing:
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1. Use `extract_text(path)` to get raw text.
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2. Use `extract_tables(path)` for tables.
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3. Use `summarize(text)` for long docs.
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## Tool examples
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To extract: extract_text('/path/to/doc.pdf')
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To find tables: extract_tables(path, page=2)
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```
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→ LLM 가 SKILL.md 읽음 + 자체 use.
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### Discovery
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```
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Agent loop:
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1. User query.
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2. List available skills.
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3. LLM 가 relevant skill 선택.
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4. Skill description + tools 가 context 에.
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5. Use.
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```
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→ Lazy load (token 절약).
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### Skill 의 ingredient
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```
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- Description: 언제 use.
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- Examples: input → output.
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- Tools: 호출 가능.
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- Constraints: 안 do.
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- Workflow: step-by-step.
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```
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### Multi-skill
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```
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User: "Analyze this PDF and write a summary email".
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LLM 가:
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1. PDF Processor skill (extract).
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2. Data Analyst skill (analyze).
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3. Email Writer skill (write).
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→ Compose multiple.
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```
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### vs MCP server
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```
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MCP: standardized protocol (Anthropic).
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Skills: skill 의 unit (markdown + tool).
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→ Skills 가 MCP 위에 사용 가능.
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```
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→ [[AI_MCP_Server_Building]].
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### Code skill 예
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```markdown
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---
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name: Code Reviewer
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description: Review code for bugs, style, performance
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---
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# Code Reviewer
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For each file:
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1. Check for common bugs (null check, off-by-one).
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2. Verify style (lint, format).
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3. Suggest performance improvement.
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4. Output JSON: { issues: [...], suggestions: [...] }
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## Don't
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- Don't rewrite entire file.
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- Don't suggest opinion-based changes (subjective style).
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```
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### Versioning
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```
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skills/
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├── pdf-processor/
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│ └── v2/
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│ ├── SKILL.md
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│ └── ...
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```
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→ Multiple version, A/B test.
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### Sharing
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```
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Internal company skills:
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- Marketing Copywriter.
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- Customer Support Triage.
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- Code Reviewer.
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- Data Analyst.
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→ Library 가 매 team 가 share.
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```
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### Public skill marketplace
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```
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2026 의 가능성:
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- "Skills marketplace" (Hugging Face 식).
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- 매 skill 가 review / rating.
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- 매 use case 의 best skill.
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```
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### Custom Claude
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```
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Claude Pro / Team:
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- Custom instruction (system prompt).
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- Memory (persistent).
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- Skills (loaded on demand).
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→ Specialized assistant.
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```
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### Anthropic Computer Use + Skills
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```
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Skill: 'Search the web for X and summarize'.
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- Computer Use tool.
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- Browser navigation.
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- Reading + summary.
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→ Modular agent.
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```
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### Skill 작성 가이드
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```
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1. Description 가 specific (애매 X).
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2. Examples (input → expected output).
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3. Constraints (안 do).
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4. Tool 가 명확.
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5. Output format (JSON 등).
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6. Eval (test cases).
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```
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### Iterative improve
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```
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1. Initial skill.
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2. Test cases.
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3. Fail case 분석.
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4. Skill update.
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5. Re-test.
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→ "Skill engineering" 의 process.
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```
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### vs prompt template
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```
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Prompt template: 1 prompt + variable.
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Skill: doc + tool + workflow.
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→ Skill 가 더 큰 unit.
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```
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### Production
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```
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- Code review bot (PR 매번).
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- Data analyst (Slack bot).
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- Customer support (1-line).
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- Personal assistant.
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→ 매 task = own skill.
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```
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### Skills + RAG
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```
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Skill 가 own knowledge base:
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- Doc 가 vector DB.
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- Skill 가 retrieve.
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- LLM 가 answer.
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→ Skill 가 expert in domain.
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```
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### Skills + sub-agent
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```
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Main agent: orchestrator.
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Skill A → sub-agent A.
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Skill B → sub-agent B.
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→ Multi-agent coordination.
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```
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→ [[AI_Multi_Agent_Coordination]].
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### Anthropic 의 official format
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```
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2026 시점 evolving.
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- Markdown + frontmatter.
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- Tool definitions.
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- Examples.
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- Constraints.
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```
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### Implementation 예 (TS)
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```ts
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class SkillManager {
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skills = new Map<string, Skill>();
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register(skill: Skill) {
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this.skills.set(skill.name, skill);
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}
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list(): SkillSummary[] {
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return [...this.skills.values()].map(s => ({
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name: s.name,
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description: s.description,
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}));
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}
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async load(name: string): Promise<string> {
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const skill = this.skills.get(name);
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return skill ? skill.fullContent : '';
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}
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}
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// LLM 의 system prompt 에 skill list.
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// LLM 가 'load_skill(name)' tool 호출.
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// Loaded skill content 가 context 에 추가.
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```
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### 함정
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```
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- Skill 너무 많음 = LLM 가 헷갈림.
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- Description 가 vague.
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- Tool overlap.
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- No eval.
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- Version 관리 X.
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- Cross-skill state share.
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```
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### Best practice
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```
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1. Specific name + description.
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2. Self-contained (depend X).
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3. Test case (10+).
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4. Versioning (skill v1, v2).
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5. Composable (다른 skill 와 stack).
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6. Idempotent (re-run OK).
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```
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## 🤔 의사결정 기준
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| 작업 | 추천 |
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| Specific task | Skill |
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| 일반 prompt | System prompt |
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| External integration | MCP |
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| Multi-step | Skill + sub-agent |
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| Re-usable across team | Skill library |
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## ❌ 안티패턴
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- **Skill 너무 많음**: LLM 헷갈림.
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- **Vague description**: 안 invoke.
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- **No example**: bad output.
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- **Tool overlap**: confusion.
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- **No eval**: silent regression.
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## 🤖 LLM 활용 힌트
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- Skills = modular capability.
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- Markdown + tool 가 unit.
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- MCP 위 가능.
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- Specific name + description 가 핵심.
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## 🔗 관련 문서
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- [[AI_Skills_Patterns]]
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- [[AI_MCP_Integration_Patterns]]
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- [[AI_Multi_Agent_Coordination]]
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