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Found 9,571 Skills
Day 1 테스트 스킬. "/day1-test-skill" 입력 시 실행된다. Skill이 어떻게 동작하는지 직접 체험하는 용도.
Automatically loads domain README.md context when you start working in a registered library or module. Reads architecture, public API, and patterns before touching domain-specific code. Activate when the task involves a specific library, domain path, or module the user references.
Design and apply AI personas for specialized contexts — project-specific voices, domain expert modes, or custom interaction styles. Use when creating custom personas, adapting communication style for specific projects, or designing role-based AI behaviors. Triggers on "페르소나", "persona", "역할 설정", "톤 설정", "voice", "캐릭터", "role definition".
Archive completed plans from plans/active/ to plans/completed/, supplement verification results and completion time. Suitable for calling after implementation is finished.
Provides information about how to create, structure, install, and audit Agent Skills, Plugins, Antigravity Workflows, and Sub-agents. Trigger this when specifications, rules, or best practices for the ecosystem are required.
Create new skills, modify and improve existing skills, and measure skill performance. Enhanced version with quick commands. Use when users want to create a skill from scratch, update or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy. Triggers on phrases like "make a skill", "create a new skill", "build a skill for", "improve this skill", "optimize my skill", "test my skill", "turn this into a skill", "skill description optimization", or "help me create a skill".
Interactive debugging via DAP-MCP for multiple languages with natural language commands
어떤 주제/과제든 받아서 스스로 팀을 구성하고 조사·분석·검토·결과도출까지 처리하는 범용 에이전트 팀 오케스트레이터. "팀으로 분석해줘", "에이전트 팀으로 조사해줘", "다각도로 검토해줘", "심층 분석 부탁해", "여러 관점으로 봐줘", "think-team", "think team" 키워드로 트리거. 단순 질문이 아닌 복합적 판단, 조사, 전략 결정이 필요한 모든 상황에서 사용.
Agent skill to convert any arxiv paper into a citation-anchored, working Python implementation with ambiguity auditing
Switch model profile for GSD agents (quality balanced budget)
Run a decision through 5 AI advisors with different thinking styles, anonymous peer review, and chairman synthesis. For genuine decisions with stakes and tradeoffs — not simple questions. Based on Karpathy's LLM Council.
Plan-then-execute implementation against SPEC.md. Native single-thread loop, no sub-agents. On test or build failure, auto-invokes the backprop skill before retrying — a failed verification always considers whether a new §V invariant would prevent recurrence. Triggers when the user asks to build, implement, execute the spec, or tackle a specific §T task (`build §T.3`, `build --next`, `implement next task`, `run the build`). Expects SPEC.md to exist; if not, defers to the spec skill.