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Found 1,749 Skills
LangGraph framework for building stateful, multi-agent AI applications with cyclical workflows, human-in-the-loop patterns, and persistent checkpointing.
The foundational context engineering skill — start here when exploring the discipline. This skill should be used when the user asks to "understand context", "explain context windows", "design agent architecture", "debug context issues", "optimize context usage", or discusses context components, attention mechanics, progressive disclosure, or context budgeting. Also activates when the user mentions "context engineering" or "context-engineering" for foundational understanding of AI agent context systems.
Build MCP (Model Context Protocol) servers including tool definition, schema design, authentication, error handling, and Claude Code integration. Use this skill when the user needs to create an MCP server, expose APIs or databases to AI agents, design tool schemas, or integrate with Claude Code — even if they say 'build an MCP server', 'connect Claude to our database', 'expose our API to AI', or 'create a tool for Claude Code'.
Comprehensive security auditor for AI agent skills, prompts, and instructions. Checks for typosquatting, dangerous permissions, prompt injection, supply chain risks, and data exfiltration patterns — before you use any agent or skill.
Decomposes a spec or architecture into buildable tasks with acceptance criteria, dependencies, and implementation order for AI agents or engineers. Produces `.agents/tasks.md`. Not for clarifying unclear requirements (use discover) or designing architecture (use system-architecture). For code quality checks after building, see review-chain. For packaging and PRs, see ship.
Use when executing implementation plans. Dispatches independent subagents for individual tasks with code review checkpoints between iterations for rapid, controlled development.
Use when dealing with 2 or more independent tasks that have no shared state or sequential dependencies
개발 DB에 SQL을 직접 실행하여 데이터를 조회하는 스킬. "DB 확인", "데이터 조회", "테이블 조회", "SQL 실행", "데이터 검증" 키워드로 트리거. AI 에이전트가 백엔드/프론트엔드 개발 중 개발 DB 데이터를 즉시 확인할 때 사용.
BrandJet AI platform help — multi-channel outreach sequences, unified inbox, brand monitoring, AI visibility tracking, lead discovery, social listening, email warmup, Artemis AI agent, and integrations. Use when outreach sequences aren't getting replies, brand mentions going unnoticed, multi-channel sequences feel disjointed, unified inbox is overwhelming, or AI visibility scores are dropping. Do NOT use for designing cadence strategy (use /sales-cadence), cross-platform deliverability (use /sales-deliverability), social listening strategy (use /sales-social-listening), or enriching contacts (use /sales-enrich).
Use when acting as Grunk - reads specs from beads, plans, implements, commits, tags pr-ready. Merged TL+Engineer. Works in loop mode or interactive mode.
Design multi-agent architectures for complex tasks. Use when single-agent context limits are exceeded, when tasks decompose naturally into subtasks, or when specializing agents improves quality.
Builds robust, tool-specific prompts from user intent using a structured extraction and routing engine. Use when the user asks for prompt creation, prompt repair, prompt decomposition, or adapting prompts across Claude, GPT, reasoning models, Gemini, coding IDEs, autonomous agents, and image tools.