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Found 2,422 Skills
Decision guide for delegating to hui-style subagents. Tells the main thread WHEN to spawn `huicrew-investigator` (locate code), `huicrew-builder` (1-2 file edit), or `huicrew-reviewer` (diff review) instead of doing the work inline or using vanilla `Explore`. Subagent output is hui-compressed so the tool-result injected back into main context is ~60% smaller — main context lasts longer across long sessions. Trigger: "delegate to subagent", "use huicrew", "spawn investigator/builder/reviewer", "save context", "compressed agent output".
Use when the user wants to build, initialize, validate, optimize, or refactor a model-powered assistant, internal tool, automation, evaluator, or workflow from a business scenario or common problem statement, including project-structure refactors or starter skeletons that may separate model setup, prompt config, and orchestration, even if the request also mentions a UI, app shell, or local model service such as Ollama, and it is still unclear whether the solution should stay a single request, add supporting capabilities, or become orchestration. The user does not need to mention Agently explicitly.
Improve (or bootstrap) an AGENTS.md / CLAUDE.md file using `<important if>` conditional blocks so the agent actually attends to the right guidance at the right time. Use this skill whenever the user mentions AGENTS.md, CLAUDE.md, agent instructions, project rules for AI, "my claude config", onboarding docs for agents, or asks to tighten / shorten / audit / rewrite an existing one — even if they don't explicitly say the filename. Also use when the user complains that an agent keeps ignoring their project rules.
Implementation planning skill. Creates detailed technical plans through interactive research and iteration.
End-to-end feature owner with expertise across the entire stack. Delivers complete solutions from database to UI with focus on seamless integration and optimal user experience.
Letta framework for building stateful AI agents with long-term memory. Use for AI agent development, memory management, tool integration, and multi-agent systems.
Comprehensive guide and utilities for building AI agents using the Agent2Agent (A2A) Protocol. Use when implementing agent-to-agent communication, creating A2A servers/clients, or working with JSON-RPC based agent systems.
Use when designing futuristic agentic workflows, when wanting AI to proactively act on team communications, or when eliminating the bottleneck of formal specifications
Write AI-scannable technical documentation.
Use when you need a complete research workflow from initial literature search to polished, fact-checked document. Chains researcher -> synthesizer -> devils-advocate -> fact-checker -> editor automatically.
연구 논문(PDF/arXiv URL)을 분석하여 실행 가능한 코드로 변환합니다. 논문 복제, 알고리즘 구현, 연구 재현 요청 시 자동으로 활성화됩니다. "이 논문 구현해줘", "paper2code", "논문 코드로 변환" 등의 요청에 반응합니다.
Assistive AI와 Agentic AI의 차이, ReAct 루프, Tool Use, MCP 개념을 학습시키는 모듈.