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Found 7,249 Skills
OpenContext를 활용한 AI 에이전트 영구 메모리 및 컨텍스트 관리. 세션/레포/날짜 간 컨텍스트 유지, 결론 저장, 문서 검색 워크플로우 제공.
AI 코딩 에이전트를 시각적 Kanban 보드에서 관리. To Do→In Progress→Review→Done 흐름으로 병렬 에이전트 실행, git worktree 자동 격리, GitHub PR 자동 생성.
Interactive plan and diff review for AI coding agents. Visual browser UI for annotating agent plans — approve or request changes with structured feedback. Supports code review, image annotation, and auto-save to Obsidian/Bear Notes.
oh-my-claudecode — Teams-first multi-agent orchestration layer for Claude Code. 32 specialized agents, smart model routing, persistent execution loops, and real-time HUD visibility. Zero learning curve.
Ultimate multi-agent framework for Google Antigravity. Orchestrates specialized domain agents (PM, Frontend, Backend, Mobile, QA, Debug) via Serena Memory.
Build production-ready AI workflows using Firebase Genkit. Use when creating flows, tool-calling agents, RAG pipelines, multi-agent systems, or deploying AI to Firebase/Cloud Run. Supports TypeScript, Go, and Python with Gemini, OpenAI, Anthropic, Ollama, and Vertex AI plugins.
JEO — 통합 AI 에이전트 오케스트레이션 스킬. ralph+plannotator로 계획 수립, team/bmad로 실행, agent-browser로 브라우저 동작 검증, 작업 완료 후 worktree 자동 정리. Claude, Codex, Gemini CLI, OpenCode 모두 지원. 설치: ralph, omc, omx, ohmg, bmad, plannotator, agent-browser.
Create and scaffold new skills with proper frontmatter, directory structure, and validation. Use when the user asks to build a new capability, integrate a new API, or extend the system with a repeatable workflow.
Use this skill for requests related to web research; it provides a structured approach to conducting comprehensive web research
股票投资调研执行引擎，执行8阶段投资尽调流程。接收stock-question-refiner生成的结构化调研指令，部署多智能体并行研究，生成带引用的投资尽调报告。覆盖：公司事实底座、行业周期、业务拆解、财务质量、股权治理、市场分歧、估值护城河、综合报告。当用户需要进行股票投资研究、基本面分析、投资尽调时使用此技能。
Conduct context-driven code reviews focusing on quality, testability, and maintainability. Use when reviewing code, providing feedback, or establishing review practices.
General-purpose deep research with multi-source synthesis and confidence-scored findings. Auto-classifies complexity from quick lookup to exhaustive investigation. Cross-validates across independent sources with anti-hallucination verification, contradiction detection, and bias auditing. Produces synthesis products with evidence chains and provenance. Resumable journal sessions. Use when investigating technical topics, academic questions, market analysis, competitive intelligence, architecture decisions, technology evaluation, fact-checking, literature review, or trend analysis. NOT for code review (use honest-review), strategic decisions (use wargame), multi-perspective debate (use host-panel), or simple factual Q&A answerable in one search.