Total 42,890 skills, AI & Machine Learning has 6869 skills
Showing 12 of 6869 skills
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.
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.
Ultimate multi-agent framework for Google Antigravity. Orchestrates specialized domain agents (PM, Frontend, Backend, Mobile, QA, Debug) via Serena Memory.
Integrate Firebase AI Logic (Gemini in Firebase) for intelligent app features. Use when adding AI capabilities to Firebase apps, implementing generative AI features, or setting up Firebase AI SDK. Handles Firebase AI SDK setup, prompt engineering, and AI-powered features.
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.
Generate and save images using Pollinations.ai's free, open-source API. No signup required. Supports URL-based generation, custom parameters (width, height, model, seed), and automatic file saving. Perfect for quick prototypes, marketing assets, and creative workflows.
AI 에이전트 실전 워크플로우와 생산성 기법. 명령어, 단축키, Git 통합, MCP 활용, 세션 관리 등 일상 개발 작업의 최적화 패턴 제공.
AI 에이전트와 협업하는 에이전틱 개발의 범용 원칙. 분해정복, 컨텍스트 관리, 추상화 수준 선택, 자동화 철학을 정의. 모든 AI 코딩 도구에 적용 가능.
AI 에이전트 설정 정책 및 보안 가이드. 프로젝트 설명 파일 작성법, Hooks/Skills/Plugins 설정, 보안 정책, 팀 공유 워크플로우 정의.
Design and implement comprehensive evaluation systems for AI agents. Use when building evals for coding agents, conversational agents, research agents, or computer-use agents. Covers grader types, benchmarks, 8-step roadmap, and production integration.
Multi-agent orchestration layer for OpenAI Codex CLI. Provides 30 specialized agents, 40+ workflow skills, team orchestration in tmux, persistent MCP servers, and staged pipeline execution.
README-first AI repo reproduction orchestrator. Use when the user wants an end-to-end minimal trustworthy reproduction flow that reads the repo, selects the smallest documented inference or evaluation target, coordinates the intake, setup, execution, and optional paper-gap sub-skills, enforces conservative patch rules, and writes the standardized `repro_outputs/` bundle. Do not use for paper summary, generic environment setup, isolated repo scanning, standalone command execution, or broad research assistance outside repository-grounded reproduction.