phase2-5-autonomous
Phase 2-5 autonomous execution guidance - Activate when Claude needs to decide technical choices during implementation/testing/review/release phases
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Phase 2-5 autonomous execution guidance - Activate when Claude needs to decide technical choices during implementation/testing/review/release phases
A simple demonstration skill that greets users and provides helpful information
Comprehensive overview of Claude Code features, architecture, and capabilities. Learn about hooks, skills, commands, permissions, and best practices for configuring Claude Code as your AI development assistant....
This skill should be used when the user asks to "move candidate through pipeline", "schedule an interview", "manage applications", "track hiring progress", "source candidates", "advance to next stage", or menti...
効果的なスキルを作成するためのガイド。ユーザーがClaudeの機能を専門知識、ワークフロー、ツール統合で拡張する新しいスキルを作成(または既存のスキルを更新)したい場合に使用すべきスキルです。
ChromaDB vector database client for storing and retrieving text embeddings with hybrid search (dense + sparse). Use for RAG operations, contextual retrieval, and similarity search in clinical notes.
Use when creating AGENTS.md files, writing SKILL.md files, or improving agentic coding developer experience. Triggers include create AGENTS.md, write SKILL.md, agent configuration, agentic DX, AI assistant cont...
standardized protocols for agent collaboration. Use this to implement the Agent2Agent (A2A) protocol and Agent Cards to transform isolated agents into a collaborative ecosystem.
extended MCP features beyond tools, including Resources (data access), Prompts (templates), and Sampling (server-initiated model calls). Use this to implement rich, two-way agent interactions.
Create, modify, and manage Claude Code subagents with specialized expertise. Use when you need to "work with agents", "create an agent", "modify an agent", "set up a specialist", "I need an agent for [task]", o...
core cognitive architectures for Gemini agents. Use this to implement reasoning loops (ReAct, Chain-of-Thought) and understand the orchestration layer.
基于 500+ AI Agents Projects 仓库和 2026 年趋势,推荐各行业最有潜力的 AI Agent 项目。当用户需要了解特定行业的 AI Agent 应用、选择适合的 agent 项目或寻找行业最佳实践时使用此技能。涵盖金融、医疗、教育、零售、法律...
LangChain skill for building LLM orchestration, agents, RAG pipelines, tools, memory, callbacks, and deployment.
Persistent memory for the skills project - auto-generates Claude Code skills from conversation transcripts
Reference for creating Claude-executable plans (PLAN.md)
Bootstraps a repository with project skills from the trusted internal skillregistry. Detect stack, install baseline + language skills into .codex/skills and .claude/skills, generate project-specific overlay ski...
Use this skill for rigorous theoretical derivation with supercollider mode (G1-G7 simultaneous), diffusion reasoning, and synthesis engine. Applies enhanced Dokkado Protocol with generator hooks, meta-pattern r...
Analyze Claude Code session transcripts (JSONL files) for context window content, token usage, and decision analysis. NOT for application logs (use analyze-logs instead). Use when debugging Claude behavior, inv...
LTM (Long Term Memory) command reference. Use when saving memories, searching memories, or managing the memory system. Provides syntax for remember, recall, forget, memories, and other LTM commands.
Search code semantically using AG4ONE Serena integration
Interview user in-depth about an epic, task, or spec file to extract complete implementation details. Use when user wants to flesh out a spec, refine requirements, or clarify a feature before building. Triggers...
managing live agents through the Observe-Act-Evolve loop. Use this to maintain performance, manage unpredictable costs, and strategically improve agents based on production data.
Search and save conversation memories using semantic search. Use this skill when the user mentions past conversations, asks "do you remember", or when historical context from previous sessions would help solve...
methodologies for assessing agent capabilities, tool-use trajectories, and final response quality. Use this to implement automated testing and human-in-the-loop validation for agents.
Le classement utilise les étoiles du dépôt GitHub indexé. Plusieurs skills peuvent partager ce nombre : ce n’est ni un nombre d’installations ni une note de qualité.