performance-analysis
Comprehensive performance analysis, bottleneck detection, and optimization recommendations for Claude Flow swarms
Comprehensive performance analysis, bottleneck detection, and optimization recommendations for Claude Flow swarms
创建有效技能的指南。当用户想要创建新技能(或更新现有技能)以通过专业知识、工作流程或工具集成扩展 Claude 功能时,应使用此技能。
ANIMA as limit construction over condensed skill applications. Formalizes prediction markets as belief ANIMAs, structure dishes as condensation media, and impact as equivalence class change. Use for understanding agency at maximum entropy, compositional world modeling, or applying Scholze-Clausen condensed mathematics to AI.
Build AI agents with Google's Agent Development Kit (ADK) Python - an open-source toolkit for building, evaluating, and deploying AI agents. Features LlmAgent, workflow agents (sequential, parallel, loop), tool integration, multi-agent systems, and deployment to Vertex AI or Cloud Run.
Design, implement, and debug a custom ChatKit backend in Python that powers the ChatKit UI without Agent Builder, using the OpenAI Agents SDK (and optionally Gemini via an OpenAI-compatible endpoint). Use this Skill whenever the user wants to run ChatKit on their own backend, connect it to agents, or integrate ChatKit with a Python web framework (FastAPI, Django, etc.).
Implement agent evaluation and safety gates using MLflow 3.x. Use for creating LLM-as-Judge scorers, evaluation datasets, quality gates, tracing, and continuous evaluation. Triggers on "evaluate agent", "MLflow scorer", "LLM judge", "safety evaluation", "quality gate", "agent testing", "hallucination detection", or when implementing spec/010-agent-evaluation.md requirements.
Local LangChain AI documentation reference. Use when asked about LangChain, LangGraph, agents, chains, prompts, memory, tools, retrieval, RAG, vector stores, document loaders, or building LLM applications.
Branch the current Claude Code session into a new tmux session using --resume. Creates a parallel conversation that can diverge independently. Use when you want to explore an alternative approach without losing your current context.
Build AI-first applications with RAG pipelines, embeddings, vector databases, agentic workflows, and LLM integration. Master prompt engineering, function calling, streaming responses, and cost optimization for 2025+ AI development. Includes local LLM inference with Ollama for 93% CI cost reduction.
Determines appropriate agent routing based on task type, scale, and agent capabilities
Prompt engineering guidance for Gemini (Google) model. Use when crafting prompts for Gemini to leverage system instructions, multimodal capabilities, ultra-long context, and strong reasoning features.
Stream-JSON chaining for multi-agent pipelines, data transformation, and sequential workflows
Use when beginning a new conversation to work on an open-ended goal, loading context from previous iterations through iteration journals
Universal knowledge storage and retrieval patterns using memory graph
Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.
Senior MLOps Engineer with 8+ years ML systems experience. Use when integrating LLM APIs (Gemini, OpenAI, Groq), building AI pipelines, managing prompts, setting up model serving, implementing AI cost optimization, or building training data pipelines.
Project-agnostic guidance for continuing work across Claude sessions. Ensures context recovery, task resumption, and state reconciliation.
Multi-repository coordination, synchronization, and architecture management with AI swarm orchestration
Enhance talk notes with Blinkist-style summaries and timestamps. Use when asked to "enhance talk", "improve talk notes", "add timestamps", "blinkist-style talk summary", or "make talk notes better". Adds Core Message, Key Insights with timestamps, Talk Structure, Notable Quotes, Who Should Watch, and Action Items via transcript analysis.
Provides expert guidance for writing and optimizing prompts for large language models. Use this skill when: (1) user mentions "prompt", "prompting", or "prompt engineering", (2) user requests to write, create, improve, optimize, or review any prompt, (3) user is creating or updating AGENTS.md, CLAUDE.md, .claude/commands/*.md, or .claude/skills/*/SKILL.md files, (4) user is writing system prompts, custom instructions, or LLM agent configurations.
Deploy generated skills to system locations. Use after /generate-agent-skills to install skills.
Maintain Claude Code memory hygiene by auditing, organizing, updating, and optimizing memory files in `.claude/memory/`. Use when users request memory cleanup, organization, updates, or want to reduce context pollution. Handles stale content, redundancy, conflicts, and file organization issues.