using-superpowers
Use when starting any conversation - establishes how to find and use skills, requiring Skill tool invocation before ANY response including clarifying questions
Use when starting any conversation - establishes how to find and use skills, requiring Skill tool invocation before ANY response including clarifying questions
Capture task outcomes, score performance, and derive rules as token priors for continual learning without model weight changes. Use for post-task feedback, experience capture, pattern extraction, and learning from mistakes. Achieves continual learning for $18 per 100 samples vs $10k fine-tune cost. Triggers on "learn from experience", "capture patterns", "post-task analysis", "continual learning", "experience extraction".
ML Engineer role: LLM APIs (OpenAI, Claude, Gemini), embeddings, RAG pipelines, fine-tuning, LangChain, LlamaIndex, vector databases (Pinecone, Chroma, Weaviate), prompt engineering, model evaluation, cost optimization, Agentic RAG, AI Agents, MCP, LLM observability. 30 methodologies.
Use when starting any conversation - establishes mandatory workflows for finding and using skills, including using Skill tool before announcing usage
Comprehensive guide to implementing permission systems for AI agent tools including RBAC, approval workflows, and security policies
How to write/update .claude/skills and capture durable OpenEvent learnings via retrospectives.
Build complete agent prompts deterministically via Python script. Use BEFORE spawning any BAZINGA agent (Developer, QA, Tech Lead, PM, etc.).
Apply Anthropic's 4D Framework for AI delegation: Delegation (task selection), Description (instructions), Discernment (verification), and Diligence (iteration).
Implements a proactive confirmation and continuous loop mechanism for all agent interactions. Use this skill when engaging in any task (consultation, development, debugging) to ensure step-by-step user approval and maintain continuous workflow until explicit termination.
Add a React + WebSocket UI on top of Claude Agent SDK agents with tool approval and SQLite persistence
Patterns for building AI applications with Vercel AI SDK including streaming LLM responses, tool calling, structured outputs, and multi-provider support. Use when integrating LLMs into applications.
Master controller for complete autonomous operation. Use when starting full autonomous projects, managing end-to-end workflow, controlling autonomous lifecycle, or running complete implementations.
Standards for writing CLAUDE.md and modular rules files (.claude/rules/) that maximize LLM execution efficiency. Use when creating or editing project instructions, defining conventions, or optimizing agent behavior for determinism and token efficiency. Focuses on eliminating ambiguity and producing consistent agent execution.
Multi-model consensus scoring for content ideas. Scores the same idea with Claude, GPT-4o, Gemini, and Grok in parallel, then aggregates for a balanced verdict. Reduces single-model bias and improves viral predictions.
List all available skills configured in AGENTS.md. Scan and display skills with their names, descriptions, and trigger commands. Triggers when user mentions "列出技能", "list skills", "可用技能", "show skills", "技能列表", or uses command /skill-list.
Guides creating Claude Code agents, subagents, and skills. Use when building new agents, optimizing existing ones, or structuring skills.
Cost optimization strategies for production AI pipelines in Clojure+Vertex AI. Covers multi-model routing (70% Gemini/20% Haiku/10% Sonnet), token optimization (prompt engineering, output constraints), aggressive caching (58% cost reduction), batch processing, and real-time monitoring. Includes production metrics showing $0.391 to $0.162 per pipeline (-58%). Use when optimizing production costs, implementing multi-model strategies, designing budget controls, or scaling to high volume.