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Found 44 Skills
Expert prompt optimization for LLMs and AI systems. Use when building AI features, improving agent performance, crafting system prompts, or optimizing LLM interactions. Masters prompt patterns and techniques.
Make AI solve hard problems that need planning and multi-step thinking. Use when your AI fails on complex questions, needs to break down problems, requires multi-step logic, needs to plan before acting, gives wrong answers on math or analysis tasks, or when a simple prompt isn't enough for the reasoning required. Covers ChainOfThought, ProgramOfThought, MultiChainComparison, and Self-Discovery reasoning patterns in DSPy.
Expert in designing effective prompts for LLM-powered applications. Masters prompt structure, context management, output formatting, and prompt evaluation. Use when: prompt engineering, system prompt, few-shot, chain of thought, prompt design.
Create, improve, or optimize prompts using best practices
Analyzes and transforms prompts using 7 research-backed frameworks (CO-STAR, RISEN, RISE-IE, RISE-IX, TIDD-EC, RTF, Chain of Thought, Chain of Density). Provides framework recommendations, asks targeted questions, and structures prompts for maximum effectiveness. Use when users need expert prompt engineering guidance.
Meta-skill for improving and optimizing prompts using Anthropic's prompt engineering best practices. Provides the 4-step improvement workflow (example identification, initial draft, chain of thought refinement, example enhancement), keyword registries for documentation lookup, and decision trees for improvement strategies. Use when improving prompts, optimizing for accuracy, adding chain of thought reasoning, structuring with XML tags, enhancing examples, or iterating on prompt quality. Delegates to docs-management skill for official prompt engineering documentation.
Use when "DSPy", "declarative prompting", "automatic prompt optimization", "Stanford NLP", or asking about "optimizing prompts", "prompt compilation", "modular LLM programming", "chain of thought", "few-shot learning"
Use when facing complex reasoning tasks - multi-step math, logic puzzles, decisions with tradeoffs, problems where direct answers fail, or when you need to show your work. Triggers on arithmetic errors, shallow analysis, or "I'm not sure" hedging.