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Found 92 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.
INVOKE THIS SKILL when optimizing, improving, or debugging LLM prompts using production trace data, evaluations, and annotations. Covers extracting prompts from spans, gathering performance signal, and running a data-driven optimization loop using the ax CLI.
Improve and rewrite user prompts to reduce ambiguity and improve LLM output quality. Use when a user asks to optimize, refine, clarify, or rewrite a prompt for better results, or when the request is about prompt optimization or prompt rewriting.
Analyze and improve existing prompts for better performance
Active diagnostic tool for analyzing skill prompts to identify token waste, anti-patterns, trigger issues, and optimization opportunities. Use when reviewing skill prompts, debugging why skills aren't triggering, optimizing token usage, or preparing skills for publication. Provides specific, actionable suggestions with examples.
INVOKE THIS SKILL for Arize Prompt Hub and `ax prompts` workflows: author or import templates and save (Workflows A–B), label/promote (C), or list/get/edit/delete/duplicate (D). Use when the user mentions ax prompts, Prompt Hub, creating/editing/saving a prompt, `{variable}` placeholders, or production/staging labels. For improving prompt text using traces or eval scores, use arize-prompt-optimization. For running experiments, use arize-experiment.
Transforms vague UI ideas into polished, Stitch-optimized prompts. Enhances specificity, adds UI/UX keywords, injects design system context, and structures output for better generation results.
Reference for writing and editing excellent skills: Vocabulary and principles to make skills predictable.
Build complex AI systems with declarative programming, optimize prompts automatically, create modular RAG systems and agents with DSPy - Stanford NLP's framework for systematic LM programming
DSPy declarative framework for automatic prompt optimization treating prompts as code with systematic evaluation and compilers
Guide for experimenting with AI configurations. Helps you test different models, prompts, and parameters to find what works best through systematic experimentation.
Transforms vague prompts into optimized Claude Code prompts. Adds verification, specific context, constraints, and proper phasing. Invoke with /best-practices.