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Found 88 Skills
Use when optimizing CLAUDE.md, AGENTS.md, custom commands, or skill files for Claude 4.5 models - applies documented Anthropic best practices systematically instead of inventing improvements
Analyze and optimize user prompts for clarity, specificity, and completeness using interactive questionnaires or direct analysis. Use this skill when user requests are vague, ambiguous, incomplete, or lack necessary details. Supports two modes - Interactive Mode (uses AskUserQuestion tool for guided clarification) and Direct Analysis Mode (provides optimization suggestions). Triggers on prompts containing vague language like "something", "thing", "stuff", "it", or when requests lack context, technical specifications, success criteria, or examples. When user requests interactive/questionnaire mode, use AskUserQuestion to guide them through structured questions. Helps transform unclear requests into well-structured, actionable prompts.
Analyze and optimize system prompts using a structured prompting guidelines framework — AI-powered analysis and rewriting. Use when a prompt needs improvement, experiment results show quality gaps, or you want a structured review of an existing system prompt. Do NOT use when production traces show failures (use analyze-trace-failures first to identify patterns). Do NOT use to build evaluators (use build-evaluator).
Transform user requests into detailed, precise prompts for AI models. Use when users say "promptify", "promptify this", or explicitly request prompt engineering or improvement of their request for better AI responses.
Creates professional AI image/video prompts with photographer's and cinematographer's eye. Specializes in composition, lighting, color grading, and storytelling. Use when generating AI images/videos with artistic vision, working with models like Nano Banana Pro, Qwen, Sora2, Wan 2.2. For graphic design work (thumbnails, banners, layouts), use /graphic-designer instead.
Expert prompt engineering for LLM applications including prompt design, optimization, RAG systems, agent architectures, and AI product development.
Test, validate, and improve agent instructions (CLAUDE.md, system prompts) using sub-agents as experiment subjects. Measures instruction compliance, context decay, and constraint strength. Use for "test prompt", "validate instructions", "prompt effectiveness", "instruction decay", or when designing robust agent behaviors.
Create optimized prompts for Claude-to-Claude pipelines with research, planning, and execution stages. Use when building prompts that produce outputs for other prompts to consume, or when running multi-stage workflows (research -> plan -> implement).
Improve a rough or thin prompt into a detailed, actionable one using project context. Use when the user types '/improve-prompt <rough idea>' or '/?? <rough idea>'. Takes a vague request and returns a well-structured prompt with specific file paths, project patterns, acceptance criteria, and relevant context. Do NOT use for executing tasks — this only improves the prompt text.
Create, improve, or optimize prompts using best practices
Forensic root cause analyzer for Antigravity sessions. Classifies scope deltas, rework patterns, root causes, hotspots, and auto-improves prompts/health.
Build type-safe LLM applications with DSPy.rb — Ruby's programmatic prompt framework with signatures, modules, agents, and optimization. Use when implementing predictable AI features, creating LLM signatures and modules, configuring language model providers, building agent systems with tools, optimizing prompts, or testing LLM-powered functionality in Ruby applications.