Refines and optimizes LLM prompts using Anthropic's structured 4-step improvement workflow.
The Prompt Optimizer skill transforms raw instructions into high-performing, structured prompts by applying Anthropic’s industry-standard best practices. It automatically organizes prompt components using XML tags, determines the ideal level of Chain of Thought (CoT) reasoning based on task complexity, and generates or enhances examples with internal thinking steps. Whether you are starting from a rough draft, an existing file, or refining a recent conversation, this skill ensures your AI interactions are clear, unambiguous, and production-ready while preventing common failure modes like accidental execution of input text.
Key Features
01Supports iterative refinement via direct feedback and context-aware optimization
02Implements Anthropic's official 4-step prompt engineering methodology
03Calibrates Chain of Thought (CoT) levels from basic to structured reasoning
0438 GitHub stars
05Generates and enhances few-shot examples with step-by-step analysis
06Automatically structures prompts with XML tags for improved model steerability
Use Cases
01Iteratively refining complex prompts based on specific performance feedback
02Converting loose instructions into a production-grade system prompt with clear boundaries
03Adding reasoning steps and synthetic examples to minimal prompts to improve accuracy
