llm-prompt-optimizer

# LLM Prompt Optimizer ## Overview This skill transforms weak, vague, or inconsistent prompts into precision-engineered instructions that reliably produce high-quality outputs from any LLM (Claude, Gemini, GPT-4, Llama, etc.). It applies systematic prompt engineering frameworks — from zero-shot to few-shot, chain-of-thought, and structured output patterns. ## When to Use This Skill - Use when a prompt returns inconsistent, vague, or hallucinated results - Use when you need structured/JSON output from an LLM reliably - Use when designing system prompts for AI agents or chatbots - Use when you want to reduce token usage without sacrificing quality - Use when implementing chain-of-thought reasoning for complex tasks - Use when prompts work on one model but fail on another ## Step-by-Step Guide ### 1. Diagnose the Weak Prompt Before optimizing, identify which problem pattern applies: | Problem | Symptom | Fix | |---------|---------|-----| | Too vague | Generic, unhelpful answers | Add role + context + constraints | | No structure | Unformatted, hard-to-parse output | Specify output format explicitly | | Hallucination | Confident wrong answers | Add "say I don't know if unsure" | | Inconsistent | Different answers each run | Add few-shot examples | | Too long | Verbose, padded responses | Add length constraints | ### 2. Apply the RSCIT Framework Every optimized prompt should have: - **R** — **Role**: Who is the AI in this interaction? - **S** — **Situation**: What conte...