llm-prompt-optimizer
Use when improving prompts for any LLM. Applies proven prompt engineering techniques to boost output quality, reduce hallucinations, and cut token usage.
npx skills add majiayu000/claude-skill-registry --skill llm-prompt-optimizer --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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" | | Inco
- Overview
- When to Use This Skill
- Step-by-Step Guide
- 1. Diagnose the Weak Prompt
- 2. Apply the RSCIT Framework
- 3. Chain-of-Thought (CoT) Pattern
- 4. Few-Shot Examples Pattern
- 5. Structured JSON Output Pattern
- 6. Reduce Hallucination Pattern
- 7. Prompt Compression Techniques
- Best Practices
- Prompt Audit Checklist
- Troubleshooting
- Limitations
What does the llm-prompt-optimizer skill do?
Use when improving prompts for any LLM. Applies proven prompt engineering techniques to boost output quality, reduce hallucinations, and cut token usage.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill llm-prompt-optimizer --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.