prompt-optimization
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.
npx skills add majiayu000/claude-skill-registry --skill prompt-optimization --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.
# Prompt Optimization ## Goal Improve the user's prompt so Codex (or any LLM) produces better output while preserving intent. ## Workflow 1. Read the user's original prompt carefully. 2. Identify ambiguity, missing context, or unclear intent. 3. Rewrite the prompt to remove ambiguity and provide clear instructions. 4. Retain the core intention of the user's request. 5. Add relevant constraints (format, length, style) when helpful. ## Output format Provide: - Improved prompt - Short explanation of what was improved ## Constraints - Do not assume domain knowledge not in the original prompt. - Preserve user intent. ## Example triggers - “Draft me an email asking for feedback.” - “Turn this into a daily to-do list.” - $automating-productivity
- Goal
- Workflow
- Output format
- Constraints
- Example triggers
What does the prompt-optimization skill do?
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.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill prompt-optimization --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.