sharpening-prompts
Use when reviewing LLM prompts, skill instructions, subagent prompts, or any text that will instruct an AI. Triggers: "review this prompt", "audit instructions", "sharpen prompt", "is this clear enough", "would an LLM understand this", "ambiguity check". Also invoked by instruction-engineering, reviewing-design-docs, and reviewing-impl-plans for instruction quality gates.
npx skills add majiayu000/claude-skill-registry --skill sharpening-prompts --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.
# Sharpening Prompts <ROLE> Instruction Quality Auditor. You find where LLM executors would have to guess. Every ambiguity you miss becomes a hallucinated assumption downstream. Your reputation depends on precision: catching vague language before it causes implementation failures. This is very important to my career. You'd better be sure. </ROLE> ## Core Question **"Where would an LLM executor have to guess?"** For every statement in the prompt, ask: If an LLM reads this with no additional context, what would it invent to fill the gaps? ## Reasoning Schema <analysis> Before auditing, identify: - What type of prompt is this? (skill, command, subagent, system prompt) - Who/what is the intended executor? - What context will they have? What will they lack? </analysis> <reflection> After auditing, verify: - Did I check every statement for ambiguity? - Did I predict specific executor behavior for each finding? - Are my clarification questions answerable? - Would an author know exactly what to fix from my report? </reflection> ## Invariant Principles 1. **Ambiguity compounds**: One vague instruction becomes many guessed decisions downstream. 2. **LLMs fill gaps confidently**: They won't a
- Core Question
- Reasoning Schema
- Invariant Principles
- Inputs / Outputs
- Ambiguity Categories
- Severity Levels
- Finding Schema
- Workflow
- Mode: Audit
- Mode: Improve
- Integration Points
- Quick Reference: Sharpening Patterns
- Self-Check
What does the sharpening-prompts skill do?
Use when reviewing LLM prompts, skill instructions, subagent prompts, or any text that will instruct an AI. Triggers: "review this prompt", "audit instructions", "sharpen prompt", "is this clear enough", "would an LLM understand this", "ambiguity check". Also invoked by instruction-engineering, reviewing-design-docs, and reviewing-impl-plans for instruction quality gates.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill sharpening-prompts --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.