review-prompt

Review LLM prompts. Report findings without modifying files — this skill diagnoses; it never edits the prompt. **First**: invoke the prompt-engineering skill to load the principles. Evaluate the prompt against them. **Input**: if no prompt is given (file path or inline text), ask before analyzing — don't assume. Report format: ## Assessment: {Excellent | Good with Minor Issues | Needs Work} **Strengths** — what's working, what the author should preserve. **Issues**: | Issue | Severity | Fix | Tag | |-------|----------|-----|-----| | {description} | High / Medium / Low | {concrete recommendation} | `NEEDS_USER_INPUT` or `AUTO_FIXABLE` | **Priority**: the highest-impact change first. **Severity**: - **High** — the prompt actively misbehaves or breaks a contract. Examples: contradiction between two rules that can't both hold; missing the goal entirely; absolute used on a judgment call that observably misfires; the agent declares a need for a tool it doesn't have, or omits a tool it actually uses. - **Medium** — the prompt works but drifts toward known failure modes. Examples: vague directive that produces inconsistent behavior across runs; restated model default adding noise the model has to wade through; missing a gap-closer that the discipline says should be there; arbitrary numbers without a rubric; boundary failures — naming a harness-bound primitive, a rule-scope qualifier that silently excludes valid cases, mechanism stated as the only path, or one principle split...