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---
name: review-prompt
description: 'Review LLM prompts against the prompt-engineering skill''s principles — leading with where each line came from — and report the findings without modifying files. Use when reviewing prompt quality, auditing a prompt, evaluating a system prompt, or checking whether prompt issues are high-confidence and fixable.'
user-invocable: true
---
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.
The provenance question leads: for each line, where did it come from — a user ruling, knowledge outside what the run will read, or a default it counteracts? A line the author could have worked out from material the run also gets is the finding. Judge a prompt by what its lines do, never by sections it lacks: a short prompt with nothing spare is the target, not a deficient one.
**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; a line whose only provenance is the author's own reading; a real gap left unclosed; 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 across multiple places.
- **Low** — minor friction with no functional impact. Examples: duplication that doesn't change behavior; awkward phrasing where the meaning is still unambiguous; stylistic-only cleanup.
**Tag** (parsed as control flow by `/auto-optimize-prompt` — do not rename or repurpose):
- `NEEDS_USER_INPUT` — only the author can resolve: missing context, unclear intent, ambiguity the model can't infer.
- `AUTO_FIXABLE` — a clear fix exists per the loaded principles.
Only flag high-confidence issues. Low-confidence findings are noise — skip style preferences, minor wording, and unverified hunches.