review-prompt

Audits AI prompts to find unclear instructions or confusing wording that could cause mistakes.

Installation
Run `npx skills add "https://github.com/doodledood/manifest-dev" --skill "review-prompt"` to install this skill, then follow its SKILL.md instructions for my next request.

Paste this into Claude Code, Cursor, or any agent that can run commands.

SKILL.mdShow the author's original SKILL.md
---
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; modify no file.

**First**: invoke the prompt-engineering skill to load the principles. Evaluate the prompt against them. They are opinionated defaults: where a rule the user stated, in the session or a context file, or a convention the project states or documents differs, that rule governs, a prompt following it raises no finding, and the report names the rule applied — except a High issue on the scale below that makes the prompt miss its goal, which is still reported, naming the rule that produced it.

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? Identify material the author merely derived from what the run also receives, and show the consequence of retaining it. Judge a prompt by what its lines do for its actual goal, inputs and consumer, never by sections it lacks: a short prompt with nothing spare is the target, not a deficient one.

The caller owns acceptance. When asked for a gate verdict, assess its substantive claims and any
deliberately required policy. A grade or removable sentence alone does not establish a material
failure; state the conflicting instruction, missing context or concrete behavioral or reading
cost. Required evidence that is missing leaves the assessment unresolved. Otherwise report the
findings and assessment without inventing a severity threshold.

**Input**: if no prompt is given (file path or inline text), ask before analyzing — don't assume. With no one to answer, report BLOCKED, naming the missing input.

Report format:

**Strengths** — what's working, what the author should preserve.

**Issues**:

| Issue and evidence of its consequence | Severity | Fix |
|---------------------------------------|----------|-----|
| {description and affected claim or concrete cost} | High / Medium / Low | {concrete recommendation} |

**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** — a concrete quality gap materially impairs the prompt's use. Examples: an ambiguous directive produces inconsistent behavior; duplicated or historical instructions obscure which rule applies; an arbitrary number forces the wrong decision; a host-specific primitive excludes a supported context. Name that cost rather than grading a category of wording automatically.
- **Low** — polish or a nit of any kind: style, naming, structure, or otherwise. Example: duplication that adds reading load without changing behavior or the underlying promise.

Report a finding only when you can point to the text and name its concrete cost; an unverified hunch is not a finding. Style preferences and minor wording land at Low.

Mirrored from the author's public source. Install counts from the open skills registry.

The systems behind these skills get built for partners every week.

Partner with us