prompt-reviewer
Skill3 starsChanged 4 months agoMITView on GitHub
Review prompts against quality, safety, and reliability criteria; produce findings with severity and fix recommendations. Use when working on prompt engineering, AI workflows, agents, context design, or LLM optimization.
2 other projects use a version of this.
Adopted by
Most recently taken up by
- Git-FED/MuseAi-Skills-Guidetheir own version, first seen Nov 8, 2026
- dementor88/prompt-reviewertheir own version, first seen Oct 15, 2026
Other versions2 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 2
- Git-FED/MuseAi-Skills-Guide★ 121same name
- dementor88/prompt-reviewer★ 0same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository59 other files it gives its agents.
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The file
We split this skill into the rules it gives an agent
2 rules. About 0 are common, 2 are distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈2
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 107
Test mentally with adversarial inputs.
- Distinctive: line 109
Verify output format is parseable.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Prompt Reviewer
Purpose
Review prompts against quality, safety, and reliability criteria; produce findings with severity and fix recommendations.
Input: Prompt specification or raw prompt text, use case context, risk level
Output: Prompt Review Report with scored dimensions, findings, and revised sections
Examples: See examples.md for worked input/output.
Workflow
Step 1: Establish review context
Confirm:
- Prompt purpose and use case
- Risk level (internal / customer-facing / regulated)
- Model target (if known)
- Expected output format
Step 2: Score review dimensions
| Dimension | Score (1-5) | Notes |
| Clarity | Instructions unambiguous | |
| Completeness | All needed context and rules | |
| Output contract | Format enforceable | |
| Safety | Injection resistance, refusal behavior | |
| Efficiency | Token usage reasonable | |
| Testability | Can verify output programmatically | |
| Maintainability | Variables, versioning, modularity |
Step 3: Run safety checklist
- Input delimiter boundaries defined
- Instruction/data separation clear
- Out-of-scope refusal behavior
- No secrets or credentials in template
- PII handling rules if applicable
- Jailbreak resistance for customer-facing
Step 4: Document findings
| ID | Severity | Category | Finding | Fix |
Severity: Critical | High | Medium | Low
Step 5: Provide revised sections
For Critical/High findings, rewrite affected prompt sections.
Step 6: Validate
Run Validation checklist.
Decision Rules
| Condition | Action |
| Customer-facing with no refusal rules | Critical finding; block deploy |
| Instructions contradict examples | High finding; fix examples or instructions |
| Ambiguous output format | High finding; specify exact format |
| Role Play detected | Medium finding; rewrite to task-based instructions |
| No test cases provided | Recommend prompt-engineer test cases |
Validation
- All 7 dimensions scored with notes
- Safety checklist completed
- ≥3 findings documented (or explicit "no issues" with evidence)
- Critical/High findings have fix recommendations
- Revised sections provided for Critical/High
- Overall deploy recommendation: Approve / Approve with fixes / Block
- No Role Play introduced in revisions
Anti-patterns
- Rubber stamp approval — all 5s without analysis.
- Style-only feedback — word choice vs structural issues.
- Missing safety on external prompts — ignoring injection risks.
- Vague fixes — "make it clearer" without rewrite.
- Ignoring examples — examples that teach wrong behavior.
Best Practices
- Test mentally with adversarial inputs.
- Check instruction-example consistency.
- Verify output format is parseable.
- Align safety level with use case risk.
- Reference OWASP LLM Top 1 (prompt injection) for external prompts.
Output Structure
# Prompt Review: [Prompt Name]
**Date:** YYYY-MM-DD
**Risk level:** [level]
**Recommendation:** Approve | Approve with fixes | Block
## Dimension Scores
| Dimension | Score | Notes |
|-----------|-------|-------|
## Safety Checklist
- [x] or [ ] [item]
## Findings
| ID | Severity | Finding | Fix |
|----|----------|---------|-----|
## Revised Sections
### [Section name]
[Rewritten content]
## Required Actions Before Deploy
1. [Action]Next Skills
| Outcome | Recommended Skill |
| Optimize approved prompt | ai/prompt-optimizer |
| Major rewrite needed | ai/prompt-engineer |
| Context budget issues | ai/context-engineering |
| AI security review | security/prompt-injection-detector |
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