Systematic code review catches 60-90% of defects before production, reduces maintenance costs by 40%, and serves as effective knowledge transfer. This skill provides structured review guidance for both human reviewers and AI agents.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versioncode-reviewExecute the skills CLI command in your project's root directory to begin installation:
Fetches code-review from jwynia/agent-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate code-review. Access via /code-review in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
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Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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Systematic code review catches 60-90% of defects before production, reduces maintenance costs by 40%, and serves as effective knowledge transfer. This skill provides structured review guidance for both human reviewers and AI agents.
Use this skill when:
Do NOT use this skill when:
Review effectiveness degrades sharply with PR size. Under 400 lines: highest defect detection. 400-800 lines: 50% less effective. 800+ lines: 90% less effective.
| Factor | Optimal | Degraded |
|---|---|---|
| PR size | < 400 lines | > 800 lines |
| Review time | < 60 minutes | > 90 minutes |
| Review speed | 200-400 LOC/hour | > 500 LOC/hour |
| Reviewers | 2 | 4+ (diminishing returns) |
| Level | Checks | Catches | Frequency |
|---|---|---|---|
| 1. Automated | Lint, types, unit tests, security scan | 60% | Every commit |
| 2. Integration | Integration tests, contracts, performance | 25% | Every PR |
| 3. Human Review | Design, logic, maintainability, context | 15% | Significant changes |
Questions:
Validation: Test coverage, business logic, data integrity, concurrency handling
Questions:
Indicators: Clear naming, single responsibility, minimal coupling, high cohesion
Questions:
Red Flags: N+1 queries, unbounded loops, synchronous I/O in async context, memory leaks
Questions:
Critical Checks: No hardcoded secrets, SQL parameterized, XSS prevention, CSRF tokens
| Smell | Threshold | Action |
|---|---|---|
| Long method | > 50 lines | Extract method |
| Long parameter list | > 5 params | Parameter object |
| Duplicate code | > 10 similar lines | Extract common |
| Dead code | Never called | Remove |
| Smell | Symptoms | Action |
|---|---|---|
| God class | > 1000 lines, > 20 methods | Split class |
| Feature envy | Uses other class data excessively | Move method |
| Data clumps | Same parameter groups | Extract class |
| Smell | Detection | Action |
|---|---|---|
| Circular dependencies | Dependency cycles | Introduce interface |
| Unstable dependencies | Depends on volatile modules | Dependency inversion |
[BLOCKING] - Must fix before merge
[MAJOR] - Should fix before merge
[MINOR] - Can fix in follow-up
[QUESTION] - Seeking clarification
Observation + Impact + Suggestion
Example:
"This method is 200 lines long [observation].
This makes it hard to understand and test [impact].
Consider extracting helper methods [suggestion]."
| Range | Classification | Action |
|---|---|---|
| 1-10 | Simple | OK |
| 11-20 | Moderate | Consider refactoring |
| 21-50 | Complex | Refactor required |
| > 50 | Untestable | Must decompose |
| Range | Classification |
|---|---|
| < 7 | Clear |
| 7-15 | Acceptable |
| > 15 | Confusing - refactor needed |
Approving without thorough review. "LGTM" in < 1 minute. Fix: Minimum review time, required comments, random audits.
50+ style comments, missing real issues. Fix: Automate style checks, focus on logic/design, limit minor comments.
2000+ line PRs that overwhelm. Fix: Stack small PRs, feature flags, review drafts early.
| Level | Definition | SLA |
|---|---|---|
| Critical | Remote code execution possible | Fix immediately |
| High | Data breach possible | Fix within 24 hours |
| Medium | Limited impact | Fix within sprint |
| Low | Minimal risk | Fix when convenient |
| Metric | Target |
|---|---|
| First review turnaround | < 4 hours |
| Review cycles | < 3 |
| PR to merge time | < 24 hours |
| Metric | Target |
|---|---|
| Defect detection rate | > 80% |
| Post-merge defects | < 0.5 per PR |
| Review coverage | 100% |
Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
jwynia/agent-skills
asyrafhussin/agent-skills
shadcn/improve
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
code-review reduced setup friction for our internal harness; good balance of opinion and flexibility.
I recommend code-review for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Useful defaults in code-review — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
code-review reduced setup friction for our internal harness; good balance of opinion and flexibility.
We added code-review from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Registry listing for code-review matched our evaluation — installs cleanly and behaves as described in the markdown.
code-review fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
code-review is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
code-review has been reliable in day-to-day use. Documentation quality is above average for community skills.
code-review has been reliable in day-to-day use. Documentation quality is above average for community skills.
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