Coordinate parallel code reviews across multiple quality dimensions with deduplication and severity calibration.
Works with
Allocates reviews across five dimensions (Security, Performance, Architecture, Testing, Accessibility) with recommended combinations for different code change types
Deduplicates findings from multiple reviewers using merge rules based on file location and issue type, with conflict resolution for severity ratings
Provides severity calibration criteria (Critical, High, Mediu
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionmulti-reviewer-patternsExecute the skills CLI command in your project's root directory to begin installation:
Fetches multi-reviewer-patterns from wshobson/agents 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 multi-reviewer-patterns. Access via /multi-reviewer-patterns 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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Patterns for coordinating parallel code reviews across multiple quality dimensions, deduplicating findings, calibrating severity, and producing consolidated reports.
| Dimension | Focus | When to Include |
|---|---|---|
| Security | Vulnerabilities, auth, input validation | Always for code handling user input or auth |
| Performance | Query efficiency, memory, caching | When changing data access or hot paths |
| Architecture | SOLID, coupling, patterns | For structural changes or new modules |
| Testing | Coverage, quality, edge cases | When adding new functionality |
| Accessibility | WCAG, ARIA, keyboard nav | For UI/frontend changes |
| Scenario | Dimensions |
|---|---|
| API endpoint changes | Security, Performance, Architecture |
| Frontend component | Architecture, Testing, Accessibility |
| Database migration | Performance, Architecture |
| Authentication changes | Security, Testing |
| Full feature review | Security, Performance, Architecture, Testing |
When multiple reviewers report issues at the same location:
For each finding in all reviewer reports:
1. Check if another finding references the same file:line
2. If yes, check if they describe the same issue
3. If same issue: merge, keeping the more detailed description
4. If different issue: keep both, tag as "co-located"
5. Use highest severity among merged findings
| Severity | Impact | Likelihood | Examples |
|---|---|---|---|
| Critical | Data loss, security breach, complete failure | Certain or very likely | SQL injection, auth bypass, data corruption |
| High | Significant functionality impact, degradation | Likely | Memory leak, missing validation, broken flow |
| Medium | Partial impact, workaround exists | Possible | N+1 query, missing edge case, unclear error |
| Low | Minimal impact, cosmetic | Unlikely | Style issue, minor optimization, naming |
## Code Review Report
**Target**: {files/PR/directory}
**Reviewers**: {dimension-1}, {dimension-2}, {dimension-3}
**Date**: {date}
**Files Reviewed**: {count}
### Critical Findings ({count})
#### [CR-001] {Title}
**Location**: `{file}:{line}`
**Dimension**: {Security/Performance/etc.}
**Description**: {what was found}
**Impact**: {what could happen}
**Fix**: {recommended remediation}
### High Findings ({count})
...
### Medium Findings ({count})
...
### Low Findings ({count})
...
### Summary
| Dimension | Critical | High | Medium | Low | Total |
| ------------ | -------- | ----- | ------ | ----- | ------ |
| Security | 1 | 2 | 3 | 0 | 6 |
| Performance | 0 | 1 | 4 | 2 | 7 |
| Architecture | 0 | 0 | 2 | 3 | 5 |
| **Total** | **1** | **3** | **9** | **5** | **18** |
### Recommendation
{Overall assessment and prioritized action items}
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.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
multi-reviewer-patterns has been reliable in day-to-day use. Documentation quality is above average for community skills.
Useful defaults in multi-reviewer-patterns — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
We added multi-reviewer-patterns from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Keeps context tight: multi-reviewer-patterns is the kind of skill you can hand to a new teammate without a long onboarding doc.
Useful defaults in multi-reviewer-patterns — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
multi-reviewer-patterns has been reliable in day-to-day use. Documentation quality is above average for community skills.
Registry listing for multi-reviewer-patterns matched our evaluation — installs cleanly and behaves as described in the markdown.
multi-reviewer-patterns fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
We added multi-reviewer-patterns from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
I recommend multi-reviewer-patterns for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
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