Coordinate parallel code reviews across multiple quality dimensions with finding deduplication, severity calibration, and consolidated reporting. Use this skill when organizing multi-reviewer code reviews, calibrating finding severity, or consolidating review results.
npx skills add https://github.com/wshobson/agents --skill multi-reviewer-patterns
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}
Use when receiving code review feedback, before implementing suggestions, especially if feedback seems unclear or technically questionable - requires technical rigor and verification, not performative agreement or blind implementation
Use when completing tasks, implementing major features, or before merging to verify work meets requirements
Execute git commit with conventional commit message analysis, intelligent staging, and message generation. Use when user asks to commit changes, create a git commit, or mentions "/commit". Supports: (1) Auto-detecting type and scope from changes, (2) Generating conventional commit messages from diff, (3) Interactive commit with optional type/scope/description overrides, (4) Intelligent file staging for logical grouping
Comprehensive GitHub code review with AI-powered swarm coordination
Behavioral guidelines to reduce common LLM coding mistakes. Use when writing, reviewing, or refactoring code to avoid overcomplication, make surgical changes, surface assumptions, and define verifiable success criteria.
Use this skill to review code. It supports both local changes (staged or working tree) and remote Pull Requests (by ID or URL). It focuses on correctness, maintainability, and adherence to project standards.
Refactor bloated AGENTS.md, CLAUDE.md, or similar agent instruction files to follow progressive disclosure principles. Splits monolithic files into organized, linked documentation.
Create high-quality git commits: review/stage intended changes, split into logical commits, and write clear commit messages (including Conventional Commits). Use when the user asks to commit, craft a commit message, stage changes, or split work into multiple commits.
Take wshobson/multi-reviewer-patterns from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.