Agent skill

Agent Code Review Swarm

by ruvnet in ruvnet/ruflo

Agent skill for code-review-swarm - invoke with $agent-code-review-swarm

MITAuto-check passedDevelopment

Install Agent Code Review Swarm

skills CLI
$ npx skills add ruvnet/ruflo --skill agent-code-review-swarm -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install ruvnet/ruflo agent-code-review-swarm --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/ruvnet/ruflo.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/agent-code-review-swarm .claude/skills/agent-code-review-swarm && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
agent-code-review-swarm
GitHub stars
74k
Used in
3 other repos
Token cost
~3k tokens
SKILL.md length
264 words
Files
1
Skills in repo
264
Repo updated
First seen
Licence
MIT

At a glance

Agent skill for code-review-swarm - invoke with $agent-code-review-swarm

  • Works in 12 steps: Multi-Agent Review System → Specialized Review Agents → Review Configuration → …
  • Tasks that involve Code review
  • SKILL.md covers Overview, Core Features, Review Agents and Advanced Review Features, plus 5 more sections
  • Calls npx, gh and jq; needs GITHUB_TOKEN

What it does

Agent Code Review Swarm is an agent skill from ruvnet/ruflo. Agent skill for code-review-swarm - invoke with $agent-code-review-swarm

Its SKILL.md is about 3k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Development, covering Code review. It works with Model Context Protocol. The repository describes itself as: 🌊 The original agent harness. Deploy intelligent multi-player swarms, coordinate autonomous workflows, and build conversational AI systems. Features adaptive memory…. The licence is MIT.

When your agent uses it

  • Tasks that involve Code review

Example prompts

  • “/agent-code-review-swarm”

Requirements

  • Node.js
  • A credential in GITHUB_TOKEN

Workflow steps

12 steps, taken from the step headings in SKILL.md.

  1. Multi-Agent Review System
  2. Specialized Review Agents
  3. Review Configuration
  4. Context-Aware Reviews
  5. Learning from History
  6. Cross-PR Analysis
  7. Review Configuration
  8. Comment Quality
  9. Performance
  10. AI Learning
  11. Custom Review Agents
  12. Review Orchestration

What it can do on your machine

Read from SKILL.md and the folder at commit de590e1. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • npx
    • gh
    • jq

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use npx and gh, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • GITHUB_TOKEN

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Agent Code Review Swarm loads about 3k tokens when it runs. Until then it costs about 24 tokens; SKILL.md has 264 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~24
When it runs · the whole SKILL.md, loaded when a task matches
~3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from ruvnet/ruflo at commit de590e1, republished under its MIT licence (© ruvnet). 264 words, ~3,046 tokens.

Download SKILL.mdSave it as .claude/skills/agent-code-review-swarm/SKILL.md (or your agent's skills folder).
name
agent-code-review-swarm
description
Agent skill for code-review-swarm - invoke with $agent-code-review-swarm

name: code-review-swarm description: Deploy specialized AI agents to perform comprehensive, intelligent code reviews that go beyond traditional static analysis tools: mcp__claude-flow__swarm_init, mcp__claude-flow__agent_spawn, mcp__claude-flow__task_orchestrate, Bash, Read, Write, TodoWrite color: blue type: development capabilities:

  • Automated multi-agent code review
  • Security vulnerability analysis
  • Performance bottleneck detection
  • Architecture pattern validation
  • Style and convention enforcement priority: high hooks: pre: | echo "Starting code-review-swarm..." echo "Initializing multi-agent review system" gh auth status || (echo "GitHub CLI not authenticated" && exit 1) post: | echo "Completed code-review-swarm" echo "Review results posted to GitHub" echo "Quality gates evaluated"

Code Review Swarm - Automated Code Review with AI Agents

Overview

Deploy specialized AI agents to perform comprehensive, intelligent code reviews that go beyond traditional static analysis.

Core Features

1. Multi-Agent Review System
bash
# Initialize code review swarm with gh CLI
# Get PR details
PR_DATA=$(gh pr view 123 --json files,additions,deletions,title,body)
PR_DIFF=$(gh pr diff 123)

# Initialize swarm with PR context
npx ruv-swarm github review-init \
  --pr 123 \
  --pr-data "$PR_DATA" \
  --diff "$PR_DIFF" \
  --agents "security,performance,style,architecture,accessibility" \
  --depth comprehensive

# Post initial review status
gh pr comment 123 --body "🔍 Multi-agent code review initiated"
2. Specialized Review Agents
Security Agent
bash
# Security-focused review with gh CLI
# Get changed files
CHANGED_FILES=$(gh pr view 123 --json files --jq '.files[].path')

# Run security review
SECURITY_RESULTS=$(npx ruv-swarm github review-security \
  --pr 123 \
  --files "$CHANGED_FILES" \
  --check "owasp,cve,secrets,permissions" \
  --suggest-fixes)

# Post security findings
if echo "$SECURITY_RESULTS" | grep -q "critical"; then
  # Request changes for critical issues
  gh pr review 123 --request-changes --body "$SECURITY_RESULTS"
  # Add security label
  gh pr edit 123 --add-label "security-review-required"
else
  # Post as comment for non-critical issues
  gh pr comment 123 --body "$SECURITY_RESULTS"
fi
Performance Agent
bash
# Performance analysis
npx ruv-swarm github review-performance \
  --pr 123 \
  --profile "cpu,memory,io" \
  --benchmark-against main \
  --suggest-optimizations
Architecture Agent
bash
# Architecture review
npx ruv-swarm github review-architecture \
  --pr 123 \
  --check "patterns,coupling,cohesion,solid" \
  --visualize-impact \
  --suggest-refactoring
3. Review Configuration
yaml
# .github$review-swarm.yml
version: 1
review:
  auto-trigger: true
  required-agents:
    - security
    - performance
    - style
  optional-agents:
    - architecture
    - accessibility
    - i18n
  
  thresholds:
    security: block
    performance: warn
    style: suggest
    
  rules:
    security:
      - no-eval
      - no-hardcoded-secrets
      - proper-auth-checks
    performance:
      - no-n-plus-one
      - efficient-queries
      - proper-caching
    architecture:
      - max-coupling: 5
      - min-cohesion: 0.7
      - follow-patterns

Review Agents

Security Review Agent
javascript
// Security checks performed
{
  "checks": [
    "SQL injection vulnerabilities",
    "XSS attack vectors",
    "Authentication bypasses",
    "Authorization flaws",
    "Cryptographic weaknesses",
    "Dependency vulnerabilities",
    "Secret exposure",
    "CORS misconfigurations"
  ],
  "actions": [
    "Block PR on critical issues",
    "Suggest secure alternatives",
    "Add security test cases",
    "Update security documentation"
  ]
}
Performance Review Agent
javascript
// Performance analysis
{
  "metrics": [
    "Algorithm complexity",
    "Database query efficiency",
    "Memory allocation patterns",
    "Cache utilization",
    "Network request optimization",
    "Bundle size impact",
    "Render performance"
  ],
  "benchmarks": [
    "Compare with baseline",
    "Load test simulations",
    "Memory leak detection",
    "Bottleneck identification"
  ]
}
Style & Convention Agent
javascript
// Style enforcement
{
  "checks": [
    "Code formatting",
    "Naming conventions",
    "Documentation standards",
    "Comment quality",
    "Test coverage",
    "Error handling patterns",
    "Logging standards"
  ],
  "auto-fix": [
    "Formatting issues",
    "Import organization",
    "Trailing whitespace",
    "Simple naming issues"
  ]
}
Architecture Review Agent
javascript
// Architecture analysis
{
  "patterns": [
    "Design pattern adherence",
    "SOLID principles",
    "DRY violations",
    "Separation of concerns",
    "Dependency injection",
    "Layer violations",
    "Circular dependencies"
  ],
  "metrics": [
    "Coupling metrics",
    "Cohesion scores",
    "Complexity measures",
    "Maintainability index"
  ]
}

Advanced Review Features

1. Context-Aware Reviews
bash
# Review with full context
npx ruv-swarm github review-context \
  --pr 123 \
  --load-related-prs \
  --analyze-impact \
  --check-breaking-changes
2. Learning from History
bash
# Learn from past reviews
npx ruv-swarm github review-learn \
  --analyze-past-reviews \
  --identify-patterns \
  --improve-suggestions \
  --reduce-false-positives
3. Cross-PR Analysis
bash
# Analyze related PRs together
npx ruv-swarm github review-batch \
  --prs "123,124,125" \
  --check-consistency \
  --verify-integration \
  --combined-impact

Review Automation

Auto-Review on Push
yaml
# .github$workflows$auto-review.yml
name: Automated Code Review
on:
  pull_request:
    types: [opened, synchronize]

jobs:
  swarm-review:
    runs-on: ubuntu-latest
    steps:
      - uses: actions$checkout@v3
        with:
          fetch-depth: 0
          
      - name: Setup GitHub CLI
        run: echo "${{ secrets.GITHUB_TOKEN }}" | gh auth login --with-token
          
      - name: Run Review Swarm
        run: |
          # Get PR context with gh CLI
          PR_NUM=${{ github.event.pull_request.number }}
          PR_DATA=$(gh pr view $PR_NUM --json files,title,body,labels)
          
          # Run swarm review
          REVIEW_OUTPUT=$(npx ruv-swarm github review-all \
            --pr $PR_NUM \
            --pr-data "$PR_DATA" \
            --agents "security,performance,style,architecture")
          
          # Post review results
          echo "$REVIEW_OUTPUT" | gh pr review $PR_NUM --comment -F -
          
          # Update PR status
          if echo "$REVIEW_OUTPUT" | grep -q "approved"; then
            gh pr review $PR_NUM --approve
          elif echo "$REVIEW_OUTPUT" | grep -q "changes-requested"; then
            gh pr review $PR_NUM --request-changes -b "See review comments above"
          fi
Review Triggers
javascript
// Custom review triggers
{
  "triggers": {
    "high-risk-files": {
      "paths": ["**$auth/**", "**$payment/**"],
      "agents": ["security", "architecture"],
      "depth": "comprehensive"
    },
    "performance-critical": {
      "paths": ["**$api/**", "**$database/**"],
      "agents": ["performance", "database"],
      "benchmarks": true
    },
    "ui-changes": {
      "paths": ["**$components/**", "**$styles/**"],
      "agents": ["accessibility", "style", "i18n"],
      "visual-tests": true
    }
  }
}

Review Comments

Intelligent Comment Generation
bash
# Generate contextual review comments with gh CLI
# Get PR diff with context
PR_DIFF=$(gh pr diff 123 --color never)
PR_FILES=$(gh pr view 123 --json files)

# Generate review comments
COMMENTS=$(npx ruv-swarm github review-comment \
  --pr 123 \
  --diff "$PR_DIFF" \
  --files "$PR_FILES" \
  --style "constructive" \
  --include-examples \
  --suggest-fixes)

# Post comments using gh CLI
echo "$COMMENTS" | jq -c '.[]' | while read -r comment; do
  FILE=$(echo "$comment" | jq -r '.path')
  LINE=$(echo "$comment" | jq -r '.line')
  BODY=$(echo "$comment" | jq -r '.body')
  
  # Create review with inline comments
  gh api \
    --method POST \
    $repos/:owner/:repo$pulls/123$comments \
    -f path="$FILE" \
    -f line="$LINE" \
    -f body="$BODY" \
    -f commit_id="$(gh pr view 123 --json headRefOid -q .headRefOid)"
done
Comment Templates
markdown
<!-- Security Issue Template -->
🔒 **Security Issue: [Type]**

**Severity**: 🔴 Critical / 🟡 High / 🟢 Low

**Description**: 
[Clear explanation of the security issue]

**Impact**:
[Potential consequences if not addressed]

**Suggested Fix**:
```language
[Code example of the fix]

References:


### Batch Comment Management
```bash
# Manage review comments efficiently
npx ruv-swarm github review-comments \
  --pr 123 \
  --group-by "agent,severity" \
  --summarize \
  --resolve-outdated

Integration with CI/CD

Status Checks
yaml
# Required status checks
protection_rules:
  required_status_checks:
    contexts:
      - "review-swarm$security"
      - "review-swarm$performance"
      - "review-swarm$architecture"
Quality Gates
bash
# Define quality gates
npx ruv-swarm github quality-gates \
  --define '{
    "security": {"threshold": "no-critical"},
    "performance": {"regression": "<5%"},
    "coverage": {"minimum": "80%"},
    "architecture": {"complexity": "<10"}
  }'
Review Metrics
bash
# Track review effectiveness
npx ruv-swarm github review-metrics \
  --period 30d \
  --metrics "issues-found,false-positives,fix-rate" \
  --export-dashboard

Best Practices

1. Review Configuration
  • Define clear review criteria
  • Set appropriate thresholds
  • Configure agent specializations
  • Establish override procedures
2. Comment Quality
  • Provide actionable feedback
  • Include code examples
  • Reference documentation
  • Maintain respectful tone
3. Performance
  • Cache analysis results
  • Incremental reviews for large PRs
  • Parallel agent execution
  • Smart comment batching

Advanced Features

1. AI Learning
bash
# Train on your codebase
npx ruv-swarm github review-train \
  --learn-patterns \
  --adapt-to-style \
  --improve-accuracy
2. Custom Review Agents
javascript
// Create custom review agent
class CustomReviewAgent {
  async review(pr) {
    const issues = [];
    
    // Custom logic here
    if (await this.checkCustomRule(pr)) {
      issues.push({
        severity: 'warning',
        message: 'Custom rule violation',
        suggestion: 'Fix suggestion'
      });
    }
    
    return issues;
  }
}
3. Review Orchestration
bash
# Orchestrate complex reviews
npx ruv-swarm github review-orchestrate \
  --strategy "risk-based" \
  --allocate-time-budget \
  --prioritize-critical

Examples

Security-Critical PR
bash
# Auth system changes
npx ruv-swarm github review-init \
  --pr 456 \
  --agents "security,authentication,audit" \
  --depth "maximum" \
  --require-security-approval
Performance-Sensitive PR
bash
# Database optimization
npx ruv-swarm github review-init \
  --pr 789 \
  --agents "performance,database,caching" \
  --benchmark \
  --profile
UI Component PR
bash
# New component library
npx ruv-swarm github review-init \
  --pr 321 \
  --agents "accessibility,style,i18n,docs" \
  --visual-regression \
  --component-tests

Monitoring & Analytics

Review Dashboard
bash
# Launch review dashboard
npx ruv-swarm github review-dashboard \
  --real-time \
  --show "agent-activity,issue-trends,fix-rates"
Review Reports
bash
# Generate review reports
npx ruv-swarm github review-report \
  --format "markdown" \
  --include "summary,details,trends" \
  --email-stakeholders

See also: swarm-pr.md, workflow-automation.md

© ruvnet, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in .agents/skills/agent-code-review-swarm of ruvnet/ruflo.

Open the folder on GitHubat commit de590e1

Used in 3 other repositories

We found 4 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 3 other GitHub owners. This page covers the copy in ruvnet/ruflo, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Agent Code Review Swarm next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Agent Code Review Swarm compared with similar skills
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Agent Code Review Swarm this skillruvnet/ruflo74k3 repos~3kAutomated safety check: PassMIT
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YugabyteDB Backport Reviewyugabyte/yugabyte-db11k—~2.5kAutomated safety check: PassCustom licence
Liveagent Code ReviewStack-Cairn/LiveAgent2.2k—~2kAutomated safety check: PassMIT
Code Review Graph Navigatorhandsontable/handsontable22k—~939Automated safety check: PassCustom licence
Code Reviewnteract/semiotic2.7k—~1.5kAutomated safety check: PassApache-2.0

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Categories

Questions about Agent Code Review Swarm

What does Agent Code Review Swarm do?

Agent skill for code-review-swarm - invoke with $agent-code-review-swarm. Agent Code Review Swarm is an agent skill from ruvnet/ruflo.

When should I use Agent Code Review Swarm?

Agent Code Review Swarm fits situations like: tasks that involve Code review.

How do I install Agent Code Review Swarm in Claude Code?

Run `npx skills add ruvnet/ruflo --skill agent-code-review-swarm -a claude-code`. Or copy the skill folder (.agents/skills/agent-code-review-swarm in ruvnet/ruflo) into .claude/skills/agent-code-review-swarm in your project. Claude Code loads it when a task matches its description.

How do I install Agent Code Review Swarm in Codex?

Run `npx skills add ruvnet/ruflo --skill agent-code-review-swarm -a codex`. Or copy the skill folder (.agents/skills/agent-code-review-swarm in ruvnet/ruflo) into .agents/skills/agent-code-review-swarm in your project. Codex loads it when a task matches its description.

Can I use Agent Code Review Swarm in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add ruvnet/ruflo --skill agent-code-review-swarm -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/agent-code-review-swarm, .gemini/skills/agent-code-review-swarm, .github/skills/agent-code-review-swarm and .opencode/skills/agent-code-review-swarm in your project.

What does Agent Code Review Swarm need to run?

Going by SKILL.md and its folder, Agent Code Review Swarm needs the command-line tools its instructions call (npx, gh and jq) and credentials named GITHUB_TOKEN. Our summary lists: Node.js; A credential in GITHUB_TOKEN.

Does Agent Code Review Swarm access the network?

SKILL.md contains no URLs. Its commands use npx and gh, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Agent Code Review Swarm safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Agent Code Review Swarm use?

Agent Code Review Swarm is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Agent Code Review Swarm use?

About 3k tokens (SKILL.md is roughly 12k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Agent Code Review Swarm?

Skills that share tags, products or a category with Agent Code Review Swarm: Graph-Based Change Review (tirth8205/code-review-graph, 32k stars), YugabyteDB Backport Review (yugabyte/yugabyte-db, 11k stars), Liveagent Code Review (Stack-Cairn/LiveAgent, 2.2k stars) and Code Review Graph Navigator (handsontable/handsontable, 22k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Agent Code Review Swarm?

ruvnet (a GitHub user) maintains it in ruvnet/ruflo, which has 74,012 GitHub stars. The repository holds 264 skills in this directory. The repository was last updated on October 7, 2026.

Source: ruvnet/ruflo on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.