Ruflo is an execution and coordination layer for Claude Code and Codex that equips AI coding agents with tools, memory, control loops, sandboxes, and collaboration mechanisms. Developers use it to organize specialized agents into swarms, coordinate workflows, retain knowledge across sessions, and communicate across machines. The catalogue entries are Ruflo’s skills, commands, agents, hooks, and plugin components.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/ruvnet/rufloWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/ruvnet/ruflo/code-review-swarm)<a href="https://agentmods.dev/commands/ruvnet/ruflo/code-review-swarm"><img src="https://agentmods.dev/badge/commands/ruvnet/ruflo/code-review-swarm.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00000 | $0.02883 |
| Opus 5 | $0.00000 | $0.01442 |
| Sonnet 5 | $0.00000 | $0.00577 |
| Haiku 4.5 | $0.00000 | $0.00288 |
Grade A, and why
code-review-swarm scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to code-review-swarm — 2 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 514 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Code Review Swarm - Automated Code Review with AI Agents
Overview
Deploy specialized AI agents to perform comprehensive, intelligent code reviews that go beyond traditional static analysis.
Core Features
1. Multi-Agent Review System
# Initialize code review swarm with gh CLI
# Get PR details
PR_DATA=$(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 "$PR_DIFF" \
--agents "security,performance,style,architecture,accessibility" \
--depth comprehensive
# Post initial review status
gh pr comment 123 --body "🔍 Multi-agent code review initiated"
2. Specialized Review Agents
Security Agent
# Security-focused review with gh CLI
# Get changed files
CHANGED_FILES=$(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
# Performance analysis
npx ruv-swarm github review-performance \
--pr 123 \
--profile "cpu,memory,io" \
--benchmark-against main \
--suggest-optimizations
Architecture Agent
# Architecture review
npx ruv-swarm github review-architecture \
--pr 123 \
--check "patterns,coupling,cohesion,solid" \
--visualize-impact \
--suggest-refactoring
3. Review Configuration
# .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
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 4d ago First seen · 514 lines · 0 tokens per session scan A 2f1b3210ca06
code-review-swarm is a command published in the GitHub repository ruvnet/ruflo (71,074 stars, last pushed 2d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 2,883 tokens. A static security scan graded it A with 0 findings. It is 100% identical to code-review-swarm, differing in 2 lines, and is treated as a copy.
Other commands, from other repositories
review-diff
Review the current working diff for correctness, security, and reuse.
fdk-refactor
Reduce function complexity in a Freshworks app to meet cyclomatic complexity ≤ 7 per function. Extracts helper functions, simplifies conditionals, and preserves behavior while improving code quality.
briefing
Structured pre-execution briefing session -- collects case context through specialist panel, builds execution plan, supports resume and depth control.
legal-5step
Execute the BetterCallClaude 5-step Swiss legal framework: intake → research → strategy → adversarial → draft. A complete end-to-end pipeline for any Swiss legal matter, from document analysis through final legal output.
legal-goal
Define a checkable legal success condition for /legal-loop. Accepts a named profile (citations-clean, draft-passes-gate, adversarial-converge, nda-batch-clean, reg-watch, timeline-sourced) or free-text objective. Produces a persisted Goal Record — never starts work itself.
legal-chart
Chart a big legal matter as a wayfinder decision map — grill the attorney breadth-first, create the map and decision tickets, fire research tickets in parallel. Planning only: charting resolves no decisions itself.