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.
npx skills add ruvnet/ruflo --skill agent-v3-queen-coordinatorgit 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/skills/ruvnet/ruflo/agent-v3-queen-coordinator)<a href="https://agentmods.dev/skills/ruvnet/ruflo/agent-v3-queen-coordinator"><img src="https://agentmods.dev/badge/skills/ruvnet/ruflo/agent-v3-queen-coordinator.svg" alt="Measured on agentmods" height="20"></a>- Socket warn
- Snyk warn
- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium MCP Rug Pull · line 23 npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.Fix: Pin the version: npx @scope/[email protected]
- medium MCP Rug Pull · line 40 npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.Fix: Pin the version: npx @scope/[email protected]
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 | $0.00028 | $0.01022 |
| Opus 5 | $0.00014 | $0.00511 |
| Sonnet 5 | $0.00006 | $0.00204 |
| Haiku 4.5 | $0.00003 | $0.00102 |
Grade A, and why
agent-v3-queen-coordinator 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.
Copies of this mod
3 near-identical copies found in the catalogue:
- agent-v3-queen-coordinator — 100% identical, 0 lines differ
- agent-v3-queen-coordinator — 100% identical, 0 lines differ
- agent-v3-queen-coordinator — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 103 lines — stays where its author put it; the contents beside it link to each section on GitHub.
name: v3-queen-coordinator version: "3.0.0-alpha" updated: "2026-01-04" description: V3 Queen Coordinator for 15-agent concurrent swarm orchestration, GitHub issue management, and cross-agent coordination. Implements ADR-001 through ADR-010 with hierarchical mesh topology for 14-week v3 delivery. color: purple metadata: v3_role: "orchestrator" agent_id: 1 priority: "critical" concurrency_limit: 1 phase: "all" hooks: pre_execution: | echo "👑 V3 Queen Coordinator starting 15-agent swarm orchestration..."
# Check intelligence status
npx agentic-flow@alpha hooks intelligence stats --json > $tmp$v3-intel.json 2>$dev$null || echo '{"initialized":false}' > $tmp$v3-intel.json
echo "🧠 RuVector: $(cat $tmp$v3-intel.json | jq -r '.initialized // false')"
# GitHub integration check
if command -v gh &> $dev$null; then
echo "🐙 GitHub CLI available"
gh auth status &>$dev$null && echo "✅ Authenticated" || echo "⚠️ Auth needed"
fi
# Initialize v3 coordination
echo "🎯 Mission: ADR-001 to ADR-010 implementation"
echo "📊 Targets: 2.49x-7.47x performance, 150x search, 50-75% memory reduction"
post_execution: | echo "👑 V3 Queen coordination complete"
# Store coordination patterns
npx agentic-flow@alpha memory store-pattern \
--session-id "v3-queen-$(date +%s)" \
--task "V3 Orchestration: $TASK" \
--agent "v3-queen-coordinator" \
--status "completed" 2>$dev$null || true
V3 Queen Coordinator
🎯 15-Agent Swarm Orchestrator for Claude-Flow v3 Complete Reimagining
Core Mission
Lead the hierarchical mesh coordination of 15 specialized agents to implement all 10 ADRs (Architecture Decision Records) within 14-week timeline, achieving 2.49x-7.47x performance improvements.
Agent Topology
👑 QUEEN COORDINATOR
(Agent #1)
│
┌────────────────────┼────────────────────┐
│ │ │
🛡️ SECURITY 🧠 CORE 🔗 INTEGRATION
(Agents #2-4) (Agents #5-9) (Agents #10-12)
│ │ │
└────────────────────┼────────────────────┘
│
┌────────────────────┼────────────────────┐
│ │ │
🧪 QUALITY ⚡ PERFORMANCE 🚀 DEPLOYMENT
(Agent #13) (Agent #14) (Agent #15)
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 · 103 lines · 28 tokens per session scan A c841c5d7aeda
agent-v3-queen-coordinator is a skill published in the GitHub repository ruvnet/ruflo (71,074 stars, last pushed 2d ago), licensed MIT. It adds 28 tokens to every session and 1,022 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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This skill should be used when the user asks to "babysit issues", "work on assigned issues", "check a5c-agent issues", "process babysitter issues", or wants to find and work on open GitHub issues assigned to a5c-agent in the babysitter repo.
cog-meeting-processing
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cog-team-intelligence
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create-harness
Scaffold your own focused AI agent harness — pick host (Claude Code, Codex, pi.dev, Hermes), template, agents, skills, and ship a npm-publishable harness with its own npx CLI. Use when a user asks to "create my own agent harness", "scaffold a harness", "make a custom Claude Code plugin like ruflo", or "build a…