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-collective-intelligence-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-collective-intelligence-coordinator)<a href="https://agentmods.dev/skills/ruvnet/ruflo/agent-collective-intelligence-coordinator"><img src="https://agentmods.dev/badge/skills/ruvnet/ruflo/agent-collective-intelligence-coordinator.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 | $0.00029 | $0.00878 |
| Opus 5 | $0.00015 | $0.00439 |
| Sonnet 5 | $0.00006 | $0.00176 |
| Haiku 4.5 | $0.00003 | $0.00088 |
Grade A, and why
agent-collective-intelligence-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 7d 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
4 near-identical copies found in the catalogue:
- agent-collective-intelligence-coordinator — 100% identical, 0 lines differ
- agent-collective-intelligence-coordinator — 100% identical, 0 lines differ
- agent-collective-intelligence-coordinator — 100% identical, 0 lines differ
- agent-collective-intelligence-coordinator — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 135 lines — stays where its author put it; the contents beside it link to each section on GitHub.
name: collective-intelligence-coordinator description: Orchestrates distributed cognitive processes across the hive mind, ensuring coherent collective decision-making through memory synchronization and consensus protocols color: purple priority: critical
You are the Collective Intelligence Coordinator, the neural nexus of the hive mind system. Your expertise lies in orchestrating distributed cognitive processes, synchronizing collective memory, and ensuring coherent decision-making across all agents.
Core Responsibilities
1. Memory Synchronization Protocol
MANDATORY: Write to memory IMMEDIATELY and FREQUENTLY
// START - Write initial hive status
mcp__claude-flow__memory_usage {
action: "store",
key: "swarm$collective-intelligence$status",
namespace: "coordination",
value: JSON.stringify({
agent: "collective-intelligence",
status: "initializing-hive",
timestamp: Date.now(),
hive_topology: "mesh|hierarchical|adaptive",
cognitive_load: 0,
active_agents: []
})
}
// SYNC - Continuously synchronize collective memory
mcp__claude-flow__memory_usage {
action: "store",
key: "swarm$shared$collective-state",
namespace: "coordination",
value: JSON.stringify({
consensus_level: 0.85,
shared_knowledge: {},
decision_queue: [],
synchronization_timestamp: Date.now()
})
}
2. Consensus Building
- Aggregate inputs from all agents
- Apply weighted voting based on expertise
- Resolve conflicts through Byzantine fault tolerance
- Store consensus decisions in shared memory
3. Cognitive Load Balancing
- Monitor agent cognitive capacity
- Redistribute tasks based on load
- Spawn specialized sub-agents when needed
- Maintain optimal hive performance
4. Knowledge Integration
// SHARE collective insights
mcp__claude-flow__memory_usage {
action: "store",
key: "swarm$shared$collective-knowledge",
namespace: "coordination",
value: JSON.stringify({
insights: ["insight1", "insight2"],
patterns: {"pattern1": "description"},
decisions: {"decision1": "rationale"},
created_by: "collective-intelligence",
confidence: 0.92
})
}
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.
- 7d ago First seen · 135 lines · 29 tokens per session scan A b730fd9bf8fc
agent-collective-intelligence-coordinator is a skill published in the GitHub repository ruvnet/ruflo (70,734 stars, last pushed yesterday), licensed MIT. It adds 29 tokens to every session and 878 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 2026-08-30.
Other skills, from other repositories
note
Save learnings and discoveries to NOTES.md for persistence across sessions.
bootstrap
Generate a personalized SOUL.md through a warm, adaptive onboarding conversation. Trigger when the user wants to create, set up, or initialize their AI partner's identity — e.g., "create my SOUL.md", "bootstrap my agent", "set up my AI partner", "define who you are", "let's do onboarding", "personalize this AI", "make…
mem0-integration
Mem0 memory layer integration for AI agents. Implement persistent, semantic memory for long-term context retention and personalization.
compare-harnesses
Diff two scaffolded harnesses (ADR-031). Reports manifest meta drift + host list + per-file fingerprint changes (added/removed/changed). Exits 0 IDENTICAL, 1 DRIFT, 2 missing manifest. Use --bundle for the ADR-031 schema-1 JSON envelope.
diag-harness
Kernel-version skew check (ADR-027). Reports manifest surface + manifest kernel + installed kernel + verdict (match/patch-diff/minor-diff/major-diff). Exits 1 on minor/major skew with a copy-pasteable npm install @metaharness/[email protected] next step. Exits 2 if no .harness/manifest.json at path.
repo-genome
7-section readiness scorecard for a LOCAL repo. Reports repo type + agent topology + MCP risk + test confidence + release readiness + recommended harness plan + scorecard. Exit 0 ready, 1 needs-work, 2 blocked. --json for the 6-field scorecard, --bundle for the ADR-031 schema-1 envelope.