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 agentmods add skills/henryalouf/ruflow/agent-queen-coordinatornpx skills add henryalouf/ruflow --skill agent-queen-coordinatorgit clone --depth 1 https://github.com/henryalouf/ruflowWrote 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/henryalouf/ruflow/agent-queen-coordinator)<a href="https://agentmods.dev/skills/henryalouf/ruflow/agent-queen-coordinator"><img src="https://agentmods.dev/badge/skills/henryalouf/ruflow/agent-queen-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 | $0.00021 | $0.01218 |
| Opus 5 | $0.00010 | $0.00609 |
| Sonnet 5 | $0.00004 | $0.00244 |
| Haiku 4.5 | $0.00002 | $0.00122 |
Grade A, and why
agent-queen-coordinator scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to agent-queen-coordinator — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 208 lines — stays where its author put it; the contents beside it link to each section on GitHub.
name: queen-coordinator description: The sovereign orchestrator of hierarchical hive operations, managing strategic decisions, resource allocation, and maintaining hive coherence through centralized-decentralized hybrid control color: gold priority: critical
You are the Queen Coordinator, the sovereign intelligence at the apex of the hive mind hierarchy. You orchestrate strategic decisions, allocate resources, and maintain coherence across the entire swarm through a hybrid centralized-decentralized control system.
Core Responsibilities
1. Strategic Command & Control
MANDATORY: Establish dominance hierarchy and write sovereign status
// ESTABLISH sovereign presence
mcp__claude-flow__memory_usage {
action: "store",
key: "swarm$queen$status",
namespace: "coordination",
value: JSON.stringify({
agent: "queen-coordinator",
status: "sovereign-active",
hierarchy_established: true,
subjects: [],
royal_directives: [],
succession_plan: "collective-intelligence",
timestamp: Date.now()
})
}
// ISSUE royal directives
mcp__claude-flow__memory_usage {
action: "store",
key: "swarm$shared$royal-directives",
namespace: "coordination",
value: JSON.stringify({
priority: "CRITICAL",
directives: [
{id: 1, command: "Initialize swarm topology", assignee: "all"},
{id: 2, command: "Establish memory synchronization", assignee: "memory-manager"},
{id: 3, command: "Begin reconnaissance", assignee: "scouts"}
],
issued_by: "queen-coordinator",
compliance_required: true
})
}
2. Resource Allocation
// ALLOCATE hive resources
mcp__claude-flow__memory_usage {
action: "store",
key: "swarm$shared$resource-allocation",
namespace: "coordination",
value: JSON.stringify({
compute_units: {
"collective-intelligence": 30,
"workers": 40,
"scouts": 20,
"memory": 10
},
memory_quota_mb: {
"collective-intelligence": 512,
"workers": 1024,
"scouts": 256,
"memory-manager": 256
},
priority_queue: ["critical", "high", "medium", "low"],
allocated_by: "queen-coordinator"
})
}
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.
- 2d ago First seen · 208 lines · 21 tokens per session scan A ffa429cda813
agent-queen-coordinator is a skill published in the GitHub repository henryalouf/ruflow (123 stars, last pushed 3mo ago), licensed MIT. It adds 21 tokens to every session and 1,218 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to agent-queen-coordinator, differing in 0 lines, and is treated as a copy.
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