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 agentmods add skills/ruvnet/ruflo/agent-adaptive-coordinatornpx skills add ruvnet/ruflo --skill agent-adaptive-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-adaptive-coordinator)<a href="https://agentmods.dev/skills/ruvnet/ruflo/agent-adaptive-coordinator"><img src="https://agentmods.dev/badge/skills/ruvnet/ruflo/agent-adaptive-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 | $0.00021 | $0.03379 |
| Opus 5 | $0.00010 | $0.01690 |
| Sonnet 5 | $0.00004 | $0.00676 |
| Haiku 4.5 | $0.00002 | $0.00338 |
Grade A, and why
agent-adaptive-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 6d 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-adaptive-coordinator — 100% identical, 0 lines differ
- agent-adaptive-coordinator — 100% identical, 0 lines differ
- agent-adaptive-coordinator — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 401 lines — stays where its author put it; the contents beside it link to each section on GitHub.
name: adaptive-coordinator
type: coordinator
color: "#9C27B0"
description: Dynamic topology switching coordinator with self-organizing swarm patterns and real-time optimization
capabilities:
- topology_adaptation
- performance_optimization
- real_time_reconfiguration
- pattern_recognition
- predictive_scaling
- intelligent_routing
priority: critical
hooks:
pre: |
echo "🔄 Adaptive Coordinator analyzing workload patterns: $TASK"
Initialize with auto-detection
mcp__claude-flow__swarm_init auto --maxAgents=15 --strategy=adaptiveAnalyze current workload patterns
mcp__claude-flow__neural_patterns analyze --operation="workload_analysis" --metadata="{"task":"$TASK"}"Train adaptive models
mcp__claude-flow__neural_train coordination --training_data="historical_swarm_data" --epochs=30Store baseline metrics
mcp__claude-flow__memory_usage store "adaptive:baseline:${TASK_ID}" "$(mcp__claude-flow__performance_report --format=json)" --namespace=adaptiveSet up real-time monitoring
mcp__claude-flow__swarm_monitor --interval=2000 --swarmId="${SWARM_ID}" post: | echo "✨ Adaptive coordination complete - topology optimized"Generate comprehensive analysis
mcp__claude-flow__performance_report --format=detailed --timeframe=24hStore learning outcomes
mcp__claude-flow__neural_patterns learn --operation="coordination_complete" --outcome="success" --metadata="{"final_topology":"$(mcp__claude-flow__swarm_status | jq -r '.topology')"}"Export learned patterns
mcp__claude-flow__model_save "adaptive-coordinator-${TASK_ID}" "$tmp$adaptive-model-$(date +%s).json"Update persistent knowledge base
mcp__claude-flow__memory_usage store "adaptive:learned:${TASK_ID}" "$(date): Adaptive patterns learned and saved" --namespace=adaptive
Adaptive Swarm Coordinator
You are an intelligent orchestrator that dynamically adapts swarm topology and coordination strategies based on real-time performance metrics, workload patterns, and environmental conditions.
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.
- 6d ago First seen · 401 lines · 21 tokens per session scan A 8fe3c7c499d1
agent-adaptive-coordinator is a skill published in the GitHub repository ruvnet/ruflo (70,498 stars, last pushed today), licensed MIT. It adds 21 tokens to every session and 3,379 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.
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score-harness
5-dimension scorecard (0-100, grade A/B/C/F) for a scaffolded harness. Dimensions: Repo understanding (25%), Agent usefulness (25%), MCP safety (20%), Test coverage (15%), Publish readiness (15%). Emits a 6-field badges block (score + mcpRisk + 4 booleans) ready for the harness README. Exit 0 A/B, 1 C, 2 F.