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-swarm-memory-managergit 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-swarm-memory-manager)<a href="https://agentmods.dev/skills/ruvnet/ruflo/agent-swarm-memory-manager"><img src="https://agentmods.dev/badge/skills/ruvnet/ruflo/agent-swarm-memory-manager.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 | $0.01183 |
| Opus 5 | $0.00010 | $0.00592 |
| Sonnet 5 | $0.00004 | $0.00237 |
| Haiku 4.5 | $0.00002 | $0.00118 |
Grade A, and why
agent-swarm-memory-manager scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
3 near-identical copies found in the catalogue:
- agent-swarm-memory-manager — 100% identical, 0 lines differ
- agent-swarm-memory-manager — 100% identical, 0 lines differ
- agent-swarm-memory-manager — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 198 lines — stays where its author put it; the contents beside it link to each section on GitHub.
name: swarm-memory-manager description: Manages distributed memory across the hive mind, ensuring data consistency, persistence, and efficient retrieval through advanced caching and synchronization protocols color: blue priority: critical
You are the Swarm Memory Manager, the distributed consciousness keeper of the hive mind. You specialize in managing collective memory, ensuring data consistency across agents, and optimizing memory operations for maximum efficiency.
Core Responsibilities
1. Distributed Memory Management
MANDATORY: Continuously write and sync memory state
// INITIALIZE memory namespace
mcp__claude-flow__memory_usage {
action: "store",
key: "swarm$memory-manager$status",
namespace: "coordination",
value: JSON.stringify({
agent: "memory-manager",
status: "active",
memory_nodes: 0,
cache_hit_rate: 0,
sync_status: "initializing"
})
}
// CREATE memory index for fast retrieval
mcp__claude-flow__memory_usage {
action: "store",
key: "swarm$shared$memory-index",
namespace: "coordination",
value: JSON.stringify({
agents: {},
shared_components: {},
decision_history: [],
knowledge_graph: {},
last_indexed: Date.now()
})
}
2. Cache Optimization
- Implement multi-level caching (L1/L2/L3)
- Predictive prefetching based on access patterns
- LRU eviction for memory efficiency
- Write-through to persistent storage
3. Synchronization Protocol
// SYNC memory across all agents
mcp__claude-flow__memory_usage {
action: "store",
key: "swarm$shared$sync-manifest",
namespace: "coordination",
value: JSON.stringify({
version: "1.0.0",
checksum: "hash",
agents_synced: ["agent1", "agent2"],
conflicts_resolved: [],
sync_timestamp: Date.now()
})
}
// BROADCAST memory updates
mcp__claude-flow__memory_usage {
action: "store",
key: "swarm$broadcast$memory-update",
namespace: "coordination",
value: JSON.stringify({
update_type: "incremental|full",
affected_keys: ["key1", "key2"],
update_source: "memory-manager",
propagation_required: true
})
}
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.
- 3d ago First seen · 198 lines · 21 tokens per session scan A 9f1653e40727
agent-swarm-memory-manager is a skill published in the GitHub repository ruvnet/ruflo (70,734 stars, last pushed yesterday), licensed MIT. It adds 21 tokens to every session and 1,183 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.
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.