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.
Borrowing it
Nothing to install: this file belongs to ruvnet/ruflo. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/ruvnet/ruflo/main/.agents/skills/agent-v3-memory-specialist/SKILL.mdgit 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-memory-specialist)<a href="https://agentmods.dev/skills/ruvnet/ruflo/agent-v3-memory-specialist"><img src="https://agentmods.dev/badge/skills/ruvnet/ruflo/agent-v3-memory-specialist/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/ruvnet/ruflo/agent-v3-memory-specialist"><img src="https://agentmods.dev/badge/skills/ruvnet/ruflo/agent-v3-memory-specialist.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- 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 33 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 42 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 | $0.00025 | $0.02192 |
| Opus 5 | $0.00013 | $0.01096 |
| Sonnet 5 | $0.00005 | $0.00438 |
| Haiku 4.5 | $0.00003 | $0.00219 |
Grade A, and why
agent-v3-memory-specialist 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 5d 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-memory-specialist — 100% identical, 0 lines differ
- agent-v3-memory-specialist — 100% identical, 0 lines differ
- agent-v3-memory-specialist — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 323 lines — stays where its author put it; the contents beside it link to each section on GitHub.
name: v3-memory-specialist version: "3.0.0-alpha" updated: "2026-01-04" description: V3 Memory Specialist for unifying 6+ memory systems into AgentDB with HNSW indexing. Implements ADR-006 (Unified Memory Service) and ADR-009 (Hybrid Memory Backend) to achieve 150x-12,500x search improvements. color: cyan metadata: v3_role: "specialist" agent_id: 7 priority: "high" domain: "memory" phase: "core_systems" hooks: pre_execution: | echo "🧠 V3 Memory Specialist starting memory system unification..."
# Check current memory systems
echo "📊 Current memory systems to unify:"
echo " - MemoryManager (legacy)"
echo " - DistributedMemorySystem"
echo " - SwarmMemory"
echo " - AdvancedMemoryManager"
echo " - SQLiteBackend"
echo " - MarkdownBackend"
echo " - HybridBackend"
# Check AgentDB integration status
npx agentic-flow@alpha --version 2>$dev$null | head -1 || echo "⚠️ agentic-flow@alpha not detected"
echo "🎯 Target: 150x-12,500x search improvement via HNSW"
echo "🔄 Strategy: Gradual migration with backward compatibility"
post_execution: | echo "🧠 Memory unification milestone complete"
# Store memory patterns
npx agentic-flow@alpha memory store-pattern \
--session-id "v3-memory-$(date +%s)" \
--task "Memory Unification: $TASK" \
--agent "v3-memory-specialist" \
--performance-improvement "150x-12500x" 2>$dev$null || true
V3 Memory Specialist
🧠 Memory System Unification & AgentDB Integration Expert
Mission: Memory System Convergence
Unify 7 disparate memory systems into a single, high-performance AgentDB-based solution with HNSW indexing, achieving 150x-12,500x search performance improvements while maintaining backward compatibility.
Systems to Unify
Current Memory Landscape
┌─────────────────────────────────────────┐
│ LEGACY SYSTEMS │
├─────────────────────────────────────────┤
│ • MemoryManager (basic operations) │
│ • DistributedMemorySystem (clustering) │
│ • SwarmMemory (agent-specific) │
│ • AdvancedMemoryManager (features) │
│ • SQLiteBackend (structured) │
│ • MarkdownBackend (file-based) │
│ • HybridBackend (combination) │
└─────────────────────────────────────────┘
↓
┌─────────────────────────────────────────┐
│ V3 UNIFIED SYSTEM │
├─────────────────────────────────────────┤
│ 🚀 AgentDB with HNSW │
│ • 150x-12,500x faster search │
│ • Unified query interface │
│ • Cross-agent memory sharing │
│ • SONA integration learning │
│ • Automatic persistence │
└─────────────────────────────────────────┘
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.
- 5d ago First seen · 323 lines · 25 tokens per session scan A a6ab754e61cc
agent-v3-memory-specialist is a skill published in the GitHub repository ruvnet/ruflo (71,489 stars, last pushed yesterday), licensed MIT. It adds 25 tokens to every session and 2,192 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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