V3 Deep Integration

V3 Deep Integration is a skill for Claude Code from ruvnet/RuView. It costs 39 tokens per session (1,711 once invoked), scanned A, original, MIT.

An integration guide for making claude-flow a specialized extension of agentic-flow instead of maintaining a separate parallel implementation.

In plain words
What is it for?
Use it to design an adapter layer and connect learning, attention, database coordination, swarm, agent, and task features.
Why use it?
It helps remove duplicated code and keep shared agent, task, session, and coordination capabilities in one underlying system.

Skill for Claude Code

Written for Claude Code: installed under .claude/.

Good fit Use it to design an adapter layer and connect learning, attention, database coordination, swarm, agent, and task features.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/ruvnet/ruview/v3-integration-deep
About the project

RuView is a WiFi sensing platform that uses disturbances in radio signals, captured by low-cost ESP32 sensors, to detect presence, movement, breathing, and heart rate without cameras or wearables. It is intended for spatial monitoring and smart-home integrations. The catalogue add-ons support workflows for operating and integrating RuView.

ruvnet/RuView · 99,196 stars · on GitHub · cognitum.one

Install

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.

Any agent
npx skills add ruvnet/RuView --skill v3-integration-deep
Clone the repo
git clone --depth 1 https://github.com/ruvnet/RuView

Made for: Claude Code.

Wrote 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.

agentmods badge for V3 Deep Integration

README.md
[![agentmods](https://agentmods.dev/badge/skills/ruvnet/ruview/v3-integration-deep/github.svg)](https://agentmods.dev/skills/ruvnet/ruview/v3-integration-deep)
Your own site
<a href="https://agentmods.dev/skills/ruvnet/ruview/v3-integration-deep"><img src="https://agentmods.dev/badge/skills/ruvnet/ruview/v3-integration-deep/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.

agentmods 80×15 button for V3 Deep Integration

Your own site · 80×15
<a href="https://agentmods.dev/skills/ruvnet/ruview/v3-integration-deep"><img src="https://agentmods.dev/badge/skills/ruvnet/ruview/v3-integration-deep.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 39 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,711 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00039 $0.01711
Opus 5.5 $0.00016 $0.00684
Sonnet 5.5 $0.00008 $0.00342
Haiku 4.5 $0.00004 $0.00171

Measured 1mo ago against content hash 4e835f83bae3, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

V3 Deep Integration 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 1mo 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.

Origin

Copies of this mod

8 near-identical copies found in the catalogue:

.claude/skills/v3-integration-deep/SKILL.md · 241 lines

How it starts

The opening of the file, as written. The whole thing — 241 lines — stays where its author put it; the contents beside it link to each section on GitHub.

V3 Deep Integration

What This Skill Does

Transforms claude-flow from parallel implementation to specialized extension of agentic-flow@alpha, eliminating massive code duplication while achieving performance improvements and feature parity.

Quick Start

# Initialize deep integration
Task("Integration architecture", "Design agentic-flow@alpha adapter layer", "v3-integration-architect")

# Feature integration (parallel)
Task("SONA integration", "Integrate 5 SONA learning modes", "v3-integration-architect")
Task("Flash Attention", "Implement 2.49x-7.47x speedup", "v3-integration-architect")
Task("AgentDB coordination", "Setup 150x-12,500x search", "v3-integration-architect")

Code Deduplication Strategy

Current Overlap → Integration

┌─────────────────────────────────────────┐
│  claude-flow          agentic-flow      │
├─────────────────────────────────────────┤
│ SwarmCoordinator  →   Swarm System      │ 80% overlap (eliminate)
│ AgentManager      →   Agent Lifecycle   │ 70% overlap (eliminate)
│ TaskScheduler     →   Task Execution    │ 60% overlap (eliminate)
│ SessionManager    →   Session Mgmt      │ 50% overlap (eliminate)
└─────────────────────────────────────────┘

TARGET: <5,000 lines (vs 15,000+ currently)

agentic-flow@alpha Feature Integration

SONA Learning Modes

class SONAIntegration {
  async initializeMode(mode: SONAMode): Promise<void> {
    switch(mode) {
      case 'real-time':   // ~0.05ms adaptation
      case 'balanced':    // general purpose
      case 'research':    // deep exploration
      case 'edge':        // resource-constrained
      case 'batch':       // high-throughput
    }
    await this.agenticFlow.sona.setMode(mode);
  }
}

Flash Attention Integration

class FlashAttentionIntegration {
  async optimizeAttention(): Promise<AttentionResult> {
    return this.agenticFlow.attention.flashAttention({
      speedupTarget: '2.49x-7.47x',
      memoryReduction: '50-75%',
      mechanisms: ['multi-head', 'linear', 'local', 'global']
    });
  }
}

Read the full file on GitHub · 241 lines

Changes

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.

  1. 1mo ago First seen · 241 lines · 39 tokens per session scan A 4e835f83bae3

Subscribe to this mod's changes

V3 Deep Integration is a skill published in the GitHub repository ruvnet/RuView (99,196 stars, last pushed today), licensed MIT. It adds 39 tokens to every session and 1,711 once invoked, about $0.0002 per session on Opus 5.5. 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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