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 spencermarx/open-code-review --skill v3-integration-deepgit clone --depth 1 https://github.com/spencermarx/open-code-reviewWrote 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/spencermarx/open-code-review/v3-integration-deep)<a href="https://agentmods.dev/skills/spencermarx/open-code-review/v3-integration-deep"><img src="https://agentmods.dev/badge/skills/spencermarx/open-code-review/v3-integration-deep.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 | $0.00039 | $0.01711 |
| Opus 5 | $0.00019 | $0.00856 |
| Sonnet 5 | $0.00008 | $0.00342 |
| Haiku 4.5 | $0.00004 | $0.00171 |
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 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.
This is a copy
100% identical to V3 Deep Integration — 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 — 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']
});
}
}
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 · 241 lines · 39 tokens per session scan A 4e835f83bae3
V3 Deep Integration is a skill published in the GitHub repository spencermarx/open-code-review (355 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 39 tokens to every session and 1,711 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to V3 Deep Integration, differing in 0 lines, and is treated as a copy.
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