V3 Performance Optimization

V3 Performance Optimization is a skill for Claude Code, Codex from Soulcynics404/AgentForge. It costs 53 tokens per session (2,435 once invoked), scanned A, a copy of V3 Performance Optimization, MIT.

A performance tuning and benchmarking suite for the third version of claude-flow. It focuses on attention processing, AgentDB search, memory use, and repeated performance measurements.

In plain words
What is it for?
Use it to establish baselines, measure search and processing speed, check memory reduction, and validate optimization targets.
Why use it?
It helps identify whether the system meets its stated speed and memory targets and where further optimization is needed.

Skill for Claude CodeCodex

Part of the claude-flow plugin — 134 skills, 52 commands, 11 agents, 4 hooks, 1 MCP server shipped together

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.

agentmods
npx agentmods add skills/soulcynics404/agentforge/v3-performance-optimization
Any agent
npx skills add Soulcynics404/AgentForge --skill v3-performance-optimization
Clone the repo
git clone --depth 1 https://github.com/Soulcynics404/AgentForge

Made for: Claude Code, Codex.

Or install claude-flow, the plugin that ships this one along with the rest of its 134 skills, 52 commands, 11 agents, 4 hooks, 1 MCP server.

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 Performance Optimization

README.md
[![agentmods](https://agentmods.dev/badge/skills/soulcynics404/agentforge/v3-performance-optimization.svg)](https://agentmods.dev/skills/soulcynics404/agentforge/v3-performance-optimization)
Your own site
<a href="https://agentmods.dev/skills/soulcynics404/agentforge/v3-performance-optimization"><img src="https://agentmods.dev/badge/skills/soulcynics404/agentforge/v3-performance-optimization.svg" alt="Measured on agentmods" height="20"></a>
Per session 53 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,435 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin 100% copy Near-identical to another mod 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 $0.00053 $0.02435
Opus 5 $0.00026 $0.01218
Sonnet 5 $0.00011 $0.00487
Haiku 4.5 $0.00005 $0.00244

Measured yesterday against content hash 25858e4c4325, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

V3 Performance Optimization 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 yesterday.

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

This is a copy

100% identical to V3 Performance Optimization — 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.

.agents/skills/v3-performance-optimization/SKILL.md · 390 lines

How it starts

The opening of the file, as written. The whole thing — 390 lines — stays where its author put it; the contents beside it link to each section on GitHub.

V3 Performance Optimization

What This Skill Does

Validates and optimizes claude-flow v3 to achieve industry-leading performance through Flash Attention, AgentDB HNSW indexing, and comprehensive system optimization with continuous benchmarking.

Quick Start

# Initialize performance optimization
Task("Performance baseline", "Establish v2 performance benchmarks", "v3-performance-engineer")

# Target validation (parallel)
Task("Flash Attention", "Validate 2.49x-7.47x speedup target", "v3-performance-engineer")
Task("Search optimization", "Validate 150x-12,500x search improvement", "v3-performance-engineer")
Task("Memory optimization", "Achieve 50-75% memory reduction", "v3-performance-engineer")

Performance Target Matrix

Flash Attention Revolution

┌─────────────────────────────────────────┐
│           FLASH ATTENTION               │
├─────────────────────────────────────────┤
│  Baseline: Standard attention           │
│  Target:   2.49x - 7.47x speedup       │
│  Memory:   50-75% reduction             │
│  Latency:  Sub-millisecond processing   │
└─────────────────────────────────────────┘

Search Performance Revolution

┌─────────────────────────────────────────┐
│            SEARCH OPTIMIZATION         │
├─────────────────────────────────────────┤
│  Current:  O(n) linear search           │
│  Target:   150x - 12,500x improvement   │
│  Method:   HNSW indexing                │
│  Latency:  <100ms for 1M+ entries       │
└─────────────────────────────────────────┘

Comprehensive Benchmark Suite

Startup Performance

class StartupBenchmarks {
  async benchmarkColdStart(): Promise<BenchmarkResult> {
    const startTime = performance.now();

    await this.initializeCLI();
    await this.initializeMCPServer();
    await this.spawnTestAgent();

    const totalTime = performance.now() - startTime;

    return {
      total: totalTime,
      target: 500, // ms
      achieved: totalTime < 500
    };
  }
}

Read the full file on GitHub · 390 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. yesterday First seen · 390 lines · 53 tokens per session scan A 25858e4c4325

Subscribe to this mod's changes

V3 Performance Optimization is a skill published in the GitHub repository Soulcynics404/AgentForge (1 stars, last pushed 11d ago), licensed MIT. It adds 53 tokens to every session and 2,435 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to V3 Performance Optimization, differing in 0 lines, and is treated as a copy.

Related

Other skills, from other repositories

building-pydantic-ai-agents

Build AI agents with Pydantic AI — tools, capabilities (including on-demand loading), structured output, streaming, testing, and multi-agent patterns. Use when the user mentions Pydantic AI, imports pydanticai, or asks to build an AI agent, add tools/capabilities, defer capability loading, stream output, define agents…

pydantic/pydantic-ai · 85 tokens

complete-partial-pr

Evaluate and complete an issue or PR where the submitted patch fixes only a narrow symptom of the reported pain point. Use when a contribution may miss adjacent integration surfaces, provider/spec semantics, roundtrip behavior, tests, docs, or historical maintainer decisions.

pydantic/pydantic-ai · 55 tokens

testing-skill

Record, rewrite, and debug VCR cassettes for HTTP recordings. Use when running tests with --record-mode, verifying cassette playback, or inspecting request/response bodies in YAML cassettes.

pydantic/pydantic-ai · 44 tokens

adding-a-provider-api-feature

Add a new provider API capability (prompt caching, strict/structured tool calling, thinking/reasoning effort, service tier, safety settings, logprobs, etc.) to Pydantic AI. Use when wiring a provider feature through the library — it enforces reasoning from the existing cross-provider abstraction before designing…

pydantic/pydantic-ai · 97 tokens

migrating-langchain-to-pydantic-ai

Migrate Python LangChain or LangGraph applications to Pydantic AI. Use for LangChain agents, chains, LCEL, or direct LangGraph graphs, persistence, interrupts, and streaming. Do not use for migrations centered on createdeepagent or Deep Agents harness features.

pydantic/pydantic-ai · 69 tokens

address-feedback

Find and address unresolved PR review comments for the current branch, then continue the canonical push, reply, reaction, and resolution workflow.

pydantic/pydantic-ai · 29 tokens