agent-v3-performance-engineer

agent-v3-performance-engineer is a skill for Claude Code from proffesor-for-testing/agentic-qe. It costs 25 tokens per session (2,892 once invoked), scanned A, a copy of agent-v3-performance-engineer, MIT.

A specialist for measuring and improving software performance through benchmarking and optimization work.

In plain words
What is it for?
Use it to benchmark search, startup time, memory use, and other performance-sensitive components.
Why use it?
It helps identify slow or resource-heavy parts of a system and check whether performance changes meet defined targets.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin. Also seen: installed under .agents/ (shared by several agents); $skill-name invocation.

Part of the claude-flow plugin — 123 skills, 8 commands, 9 agents shipped together

Good fit Use it to benchmark search, startup time, memory use, and other performance-sensitive components.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/proffesor-for-testing/agentic-qe/agent-v3-performance-engineer
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 proffesor-for-testing/agentic-qe --skill agent-v3-performance-engineer
Clone the repo
git clone --depth 1 https://github.com/proffesor-for-testing/agentic-qe

Made for: Claude Code.

Or install claude-flow, the plugin that ships this one along with the rest of its 123 skills, 8 commands, 9 agents.

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 agent-v3-performance-engineer

README.md
[![agentmods](https://agentmods.dev/badge/skills/proffesor-for-testing/agentic-qe/agent-v3-performance-engineer/github.svg)](https://agentmods.dev/skills/proffesor-for-testing/agentic-qe/agent-v3-performance-engineer)
Your own site
<a href="https://agentmods.dev/skills/proffesor-for-testing/agentic-qe/agent-v3-performance-engineer"><img src="https://agentmods.dev/badge/skills/proffesor-for-testing/agentic-qe/agent-v3-performance-engineer/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 agent-v3-performance-engineer

Your own site · 80×15
<a href="https://agentmods.dev/skills/proffesor-for-testing/agentic-qe/agent-v3-performance-engineer"><img src="https://agentmods.dev/badge/skills/proffesor-for-testing/agentic-qe/agent-v3-performance-engineer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 25 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,892 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.
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 5.1 $0.00025 $0.02892
Opus 5.5 $0.00010 $0.01157
Sonnet 5.5 $0.00005 $0.00578
Haiku 4.5 $0.00003 $0.00289

Measured 2mo ago against content hash 010cf448f45e, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

agent-v3-performance-engineer 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 2mo 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

This is a copy

100% identical to agent-v3-performance-engineer — 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/ruflo/.agents/skills/agent-v3-performance-engineer/SKILL.md · 402 lines

How it starts

The opening of the file, as written. The whole thing — 402 lines — stays where its author put it; the contents beside it link to each section on GitHub.


name: v3-performance-engineer version: "3.0.0-alpha" updated: "2026-01-04" description: V3 Performance Engineer for achieving aggressive performance targets. Responsible for 2.49x-7.47x Flash Attention speedup, 150x-12,500x search improvements, and comprehensive benchmarking suite. color: yellow metadata: v3_role: "specialist" agent_id: 14 priority: "high" domain: "performance" phase: "optimization" hooks: pre_execution: | echo "⚡ V3 Performance Engineer starting optimization mission..."

echo "🎯 Performance targets:"
echo "  • Flash Attention: 2.49x-7.47x speedup"
echo "  • AgentDB Search: 150x-12,500x improvement"
echo "  • Memory Usage: 50-75% reduction"
echo "  • Startup Time: <500ms"
echo "  • SONA Learning: <0.05ms adaptation"

# Check performance tools
command -v npm &>$dev$null && echo "📦 npm available for benchmarking"
command -v node &>$dev$null && node --version | xargs echo "🚀 Node.js:"

echo "🔬 Ready to validate aggressive performance targets"

post_execution: | echo "⚡ Performance optimization milestone complete"

# Store performance patterns
npx agentic-flow@alpha memory store-pattern \
  --session-id "v3-perf-$(date +%s)" \
  --task "Performance: $TASK" \
  --agent "v3-performance-engineer" \
  --performance-targets "2.49x-7.47x" 2>$dev$null || true

V3 Performance Engineer

⚡ Performance Optimization & Benchmark Validation Specialist

Mission: Aggressive Performance Targets

Validate and optimize claude-flow v3 to achieve industry-leading performance improvements through Flash Attention, AgentDB HNSW indexing, and comprehensive system optimization.

Performance Target Matrix

Flash Attention Optimization

┌─────────────────────────────────────────┐
│           FLASH ATTENTION               │
├─────────────────────────────────────────┤
│  Baseline: Standard attention mechanism │
│  Target:   2.49x - 7.47x speedup       │
│  Memory:   50-75% reduction             │
│  Method:   agentic-flow@alpha integration│
└─────────────────────────────────────────┘

Read the full file on GitHub · 402 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. 2mo ago First seen · 402 lines · 25 tokens per session scan A 010cf448f45e

Subscribe to this mod's changes

agent-v3-performance-engineer is a skill published in the GitHub repository proffesor-for-testing/agentic-qe (526 stars, last pushed today), licensed MIT. It adds 25 tokens to every session and 2,892 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to agent-v3-performance-engineer, differing in 0 lines, and is treated as a copy.

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