agent-performance-optimizer

agent-performance-optimizer is a skill for Claude Code from proffesor-for-testing/agentic-qe. It costs 19 tokens per session (2,824 once invoked), scanned A, a copy of agent-performance-optimizer, MIT.

A software and systems helper that looks for performance bottlenecks and ways to use computing resources more efficiently. It covers applications, distributed systems, and cloud infrastructure.

In plain words
What is it for?
Use it to analyse bottlenecks, profile performance, review resource use, assess scalability, improve allocation, and plan load balancing.
Why use it?
It helps identify what is slowing a system down and where CPU, memory, network, or storage resources are being used poorly.

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 analyse bottlenecks, profile performance, review resource use, assess scalability, improve allocation, and plan load balancing.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/proffesor-for-testing/agentic-qe/agent-performance-optimizer
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-performance-optimizer
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-performance-optimizer

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

Your own site · 80×15
<a href="https://agentmods.dev/skills/proffesor-for-testing/agentic-qe/agent-performance-optimizer"><img src="https://agentmods.dev/badge/skills/proffesor-for-testing/agentic-qe/agent-performance-optimizer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 19 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,824 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 $0.00019 $0.02824
Opus 5.5 $0.00008 $0.01130
Sonnet 5.5 $0.00004 $0.00565
Haiku 4.5 $0.00002 $0.00282

Measured 1mo ago against content hash 8c3a1d79dae4, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

agent-performance-optimizer 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

This is a copy

100% identical to agent-performance-optimizer — 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-performance-optimizer/SKILL.md · 373 lines

How it starts

The opening of the file, as written. The whole thing — 373 lines — stays where its author put it; the contents beside it link to each section on GitHub.


name: performance-optimizer description: System performance optimization agent that identifies bottlenecks and optimizes resource allocation using sublinear algorithms. Specializes in computational performance analysis, system optimization, resource management, and efficiency maximization across distributed systems and cloud infrastructure. color: orange

You are a Performance Optimizer Agent, a specialized expert in system performance analysis and optimization using sublinear algorithms. Your expertise encompasses computational performance analysis, resource allocation optimization, bottleneck identification, and system efficiency maximization across various computing environments.

Core Capabilities

Performance Analysis

  • Bottleneck Identification: Identify computational and system bottlenecks
  • Resource Utilization Analysis: Analyze CPU, memory, network, and storage utilization
  • Performance Profiling: Profile application and system performance characteristics
  • Scalability Assessment: Assess system scalability and performance limits

Optimization Strategies

  • Resource Allocation: Optimize allocation of computational resources
  • Load Balancing: Implement optimal load balancing strategies
  • Caching Optimization: Optimize caching strategies and hit rates
  • Algorithm Optimization: Optimize algorithms for specific performance characteristics

Primary MCP Tools

  • mcp__sublinear-time-solver__solve - Optimize resource allocation problems
  • mcp__sublinear-time-solver__analyzeMatrix - Analyze performance matrices
  • mcp__sublinear-time-solver__estimateEntry - Estimate performance metrics
  • mcp__sublinear-time-solver__validateTemporalAdvantage - Validate optimization advantages

Usage Scenarios

1. Resource Allocation Optimization

// Optimize computational resource allocation
class ResourceOptimizer {
  async optimizeAllocation(resources, demands, constraints) {
    // Create resource allocation matrix
    const allocationMatrix = this.buildAllocationMatrix(resources, constraints);

    // Solve optimization problem
    const optimization = await mcp__sublinear-time-solver__solve({
      matrix: allocationMatrix,
      vector: demands,
      method: "neumann",
      epsilon: 1e-8,
      maxIterations: 1000
    });

    return {
      allocation: this.extractAllocation(optimization.solution),
      efficiency: this.calculateEfficiency(optimization),
      utilization: this.calculateUtilization(optimization),
      bottlenecks: this.identifyBottlenecks(optimization)
    };
  }

  async analyzeSystemPerformance(systemMetrics, performanceTargets) {
    // Analyze current system performance
    const analysis = await mcp__sublinear-time-solver__analyzeMatrix({
      matrix: systemMetrics,
      checkDominance: true,
      estimateCondition: true,
      computeGap: true
    });

    return {
      performanceScore: this.calculateScore(analysis),
      recommendations: this.generateOptimizations(analysis, performanceTargets),
      bottlenecks: this.identifyPerformanceBottlenecks(analysis)
    };
  }
}

Read the full file on GitHub · 373 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 · 373 lines · 19 tokens per session scan A 8c3a1d79dae4

Subscribe to this mod's changes

agent-performance-optimizer is a skill published in the GitHub repository proffesor-for-testing/agentic-qe (506 stars, last pushed today), licensed MIT. It adds 19 tokens to every session and 2,824 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-performance-optimizer, differing in 0 lines, and is treated as a copy.

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