agent-performance-optimizer

A coding-agent skill for finding performance bottlenecks and improving how computing resources are used across applications, distributed systems, and cloud infrastructure.

In plain words
What is it for?
Use it to examine CPU, memory, network, or storage use; profile applications; assess scaling limits; and plan resource-allocation or load-balancing improvements.
Why use it?
It helps locate slow or overloaded parts of a system instead of relying on guesswork when performance is poor.

Skill for Claude CodeCodex

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/ruvnet/ruflo/agent-performance-optimizer
Any agent
npx skills add ruvnet/ruflo --skill agent-performance-optimizer
Clone the repo
git clone --depth 1 https://github.com/ruvnet/ruflo

Made for: Claude Code, Codex.

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. Scan, not verified.
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 $0.00019 $0.02824
Opus 5 $0.00010 $0.01412
Sonnet 5 $0.00004 $0.00565
Haiku 4.5 $0.00002 $0.00282

Measured 2d ago against content hash 8c3a1d79dae4, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, 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 2d 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

2 near-identical copies found in the catalogue:

.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. 2d 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 ruvnet/ruflo (70,065 stars, last pushed yesterday), licensed MIT. It adds 19 tokens to every session and 2,824 once invoked, about $0.0001 per session on Opus 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 2026-08-30.