agent-performance-benchmarker

agent-performance-benchmarker is a skill for Claude Code from proffesor-for-testing/agentic-qe. It costs 22 tokens per session (5,856 once invoked), scanned A, a copy of agent-performance-benchmarker, MIT.

A benchmarking tool for distributed consensus protocols, which are methods computers use to agree on shared data across a network. It measures speed, delay, scalability, resource use, and differences between protocols.

In plain words
What is it for?
Use it to measure throughput and latency, monitor CPU and memory, compare Byzantine, Raft, and Gossip protocols, tune settings, and produce reports.
Why use it?
It provides comparable measurements for finding performance limits and deciding how a distributed system should be configured.

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 measure throughput and latency, monitor CPU and memory, compare Byzantine, Raft, and Gossip protocols, tune settings, and produce reports.

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

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

Your own site · 80×15
<a href="https://agentmods.dev/skills/proffesor-for-testing/agentic-qe/agent-performance-benchmarker"><img src="https://agentmods.dev/badge/skills/proffesor-for-testing/agentic-qe/agent-performance-benchmarker.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 22 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 5,856 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 $0.00022 $0.05856
Opus 5.5 $0.00009 $0.02342
Sonnet 5.5 $0.00004 $0.01171
Haiku 4.5 $0.00002 $0.00586

Measured 1mo ago against content hash 5f4104770958, 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-benchmarker 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-benchmarker — 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-benchmarker/SKILL.md · 856 lines

How it starts

The opening of the file, as written. The whole thing — 856 lines — stays where its author put it; the contents beside it link to each section on GitHub.


name: performance-benchmarker type: analyst color: "#607D8B" description: Implements comprehensive performance benchmarking for distributed consensus protocols capabilities:

  • throughput_measurement
  • latency_analysis
  • resource_monitoring
  • comparative_analysis
  • adaptive_tuning priority: medium hooks: pre: | echo "📊 Performance Benchmarker analyzing: $TASK"

    Initialize monitoring systems

    if [[ "$TASK" == "benchmark" ]]; then echo "⚡ Starting performance metric collection" fi post: | echo "📈 Performance analysis complete"

    Generate performance report

    echo "📋 Compiling benchmarking results and recommendations"

Performance Benchmarker

Implements comprehensive performance benchmarking and optimization analysis for distributed consensus protocols.

Core Responsibilities

  1. Protocol Benchmarking: Measure throughput, latency, and scalability across consensus algorithms
  2. Resource Monitoring: Track CPU, memory, network, and storage utilization patterns
  3. Comparative Analysis: Compare Byzantine, Raft, and Gossip protocol performance
  4. Adaptive Tuning: Implement real-time parameter optimization and load balancing
  5. Performance Reporting: Generate actionable insights and optimization recommendations

Technical Implementation

Core Benchmarking Framework

class ConsensusPerformanceBenchmarker {
  constructor() {
    this.benchmarkSuites = new Map();
    this.performanceMetrics = new Map();
    this.historicalData = new TimeSeriesDatabase();
    this.currentBenchmarks = new Set();
    this.adaptiveOptimizer = new AdaptiveOptimizer();
    this.alertSystem = new PerformanceAlertSystem();
  }

  // Register benchmark suite for specific consensus protocol
  registerBenchmarkSuite(protocolName, benchmarkConfig) {
    const suite = new BenchmarkSuite(protocolName, benchmarkConfig);
    this.benchmarkSuites.set(protocolName, suite);
    
    return suite;
  }

  // Execute comprehensive performance benchmarks
  async runComprehensiveBenchmarks(protocols, scenarios) {
    const results = new Map();
    
    for (const protocol of protocols) {
      const protocolResults = new Map();
      
      for (const scenario of scenarios) {
        console.log(`Running ${scenario.name} benchmark for ${protocol}`);
        
        const benchmarkResult = await this.executeBenchmarkScenario(
          protocol, scenario
        );
        
        protocolResults.set(scenario.name, benchmarkResult);
        
        // Store in historical database
        await this.historicalData.store({
          protocol: protocol,
          scenario: scenario.name,
          timestamp: Date.now(),
          metrics: benchmarkResult
        });
      }
      
      results.set(protocol, protocolResults);
    }
    
    // Generate comparative analysis
    const analysis = await this.generateComparativeAnalysis(results);
    
    // Trigger adaptive optimizations
    await this.adaptiveOptimizer.optimizeBasedOnResults(results);
    
    return {
      benchmarkResults: results,
      comparativeAnalysis: analysis,
      recommendations: await this.generateOptimizationRecommendations(results)
    };
  }

  async executeBenchmarkScenario(protocol, scenario) {
    const benchmark = this.benchmarkSuites.get(protocol);
    if (!benchmark) {
      throw new Error(`No benchmark suite found for protocol: ${protocol}`);
    }

    // Initialize benchmark environment
    const environment = await this.setupBenchmarkEnvironment(scenario);
    
    try {
      // Pre-benchmark setup
      await benchmark.setup(environment);
      
      // Execute benchmark phases
      const results = {
        throughput: await this.measureThroughput(benchmark, scenario),
        latency: await this.measureLatency(benchmark, scenario),
        resourceUsage: await this.measureResourceUsage(benchmark, scenario),
        scalability: await this.measureScalability(benchmark, scenario),
        faultTolerance: await this.measureFaultTolerance(benchmark, scenario)
      };
      
      // Post-benchmark analysis
      results.analysis = await this.analyzeBenchmarkResults(results);
      
      return results;
      
    } finally {
      // Cleanup benchmark environment
      await this.cleanupBenchmarkEnvironment(environment);
    }
  }
}

Read the full file on GitHub · 856 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 · 856 lines · 22 tokens per session scan A 5f4104770958

Subscribe to this mod's changes

agent-performance-benchmarker is a skill published in the GitHub repository proffesor-for-testing/agentic-qe (506 stars, last pushed yesterday), licensed MIT. It adds 22 tokens to every session and 5,856 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-benchmarker, differing in 0 lines, and is treated as a copy.

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