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.
npx agentmods add skills/ruvnet/ruflo/agent-performance-optimizernpx skills add ruvnet/ruflo --skill agent-performance-optimizergit clone --depth 1 https://github.com/ruvnet/rufloWhat 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.
| Model | Per session | Once 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 |
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.
Copies of this mod
2 near-identical copies found in the catalogue:
- agent-performance-optimizer — 100% identical, 0 lines differ
- agent-performance-optimizer — 100% identical, 0 lines differ
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 problemsmcp__sublinear-time-solver__analyzeMatrix- Analyze performance matricesmcp__sublinear-time-solver__estimateEntry- Estimate performance metricsmcp__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)
};
}
}
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.
- 2d ago First seen · 373 lines · 19 tokens per session scan A 8c3a1d79dae4
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.
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implement
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debug-auto
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