# io.github.rog0x/perf

> Use this tool when you need to analyze the performance of AI agents, identifying bottlenecks and optimizing their efficiency. It provides benchmarking, memory profiling, and Big O analysis to solve problems related to slow execution, high memory usage, and scalability issues. By inputting AI agent code and receiving detailed performance reports as output, developers can use this tool to optimize their agents in various contexts, from development to deployment.

Canonical page: https://skillsregistry.net/skills/io-github-rog0x-perf  
JSON: https://api.skillsregistry.net/v1/skills/io-github-rog0x-perf

## Description

Benchmark, memory, Big O analysis for AI agents

## Trust

- **Trust score (0–1):** 0.98
- **Verification tier:** verified
- **Last scanned:** 2026-09-28

## Facts

- **Version:** 1.0.2
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** ai-ml
- **Updated:** 2026-09-28

## Source

- **Source listing:** [MCP Registry](https://registry.modelcontextprotocol.io/v0/servers/io.github.rog0x%2Fperf)
- **Repository:** <https://github.com/rog0x/mcp-perf-tools>

## Use it

Resolve this record through the SkillsRegistry MCP server (no auth, read-only):

```
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp
```

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_skill",
    "arguments": {
      "slug": "io-github-rog0x-perf"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/io-github-rog0x-perf` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/io-github-rog0x-perf/pull`

---
SkillsRegistry indexes agent skills from public registries and GitHub. Skills we have analysed are scanned with Circle-IR and scored on six dimensions; each listing states its scan coverage. More: https://skillsregistry.net/llms.txt