# llm-eval-search

> llm-eval-search — fvahedian-llm-eval-search. Use this tool when you need to evaluate the performance of large language models (LLMs) using a search-based approach, solving problems related to model benchmarking and comparison. It takes in model configurations and evaluation metrics as inputs and outputs performance scores and rankings. Ideal for use cases where accurate model assessment is crucial, such as in natural language processing and machine learning applications.

Canonical page: https://skillsregistry.net/skills/fvahedian-llm-eval-search  
JSON: https://api.skillsregistry.net/v1/skills/fvahedian-llm-eval-search

## Trust

- **Trust score (0–1):** 0.75
- **Verification tier:** verified
- **Last scanned:** 2026-09-28

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** container
- **Runtime environment:** vm
- **Updated:** 2026-09-28

## Source

- **Source listing:** [GitHub](https://github.com/fvahedian/llm-eval-search)

## 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": "fvahedian-llm-eval-search"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/fvahedian-llm-eval-search` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/fvahedian-llm-eval-search/pull`

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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