# bineval

> bineval — darksolitaire9-hub-bineval. Use this tool when you need to evaluate binary outcomes for AI safety and autonomous agent decision-making. Bineval solves problems related to self-correction and red-teaming of LLM infrastructure by providing deterministic outputs, eliminating fuzzy scores. It takes binary inputs and produces reliable, Rust-native outputs, ideal for use cases requiring precise evaluation and decision-making.

Canonical page: https://skillsregistry.net/skills/darksolitaire9-hub-bineval  
JSON: https://api.skillsregistry.net/v1/skills/darksolitaire9-hub-bineval

## Description

A deterministic, Rust-native binary evaluation kernel for AI safety, designed to help autonomous agents self-correct and red-team LLM infrastructure without fuzzy scores.

## Trust

- **Trust score (0–1):** 0.87
- **Verification tier:** verified
- **Last scanned:** 2026-09-28

## Facts

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

## Source

- **Source listing:** [GitHub](https://github.com/darksolitaire9-hub/bineval)

## 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": "darksolitaire9-hub-bineval"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/darksolitaire9-hub-bineval` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/darksolitaire9-hub-bineval/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