# code-reputation

> code-reputation — ryx2-code-reputation. Use this tool when you need to optimize AI model performance by caching semantically similar code snippets, reducing redundant computations and improving response times. It solves problems of inefficient code execution, repeated calculations, and slow model inference, making it ideal for applications with large codebases or high traffic. The tool takes in code inputs and outputs cached results, suitable for use cases where speed and efficiency are critical.

Canonical page: https://skillsregistry.net/skills/ryx2-code-reputation  
JSON: https://api.skillsregistry.net/v1/skills/ryx2-code-reputation

## Description

Semantic code caching for AI agents.

## Trust

- **Trust score (0–1):** 0.30
- **Verification tier:** unverified
- **Last scanned:** 2026-08-24

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** instructions
- **Runtime environment:** llm
- **Category:** ai-ml
- **Updated:** 2026-09-13

## Source

- **Source listing:** [ClawHub](https://clawskills.sh/skills/ryx2-code-reputation)

## 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": "ryx2-code-reputation"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/ryx2-code-reputation` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/ryx2-code-reputation/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