# raysense

> raysense — rayforcedb-raysense. Use this tool when you need to analyze and understand the internal structure of codebases, identifying potential issues and areas for improvement. Raysense provides a comprehensive X-ray of the code, solving problems related to code quality, maintainability, and scalability. It integrates with git, taking in code repositories as input and outputting detailed structural insights to inform AI-driven coding decisions.

Canonical page: https://skillsregistry.net/skills/rayforcedb-raysense  
JSON: https://api.skillsregistry.net/v1/skills/rayforcedb-raysense

## Description

A structural X-ray for the codebases AI agents are writing.

## Trust

- **Trust score (0–1):** 0.34
- **Verification tier:** scanned
- **Last scanned:** 2026-09-28

## Facts

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

## Source

- **Source listing:** [GitHub](https://github.com/RayforceDB/raysense)

## 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": "rayforcedb-raysense"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/rayforcedb-raysense` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/rayforcedb-raysense/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