# modelroute

> Use this tool when you need to identify and prioritize code issues, such as TODO, FIXME, and XXX comments, to improve code quality and maintenance. Modelroute scans code via MCP and provides findings in table, JSON, or SARIF format, making it easy to integrate into development workflows. It solves problems related to code review, technical debt, and issue tracking, and is ideal for use in software development and maintenance contexts.

Canonical page: https://skillsregistry.net/skills/cognis-digital-modelroute  
JSON: https://api.skillsregistry.net/v1/skills/cognis-digital-modelroute

## Description

Enables AI agents to scan code for TODO, FIXME, XXX issues via MCP, providing prioritized findings in table, JSON, or SARIF format.

## Trust

- **Trust score (0–1):** 0.60
- **Verification tier:** unverified

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** ai-ml
- **Updated:** 2026-06-28

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/sj7lpdox33)
- **Repository:** <https://github.com/cognis-digital/modelroute>

## 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": "cognis-digital-modelroute"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/cognis-digital-modelroute` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/cognis-digital-modelroute/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