# code-trajectory-mcp

> code-trajectory-mcp — ratatoaster-code-trajectory-mcp. Use this tool when you need to maintain continuity in your coding projects across multiple AI sessions. It solves the problem of lost progress and context switching by keeping track of your code trajectory, allowing you to pick up where you left off. With git capabilities, it accepts code repositories as input and outputs a consistent and up-to-date project state.

Canonical page: https://skillsregistry.net/skills/ratatoaster-code-trajectory-mcp  
JSON: https://api.skillsregistry.net/v1/skills/ratatoaster-code-trajectory-mcp

## Description

Code Trajectory MCP Keep your coding momentum across AI sessions.

## Trust

- **Trust score (0–1):** 0.50
- **Verification tier:** unverified

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** container
- **Runtime environment:** vm
- **License:** MIT
- **Updated:** 2026-09-20

## Source

- **Source listing:** [GitHub](https://github.com/RataToaster/code-trajectory-mcp)

## 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": "ratatoaster-code-trajectory-mcp"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/ratatoaster-code-trajectory-mcp` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/ratatoaster-code-trajectory-mcp/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