# code-memory

> code-memory — mstuart-code-memory. Use this tool when you need to retain and retrieve coding context, history, and knowledge. It solves problems of lost context, duplicated effort, and information overload by providing semantic search, git history, and intelligent context preservation. Ideal for AI coding applications, it takes in code snippets and git history as inputs and outputs relevant, context-aware information to inform coding decisions.

Canonical page: https://skillsregistry.net/skills/mstuart-code-memory  
JSON: https://api.skillsregistry.net/v1/skills/mstuart-code-memory

## Description

Persistent memory for AI coding - semantic search, git history, and intelligent context preservation

## Trust

- **Trust score (0–1):** 0.42
- **Verification tier:** scanned
- **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/mstuart/code-memory)

## 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": "mstuart-code-memory"
    }
  }
}
```

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