# codemem

> codemem — jeang42-codemem. Use this tool when you need to access and manage coding project memories across multiple machines, storing projects, reusable assets, sessions, decisions, and commits. It solves problems of fragmented project knowledge and facilitates collaboration by exposing project data to agents over the Machine Coding Protocol (MCP). With codemem, inputs include project metadata and outputs include unified project memories, making it ideal for use in multi-machine coding environments.

Canonical page: https://skillsregistry.net/skills/jeang42-codemem  
JSON: https://api.skillsregistry.net/v1/skills/jeang42-codemem

## Description

Memory for every coding project on every machine: projects, reusable assets, sessions, decisions and commits, exposed to agents over MCP.

## Trust

- **Trust score (0–1):** 0.27
- **Verification tier:** scanned
- **Last scanned:** 2026-09-28

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** container
- **Runtime environment:** vm
- **License:** Apache-2.0
- **Updated:** 2026-09-28

## Source

- **Source listing:** [GitHub](https://github.com/jeang42/codemem)

## 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": "jeang42-codemem"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/jeang42-codemem` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/jeang42-codemem/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