# cortexmem

> Use this tool when you need to leverage persistent memory for AI coding agents, building semantic memory from git history and codebase to enhance search and retrieval capabilities. It solves problems related to codebase navigation, knowledge retention, and information discovery, providing a searchable interface via MCP tools. By utilizing cortexmem, AI agents can efficiently access and manage code-related knowledge, streamlining development and collaboration processes.

Canonical page: https://skillsregistry.net/skills/ashprakash-cortexmem  
JSON: https://api.skillsregistry.net/v1/skills/ashprakash-cortexmem

## Description

Persistent memory for AI coding agents. Builds semantic memory from git history and codebase, searchable via MCP tools.

## Trust

- **Trust score (0–1):** 0.67
- **Verification tier:** scanned
- **Last scanned:** 2026-08-31

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** version-control
- **Updated:** 2026-08-31

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/abg16gipn7)
- **Repository:** <https://github.com/Ashprakash/cortexmem>

## 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": "ashprakash-cortexmem"
    }
  }
}
```

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