# Engram

> Engram — keggan-std-engram. Use this tool when you need to retain and manage knowledge across coding sessions, enabling AI agents to learn from experience and recall previous interactions. Engram solves problems of knowledge loss and repetition by providing a persistent memory cortex that integrates with git for version control. It accepts input from AI coding agents and outputs stored knowledge, ideal for use in continuous learning and development contexts.

Canonical page: https://skillsregistry.net/skills/keggan-std-engram  
JSON: https://api.skillsregistry.net/v1/skills/keggan-std-engram

## Description

Persistent Memory Cortex for AI Coding Agents

## Trust

- **Trust score (0–1):** 0.84
- **Verification tier:** verified
- **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/keggan-std/Engram)

## 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": "keggan-std-engram"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/keggan-std-engram` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/keggan-std-engram/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