# MemCell

> MemCell — ai-memcell-memcell. Use this tool when you need to enhance AI decision-making with recall and outcome reporting, solving problems of uncertain or unvalidated actions by providing a living memory of past experiences. MemCell takes in past actions and outcomes as inputs and outputs informed decisions with earned confidence. It is ideal for use in dynamic environments where AI agents require adaptive learning and reliable performance.

Canonical page: https://skillsregistry.net/skills/ai-memcell-memcell  
JSON: https://api.skillsregistry.net/v1/skills/ai-memcell-memcell

## Description

Living memory for AI coding agents: recall before acting, report outcomes so confidence is earned.

## Trust

- **Trust score (0–1):** 0.89
- **Verification tier:** verified
- **Last scanned:** 2026-09-28

## Facts

- **Version:** 0.8.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Updated:** 2026-09-28

## Source

- **Source listing:** [MCP Registry](https://registry.modelcontextprotocol.io/v0/servers/ai.memcell%2Fmemcell)
- **Repository:** <https://github.com/memcell-ai/cli>

## Use it

MCP endpoint published by the skill: `https://memcell.ai/mcp`

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": "ai-memcell-memcell"
    }
  }
}
```

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