# MemLayer

> Use this tool when you need to capture and leverage agent experiences to improve performance and build reusable capabilities. MemLayer solves the problem of knowledge retention and skill development in AI agents by surfacing effective strategies and storing them for future use. It takes in experience data and outputs optimized, reusable capabilities, making it ideal for applications requiring continuous learning and improvement.

Canonical page: https://skillsregistry.net/skills/prociq-memlayer-plugin  
JSON: https://api.skillsregistry.net/v1/skills/prociq-memlayer-plugin

## Description

Agent learning infrastructure that captures experience, surfaces what works, and builds reusable capabilities. MCP-native with 94.4% LongMemEval accuracy.

## Trust

- **Trust score (0–1):** 1.00
- **Verification tier:** verified
- **Last scanned:** 2026-09-28

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** other
- **Updated:** 2026-09-28

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/j188macbdj)
- **Repository:** <https://github.com/ProcIQ/MemLayer-Plugin>

## 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": "prociq-memlayer-plugin"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/prociq-memlayer-plugin` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/prociq-memlayer-plugin/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