---
name: memory-hub
description: "Instructions for the upstream workflow: The governed agent memory store — searchable entries with context bundles and an explicit propose, promote and forget lifecycle, typed assets, sensitivity levels and visibility, exposed as MCP tools whose access checks can refuse a read. Use when durable agent memory must be inspectable, permissioned and revocable rather than implicit. Not for caching model responses — use response-cache; not for compressing an in-flight message — use agent-compression or universal-compressor; not for scanning sessions for failure patterns — use botte-learn."
license: MIT
metadata:
  version: "1.0.0"
  domain: agent-skills
  canonical-name: memory_hub
---
# memory-hub — governed, revocable agent memory

## Package scope and prerequisites

This catalog ships instructions, not the executable runtime. Python modules, scripts, local services and optional tools named below must be installed separately from the upstream project. Check their availability before running commands; if they are missing, explain the prerequisite instead of claiming execution or results. The host agent may use model tokens while following these instructions.

## Purpose

A versioned store for durable memories, with the full lifecycle made explicit:
propose, promote, forget. Entries carry a type, a sensitivity level and a
visibility, and MCP access can raise rather than silently return data.

    MCP tools: search_hub, context_bundle, propose_memory, promote_memory, forget_memory

## When NOT to use

- You want a response cached — use response_cache.
- You want a payload compressed — use universal_compressor.
- You want failure patterns mined from logs — use botte_learn.