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memory-skill

Save, retrieve, and clear timestamped markdown conversation history with semantic search.

356|66|Updated Oct 13, 2025
Search Tags:#semantic search#vector database#chat history#context persistence#conversation memory#assistant tooling#markdown logging
One-click install
npx skills add https://github.com/zeenie-ai/MachinaOS --skill memory-skill-zeenie-ai
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: memory-skill
Source: https://github.com/zeenie-ai/MachinaOS/tree/main/server/skills/assistant/memory-skill
Command: npx skills add https://github.com/zeenie-ai/MachinaOS --skill memory-skill-zeenie-ai

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Memory management is hard during long or recurring conversations, so users need a reliable way to save context and retrieve the right details later without manually scrolling history.

Core Features & Use Cases

  • Memory capture & persistence: Save explicit user notes and conversation context into short-term (markdown) history with timestamped entries.
  • Recall & semantic search: Retrieve recent history by count or perform long-term semantic search when long-term memory is enabled.
  • Maintenance controls: View the full markdown log and clear conversation memory safely when needed.

Use case example: A user repeatedly discusses a project’s requirements across multiple days; they can save key decisions, then later search for “the decision about the auth approach” to quickly resurface the relevant discussion.

Quick Start

Ask the assistant to remember something by saying: “Remember that my project uses email-based authentication and confirm it with stakeholders before launch.”

Frequently Asked Questions about memory-skill

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I persist conversation context across multiple chat sessions?▼

You can persist conversation context by saving explicit notes and chat history into timestamped markdown logs, retrieving recent entries by count or querying long-term semantic search when enabled.

How does semantic search work for retrieving past chat history?▼

Semantic search retrieves relevant past discussions by querying long-term memory with a text query and result limits, resurfacing specific conversation details without manual scrolling.

Can I save specific project decisions and recall them later in a chat?▼

Yes, you can ask the assistant to remember specific details like project decisions, which are saved as timestamped entries in markdown history for later recall and search.

What is the best way to manage long-term conversation memory for an assistant?▼

Managing long-term memory involves saving context to markdown logs for short-term recall and utilizing optional vector database semantic search to fetch relevant historical interactions.

How do I clear conversation memory safely when needed?▼

You can clear conversation memory safely using a tool-backed maintenance operation that requires confirmation before deleting the saved chat history and markdown logs.

Does window-based truncation affect how recent chat history is retrieved?▼

Window-based truncation limits the number of recent entries retrieved from markdown history, ensuring only the most recent context is fetched during assistant interactions.

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