agent-activity-log

Create and maintain a memory.md file for multi-session AI workflows.

16|3|Updated May 5, 2026

Search Tags:#persistence #multi-session #ai-agents #agent-memory #session-tracking #memory.md #activity-log

Ready to run

npx skills add https://github.com/Kevin-Liu-01/Agent-Machines --skill agent-activity-log

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: agent-activity-log
Source: https://github.com/Kevin-Liu-01/Agent-Machines/tree/main/knowledge/skills/agent-activity-log
Command: npx skills add https://github.com/Kevin-Liu-01/Agent-Machines --skill agent-activity-log

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Persistent agent memory ensures continuity across sessions by initializing and maintaining a memory.md file in any project. It enforces a discipline where agents read memory.md at session start and append after meaningful changes to preserve history and context.

Core Features & Use Cases

Bootstrap a memory.md file for new projects and enforce an append-only history.
Read-before-act and write-after-change discipline to maintain coherent multi-session memory.
Session protocol guidance and structured logging for decisions, changes, and open threads.

Quick Start

Create and initialize a memory.md in your project and implement the read-before-act and append-after-change workflow.

Frequently Asked Questions about agent-activity-log

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

FAQPage Schema

How do I keep AI agent memories alive across multiple sessions?▼

To keep AI agent memories alive across multiple sessions, you bootstrap a persistent memory.md file in your project. This enforces a read-before-act and write-after-change protocol to maintain context and history.

What is the read-before-act protocol for agent memory persistence?▼

The read-before-act protocol for agent memory persistence requires an AI agent to read the memory.md file at the start of a session. It pairs with write-after-change discipline to ensure structured logging of decisions and coherent multi-session workflows.

How do I create a persistent activity log for AI agents in my project?▼

You create a persistent activity log for AI agents by initializing a memory.md file in your project directory. This file serves as an append-only history where agents record meaningful changes and open threads across sessions.

Can I use a structured session log to track AI agent decisions over time?▼

Yes, you can use a structured session log to track AI agent decisions over time. By maintaining a memory.md file with session protocol guidance, you keep decisions, changes, and open threads fully traceable across multiple sessions.

Do I need any external dependencies to maintain multi-session agent memory?▼

You do not need any external dependencies to maintain multi-session agent memory. The approach relies entirely on creating and updating a local memory.md file within your existing project structure without additional tools.

When should I implement an append-only history for AI agent session tracking?▼

You should implement an append-only history for AI agent session tracking when you need to preserve a continuous context across disconnected workflows. This ensures past decisions and open threads remain traceable without losing historical data.