A persistent memory skill that enables AI agents to recall past decisions, bugfixes, and technical context across different sessions and devices.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install engrm-memory
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install engrm-memory using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Engrm Memory?
Engrm Memory is a specialized tool designed to eliminate the friction of cold starts in AI-assisted development. By integrating this skill into your workflow, you allow your AI agent to pull relevant prior knowledge into current sessions, ensuring that past decisions, fixes, and architectural discoveries are never lost. It transforms passive agent interaction into an evolving knowledge base, making multi-device and multi-agent development significantly more effective through Openclaw Skills.
This skill focuses on high-signal outcomes, meaning it prioritizes durable decisions and complex bug resolutions over trivial implementation details. By maintaining this continuous memory loop, Engrm Memory ensures that your AI remains informed about the specific nuances of your codebase, even if you are returning to a project after a long break or switching between different coding environments.
Engrm Memory Use Cases
- Resuming complex development tasks where project continuity is more important than a fresh start.
- Syncing technical context and established patterns between different machines or development agents.
- Recalling the specific cause and resolution of past bugs to prevent regressions in similar subsystems.
- Capturing high-level architectural decisions and lessons learned during a session for future reference.
How Engrm Memory Works
- The agent begins by checking recent activity or searching the Engrm store for memory relevant to the current file or task.
- Timeline context is retrieved if the specific code area has undergone recent changes or churn.
- The agent analyzes prior decisions and historical data before proposing or implementing any new code changes.
- After the task is complete, the agent identifies high-signal discoveries and saves them to the persistent memory store.
Engrm Memory Setup
To use this skill, you must have the Engrm environment already configured and authenticated on your local machine. Openclaw Skills work by interfacing with existing tools; therefore, no fallback commands should be used if the environment is not detected.
# Ensure Engrm is active in your environment
# No specific CLI installation is required for the skill itself
# as it leverages the host's existing Engrm capabilities.
Engrm Memory Data Schema & Taxonomy
Engrm Memory organizes technical context into several key data structures to optimize retrieval within Openclaw Skills.
| Component | Type | Description |
|---|---|---|
| Timeline | Sequential Log | Tracks chronological changes and session churn. |
| High-Signal Memories | Key-Value Store | Stores durable decisions, bugfixes, and reusable patterns. |
| Stats | Metadata | Provides insights into memory density and retrieval frequency. |
| Recent Activity | Cache | Quick-access list of the most recent subsystem interactions. |
Engrm Memory Advanced Features
- Multi-agent synchronization to share knowledge between different AI entities working on the same codebase.
- Intelligent filtering that ignores noisy implementation details to prevent retrieval pollution.
- Cross-device continuity, allowing a session started on one machine to inform a session on another.
- Automated lesson extraction that identifies and saves the most valuable observations from a coding session.
SKILL.md
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