A structured documentation framework that enables AI agents to record, classify, and persist learning insights across sessions.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install learn-journal
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 learn-journal 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 Learn Journal?
The Learn Journal system is a technical documentation framework designed to solve the problem of ephemeral AI memory. While most AI interactions are lost once a session restarts, this skill provides a standardized method for agents to write their experiences to persistent Markdown files. By implementing this system within Openclaw Skills, developers ensure that their agents can recall past successes, avoid repeating errors, and maintain a clear record of architectural decisions.
At its core, the skill emphasizes the philosophy that mental notes do not survive session restarts. By forcing the agent to document key findings into specific files like MEMORY.md and specialized learning logs, the workspace becomes an evolving knowledge base. This significantly reduces redundant discovery phases and improves the overall reliability of autonomous agents working within Openclaw Skills.
Learn Journal Use Cases
- Conducting post-task retrospectives to capture workflow optimizations.
- Documenting root causes and prevention strategies after resolving technical errors.
- Cataloging new tools, libraries, or methodologies discovered during research tasks.
- Recording the rationale and consequences of significant configuration or design decisions.
- Creating a permanent trail of configuration changes to facilitate easier rollbacks or audits.
How Learn Journal Works
- The agent identifies a learning trigger, such as task completion, an error event, or a strategic decision.
- A unique entry ID is generated using the standardized TYPE-YYYYMMDD-NNN format to ensure traceability.
- The agent selects the appropriate template: LRN for new knowledge, ERR for errors, FEAT for tool highlights, or DEC for decisions.
- Structured content is written to the relevant file location, such as a daily log or a specialized learning directory.
- High-value insights are periodically consolidated from daily logs into the primary MEMORY.md for long-term accessibility within Openclaw Skills.
Learn Journal Setup
To initialize the Learn Journal system in your workspace, you need to create the directory structure and the core tracking files. Run the following commands in your terminal:
mkdir -p memory .learnings
touch MEMORY.md
touch .learnings/LEARNINGS.md
Once created, inform your AI agent to use these paths for all persistent logging and memory tasks associated with Openclaw Skills.
Learn Journal Data Schema & Taxonomy
The system utilizes a tiered storage strategy to organize information based on its lifecycle and importance:
| File Path | Type | Retention Level |
|---|---|---|
{workspace}/MEMORY.md |
Long-term Knowledge | High (Permanent) |
{workspace}/memory/YYYY-MM-DD.md |
Daily/Weekly Logs | Medium (Active) |
{workspace}/.learnings/LEARNINGS.md |
Subject-Specific Learning | Medium (Topic-based) |
Each entry must include a unique ID, a source reference, and a timestamp for optimal indexing within Openclaw Skills.
Learn Journal Advanced Features
- Unique ID system (TYPE-YYYYMMDD-NNN) for precise referencing across multiple documentation files.
- Standardized entry templates for Error (ERR) tracking, including root cause analysis and prevention steps.
- Strategic Decision (DEC) logging to record options considered and the rationale for the final choice.
- Feature (FEAT) rating system to evaluate the utility of new tools or methods on a 1-5 star scale.
- Multi-tier memory architecture that separates transient daily notes from high-value architectural knowledge.
SKILL.md
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