A specialized tool implementing the Active Learning Protocol to manage agent memory and structured help requests.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install active-learner
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 active-learner 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 Active Learner?
Active Learner is a core utility designed for the Openclaw ecosystem that implements the Active Learning Protocol (R3). It serves as the bridge between an agent's operational experiences and its long-term knowledge base. By programmatically managing entries in MEMORY.md, it ensures that lessons learned during task execution are preserved and organized for future use, preventing the repetition of past mistakes.
This skill is a fundamental component for developers utilizing Openclaw Skills, enabling agents to evolve their capabilities autonomously while maintaining a clear line of communication with human operators when complex obstacles arise. It focuses on turning transient execution data into permanent, actionable knowledge.
Active Learner Use Cases
- Documenting new discoveries or protocol updates directly into the agent's long-term memory.
- Standardizing the format of help requests when the agent encounters an unknown technical constraint.
- Building a searchable history of lessons learned (R3 protocol) across multiple development sessions.
- Automating the lifecycle of knowledge internalization within an AI coding workflow.
How Active Learner Works
- The agent identifies a piece of information or a failure pattern that qualifies as a reusable lesson.
- The internalize command is triggered with a unique ID, category, and descriptive text.
- The skill parses the input and appends the structured data to the designated MEMORY.md file following the R3 protocol.
- If the agent requires external clarification, the ask command generates a structured request for human intervention.
- The system maintains an organized log of these interactions to ensure continuous improvement of Openclaw Skills.
Active Learner Setup
To integrate this skill into your workflow, ensure the scripts are located in your skills directory and use the following commands:
# Internalize a new lesson into memory
node skills/active-learner/index.js internalize --id "L1" --category "Protocol" --text "Lesson content here..."
# Generate a structured help request
node skills/active-learner/index.js ask --text "I don't understand how to implement X..."
Active Learner Data Schema & Taxonomy
The Active Learner skill organizes data into a structured format within the project's memory file. The following schema is used for internalization:
| Attribute | Description |
|---|---|
| ID | A unique alphanumeric identifier for the lesson (e.g., L101). |
| Category | The classification of knowledge (e.g., Protocol, Technical, Logic). |
| Content | The actual text of the lesson or rule to be remembered. |
| Metadata | Automatic timestamps and cycle IDs provided by the Openclaw Skills environment. |
Active Learner Advanced Features
- Full implementation of the R3 Active Learning Protocol for agent evolution.
- Persistent memory management via automated MEMORY.md updates.
- Structured 'Ask for Help' triggers to reduce agent hallucination during blockers.
- Seamless integration with other Openclaw Skills to create a self-improving agent loop.
SKILL.md
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