An advanced technical learning co-pilot designed to help users master computer science, AI, and software engineering through structured knowledge analysis and personalized study plans.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install learning-assistant
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 learning-assistant 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 Learning Assistant?
The Learning Assistant is a comprehensive educational framework built for Openclaw Skills, specifically tailored for students and professionals in Computer Science, Machine Learning, and Software Engineering. It transforms a standard AI agent into a sophisticated mentor that can analyze code, generate deep research, and provide Socratic-style tutoring. By utilizing a modular architecture, the skill ensures that technical explanations are accurate, well-structured, and aligned with the user's current knowledge level.
This skill goes beyond simple Q&A by maintaining a persistent learning state. It tracks your progress through a dedicated workspace, managing your technical profile and indexing your study history. Whether you are preparing for a high-stakes technical interview or diving into the mechanics of reinforcement learning, the Learning Assistant provides the structural rigor and pedagogical depth required for high-level technical mastery within the ecosystem of Openclaw Skills.
Learning Assistant Use Cases
- Learning new technical concepts or frameworks in CS, AI, ML, and SE.
- Reviewing existing knowledge and preparing for technical job interviews.
- Generating structured study plans, knowledge summaries, and digital flashcards.
- Deeply analyzing and debugging complex code snippets for better understanding.
- Conducting in-depth research on specific technical topics with high accuracy.
How Learning Assistant Works
- Session Initialization: The skill begins by confirming the current date and checking for an existing user profile to tailor the session's depth and style.
- Review Check: It scans the global learning index for any topics due for review, offering a spaced-repetition reminder to the user.
- Intent Recognition: Based on user input or file uploads, the skill routes requests to specific modules like knowledge analysis, interaction modes (Socratic, Feynman, or Interview), or code adaptation.
- Structured Execution: The agent follows a strict three-round self-audit process to ensure technical accuracy, completeness, and timeliness of the output.
- Persistence: At the end of the session, the skill updates the user profile with new growth events, generates session anchors for continuity, and indexes new learning artifacts in the workspace.
Learning Assistant Setup
To install and initialize the Learning Assistant within your Openclaw Skills environment, follow these steps:
- Clone the skill repository into your skills directory:
git clone https://github.com/openclaw/learning-assistant.git
- Ensure the directory structure is maintained with the
references/andworkspace/folders. - Trigger the skill by typing
learning assistantortechnical learningin your agent interface. - Follow the initial questionnaire to generate your
USER_PROFILE.mdand set your technical stack preferences.
Learning Assistant Data Schema & Taxonomy
The skill organizes data within a structured workspace/ directory to ensure long-term learning continuity:
| File Name | Description |
|---|---|
USER_PROFILE.md |
Stores user expertise level, technology stack, and learning preferences. |
LEARNING_INDEX.md |
A global index tracking all study topics, tags, and review schedules. |
YYYYMMDD_[Type]_[Topic].md |
Individual learning artifacts such as notes, plans, or interview prep. |
README.md |
Documentation for the workspace data usage. |
All output follows a standard naming convention to facilitate easy searching and indexing.
Learning Assistant Advanced Features
- Three-Tier Output Modes: Choose between FAST (quick summary), STD (standard structured analysis), or DEEP (comprehensive principle derivation and code examples).
- Modular Interaction Modes: Switch between Socratic questioning, Feynman technique explanations, and simulated technical interviews.
- Session Continuity Anchors: Automatically generates anchor files to resume long-running learning tasks across different sessions.
- Automated Self-Audit: Every technical response undergoes a mandatory internal check for accuracy, completeness, and relevance.
- Dynamic Profile Reconstruction: Automatically rebuilds and refines your technical profile every 10 sessions to reflect your growing expertise.
SKILL.md
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