A pedagogical AI agent skill that transforms homework help into a structured learning experience through guided hints and subject-specific tutoring.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install homework
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 homework 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 Homework Assistant?
The Homework Assistant is a specialized protocol within the Openclaw Skills ecosystem designed to move beyond simple answer-generation. It focuses on the core philosophy that learning is the ultimate goal, not just completion. By providing a framework for AI agents to act as tutors, it ensures students receive the smallest useful nudge needed to overcome obstacles. This skill is essential for developers building educational agents that need to handle everything from complex calculus to literary analysis while maintaining high standards of academic integrity.
Built to be highly adaptable, this skill allows agents to switch between Quick Answer, Learning, and Practice modes. It provides a robust set of rules for various subjects, ensuring that math problems are step-by-step and copyable, while essays are supported through brainstorming and outlining rather than ghostwriting. It is a cornerstone for any educational implementation using Openclaw Skills.
Homework Assistant Use Cases
- Mastering STEM subjects by understanding the logic and setup behind complex formulas.
- Developing writing skills through thesis brainstorming and structural feedback rather than simple rewriting.
- Preparing for high-stakes exams with interactive, one-question-at-a-time practice sessions.
- Analyzing humanities texts using guided interpretation frameworks instead of pre-baked conclusions.
- Overcoming academic roadblocks by identifying recurring patterns in errors and misconceptions.
How Homework Assistant Works
- The agent detects the educational context and specific subject matter from the student's initial query.
- It defaults to Learning Mode, initiating a diagnostic phase by asking what the student has already attempted to solve.
- Based on the subject-specific rules defined in the Openclaw Skills framework, it provides a smallest useful nudge or hint to guide the student.
- If explicitly requested, it can transition to Quick Answer mode for urgent help, providing step-by-step copyable work with brief method notes.
- It continuously monitors for patterns in student errors, offering personalized feedback and verifying understanding through follow-up questions.
Homework Assistant Setup
To implement this skill, add the markdown definition to your project's skill directory. Ensure your AI agent is configured to recognize the pedagogical metadata inherent in Openclaw Skills.
# Create the dedicated skill directory
mkdir -p ./skills/homework-tutor
# Clone or move the SKILL.md into your environment
mv SKILL.md ./skills/homework-tutor/
Homework Assistant Data Schema & Taxonomy
The Homework Assistant organizes its logic into distinct pedagogical tracks and subject rules:
| Component | Function | Implementation |
|---|---|---|
| Response Modes | Determines the level of intervention | Learning, Quick Answer, Practice |
| Subject Modules | Applies specific rules for Math, Science, or Essays | Contextual logic gates |
| Error Tracking | Analyzes historical interaction for recurring mistakes | Metadata state |
| Math Formatting | Ensures all steps are provided in a copyable format | Plain-text math notation |
| Integrity Check | Prevents full essay generation or direct copying | Content filtering guidelines |
Homework Assistant Advanced Features
- Intelligent Level Matching: Automatically adjusts vocabulary and explanation complexity to match the student's detected proficiency level.
- Trap Problem Logic: Dynamically generates practice questions that include common pitfalls to test true conceptual grasp.
- Contextual Exam Prep: Features an interactive quiz mode that waits for student responses before providing detailed explanations.
- Understanding Verification: Implements a feedback loop where students are asked to explain steps back to the agent to confirm mastery.
- Framework-Based Analysis: For humanities, it provides structural frameworks that empower students to form their own conclusions rather than providing the final answer.
SKILL.md
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