Openclaw Skills for personalized learning turns natural-language study requests into concept explanations, language lessons, and concise study guides.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install learn-cog-free
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-cog-free 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 Personalized Learning Assistant Free Edition?
Openclaw Skills for the personalized learning assistant free edition is an AI-driven learning workflow designed to help students and self-learners understand ideas faster, practice smarter, and generate structured study materials.
It focuses on three core learning modes: concept explanations, basic language learning support, and study guide creation. With Openclaw Skills, users can ask for beginner-friendly breakdowns, analogy-based explanations, step-by-step homework help, writing feedback, summaries, and quick reference sheets—all in a structured, reusable format that fits everyday learning and knowledge acquisition.
Personalized Learning Assistant Free Edition Use Cases
- Explain difficult concepts in beginner-friendly terms, including analogies and step-by-step breakdowns.
- Get homework support for math, physics, programming, and other foundational STEM topics.
- Learn basic language skills such as grammar, vocabulary, and phrase building.
- Review or improve writing with language learning feedback and correction suggestions.
- Generate study guides, chapter summaries, and one-page reference sheets for exams.
- Use Openclaw Skills when you need fast knowledge retrieval for daily study sessions.
How Personalized Learning Assistant Free Edition Works
- Confirm the environment meets the dependency requirements for the AI agent platform and runtime.
- Provide the learning request in natural language, including the topic, target skill level, and preferred style.
- The agent parses the input parameters, applies the appropriate learning mode, and prepares a structured response.
- Openclaw Skills generates the output as a formatted learning result, such as an explanation, language lesson, or study guide.
- Review the response for completeness and accuracy, then continue with practice, revision, or follow-up prompts.
- If the result is insufficient or too advanced, refine the prompt with the learner level, style preference, or an example request.
Personalized Learning Assistant Free Edition Setup
- Ensure your AI agent environment supports SKILL.md-based skills on Claude Code, Cursor, Codex, Gemini CLI, or a similar agent platform.
- Verify the runtime is available on Windows, macOS, or Linux.
- Configure the required API key before running Openclaw Skills:
export API_KEY=${API_KEY:?Please set the environment variable}
Restart your shell or open a new terminal session so the environment variable is picked up.
Keep the API key secure and avoid committing it to source control.
Invoke the skill from your AI agent conversation by supplying:
input: the learning request or instructionoptions: optional mode, style, or formatting preferencescallback_url: optional asynchronous completion webhook
Validate the returned JSON payload and confirm the
successstatus before using the generated learning content.
Personalized Learning Assistant Free Edition Data Schema & Taxonomy
Openclaw Skills organizes inputs and outputs around a lightweight request/response schema for learning tasks.
Input schema
| Field | Type | Required | Purpose |
|---|---|---|---|
| input | string | Yes | The learning prompt, topic, or instruction to process |
| options | object | No | Optional configuration such as mode selection or formatting preference |
| callback_url | string | No | Webhook URL for asynchronous completion notifications |
Dependency model
| Dependency | Type | Required | Source |
|---|---|---|---|
| LLM API | API | Yes | Provided by the agent runtime |
Processing taxonomy
- Concept explanations: analogies, beginner breakdowns, homework support, and code debugging help.
- Language learning: grammar explanations, vocabulary building, and writing feedback.
- Study guides: chapter summaries, exam prep notes, and one-page references.
- Structured execution: input parsing, task routing, formatting, and validation.
Output schema
{
"success": true,
"data": {
"result": "processed learning output",
"execution_time": "0.5s",
"metadata": {
"version": "1.0",
"processor": "learn-cog"
}
},
"execution_log": [
"parse input parameters",
"run core processing",
"format output"
],
"error": null
}
Operational notes
- The skill returns structured metadata for downstream automation.
- Result validation is expected after each run.
- Error handling should be driven by the returned status and log details.
Personalized Learning Assistant Free Edition Advanced Features
- Supports multiple learning modes in one Openclaw Skills workflow: concept explanation, language learning, and study guide generation.
- Accepts optional configuration through
optionsfor style, format, and mode selection. - Can be used asynchronously via
callback_urlfor agent-based automation pipelines. - Produces structured JSON output with execution logs and metadata for downstream processing.
- Works across common AI agent platforms, making Openclaw Skills portable for different developer setups.
- Designed for quick iteration: users can refine prompts by learner level, analogy preference, or output format.
SKILL.md
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