AI Project Learner analyzes project descriptions to generate structured concept dependency graphs and prioritized learning roadmaps for developers.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install ai-project-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 ai-project-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 AI Project Learner?
AI Project Learner is a specialized tool designed to demystify the technical journey of building artificial intelligence applications. By evaluating a specific project description, the skill identifies the necessary technical concepts and organizes them into a logical, dependency-aware curriculum. As part of the Openclaw Skills ecosystem, it provides developers with a clear, actionable path from initial idea to technical mastery, ensuring that time is spent learning only what is relevant to the project at hand.
The skill synthesizes complex information into manageable nodes, categorizing them by difficulty and prerequisites. It bridges the gap between high-level ambition and low-level execution by providing practical descriptions of why each concept is required for the specific project context. This makes it an essential asset for anyone using Openclaw Skills to accelerate their development lifecycle.
AI Project Learner Use Cases
- Mapping out the prerequisites for building a RAG-based document search system.
- Planning the learning sequence for a team transitioning from basic API usage to custom model fine-tuning.
- Estimating the total time and technical depth required to launch a multi-agent AI workflow.
- Creating a structured onboarding guide for new developers joining an existing AI project.
How AI Project Learner Works
- The skill initiates a payment authorization through the SkillPay endpoint to ensure access to the processing engine.
- It analyzes the user's project description to determine its complexity level (beginner, intermediate, or advanced) and technical domain (such as NLP, Computer Vision, or Generative AI).
- A comprehensive graph of 8 to 20 concepts is generated, including unique identifiers, difficulty ratings, and specific prerequisites.
- The skill performs a topological sort on the concepts to produce a recommended learning order that respects all dependencies.
- Finally, it calculates the total estimated learning time based on the difficulty of each node in the graph, providing a complete project roadmap in JSON format.
AI Project Learner Setup
To utilize this skill within the Openclaw Skills framework, you must configure your environment with a valid SkillPay API key.
# Set your SkillPay API key
export SKILLPAY_API_KEY="your_api_key_here"
Authorization for each execution is handled via a POST request to the skill endpoint:
curl -X POST https://ai-project-learner.swmengappdev.workers.dev/charge \
-H "Content-Type: application/json" \
-d '{"user_id": "your_user_id"}'
AI Project Learner Data Schema & Taxonomy
The skill organizes data into a structured JSON object representing the project roadmap. This allows for easy integration into other Openclaw Skills or developer dashboards.
| Field | Type | Description |
|---|---|---|
project |
String | The original project description provided by the user. |
complexity |
String | Categorization of the project (beginner, intermediate, or advanced). |
concepts |
Array | Objects containing id, name, description, difficulty, and prerequisites. |
learning_order |
Array | A list of concept IDs sorted by dependency. |
estimated_hours |
Integer | Total time estimate for mastering the roadmap. |
AI Project Learner Advanced Features
- Complexity-Aware Roadmap Generation: Automatically scales the number and difficulty of concepts based on the project scope.
- Topological Dependency Sorting: Ensures a logical flow where foundational concepts are always presented before advanced applications.
- Domain-Specific Classification: Tailors learning paths specifically for NLP, MLOps, Computer Vision, and Generative AI domains.
- SkillPay Integration: Built-in micro-payment support for cost-effective, per-use execution within the Openclaw Skills environment.
SKILL.md
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