Skill: wms2537/ai-project-learner (version 1.0.0)

--- name: ai-project-learner description: Generate a concept dependency graph for AI projects. Identifies what you need to learn, in what order, adapted to project complexity. version: 1.0.0 metadata: openclaw: requires: env: - SKILLPAY_API_KEY emoji: "🧠" --- # AI Project Learner Generate a concept dependency graph for users building projects with AI. Identifies what they need to learn, in what order, with difficulty adapted to the project's complexity. ## How to Use This Skill ### Step 1: Charge billing POST to the skill endpoint to authorize payment: ``` POST https://ai-project-learner.swmengappdev.workers.dev/charge Content-Type: application/json {"user_id": "<user_id>"} ``` If successful, you'll receive `{"success": true}`. If payment fails, you'll receive a `payment_url` to share with the user. ### Step 2: Analyze the project From the user's project description, determine: **Complexity level:** - **beginner** — Uses a single API or library, straightforward pipeline (e.g., "chatbot using OpenAI API", "sentiment classifier with HuggingFace") - **intermediate** — Combines multiple AI techniques, has data pipeline or custom logic (e.g., "RAG chatbot with custom embeddings", "multi-agent workflow") - **advanced** — Involves fine-tuning, custom model architectures, or production-scale systems (e.g., "fine-tuned LLM for legal documents", "real-time ML pipeline with feature store") **Domain:** Classify into one of: `nlp`, `cv` (computer vision), `agents`, `data`, `multimodal`, `generative`, `mlops`, `other` ### Step 3: Build the concept dependency graph Generate **8-20 concepts** (scale with complexity: beginner ~8, intermediate ~12-15, advanced ~15-20). For each concept, provide: - **id** — kebab-case identifier (e.g., `text-embeddings`) - **name** — Human-readable name (e.g., "Text Embeddings") - **description** — 1-2 sentence explanation of what it is and why it matters for this project - **difficulty** — Integer 1-5 (1=fundamental, 5=advanced) - **prerequisites** — Array of concept `id`s that should be understood first **Rules for building the graph:** - Every concept's prerequisites must reference other concepts in the graph - Concepts with no prerequisites are entry points (difficulty 1-2) - No circular dependencies - Order concepts so prerequisites always appear before dependents - Descriptions should be practical ("what it is + why you need it for this project"), not academic - Difficulty should be calibrated to the project: a beginner project should have mostly difficulty 1-3 concepts, an advanced project can have difficulty 4-5 concepts **Concept categories to consider (pick relevant ones):** - Foundations: LLMs, APIs, prompting, tokens, context windows - Data: embeddings, vector databases, chunking, preprocessing - Architecture: RAG, agents, chains, tool use, memory, planning - Training: fine-tuning, RLHF, LoRA, evaluation, datasets - Production: deployment, monitoring, caching, rate limiting, cost optimization - Safety: guardrails, content filtering, hallucination detection, red teaming ### Step 4: Compute learning order Produce a topologically sorted `learning_order` array of concept `id`s. This is the recommended study sequence — prerequisites always come before concepts that depend on them. ### Step 5: Estimate total learning time Estimate `estimated_hours` as a total for all concepts. Use these rough heuristics: - Difficulty 1 concept: ~1 hour - Difficulty 2 concept: ~1.5 hours - Difficulty 3 concept: ~2.5 hours - Difficulty 4 concept: ~4 hours - Difficulty 5 concept: ~6 hours ### Output Format Return the result as JSON: ```json { "project": "<user's project description>", "complexity": "beginner|intermediate|advanced", "domain": "nlp|cv|agents|data|multimodal|generative|mlops|other", "concepts": [ { "id": "llm-basics", "name": "Large Language Models", "description": "Neural networks trained on vast text data that can generate and understand language. The foundation of your chatbot project.", "difficulty": 1, "prerequisites": [] }, { "id": "api-integration", "name": "LLM API Integration", "description": "Connecting to LLM providers (OpenAI, Anthropic) via REST APIs. How you'll send prompts and receive responses.", "difficulty": 1, "prerequisites": ["llm-basics"] } ], "learning_order": ["llm-basics", "api-integration"], "estimated_hours": 15 } ``` ## Pricing $0.01 USDT per call via SkillPay.me


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