ai_skill

Overview

This skill provides hands-on guidance for mastering AI, machine learning, large language models, prompt engineering, and blockchain development. It bundles practical tooling recommendations, best practices, and focused workflows to accelerate building production-ready AI applications. Use it to choose libraries, design pipelines, and apply safety and cost controls.

How this skill works

The skill catalogs core technologies across AI/LLMs, ML frameworks, vector stores, and Web3 toolchains, then maps them to common development tasks. It explains how to connect LLM APIs, integrate transformer libraries, build vector search, and scaffold smart contracts and dApps. It also highlights operational concerns like testing, monitoring, security, and cost optimization.

When to use it

• Building an application that combines LLMs with retrieval-augmented generation
• Designing ML pipelines with PyTorch, TensorFlow, or scikit-learn
• Implementing prompt engineering and deploying production LLM endpoints
• Developing, testing, and auditing smart contracts or Web3 tooling
• Choosing vector databases and search workflows for semantic search

Best practices

• Prioritize ethics and assess societal impact early in design
• Rigorously test models with diverse datasets and edge cases
• Continuously monitor model performance and data drift in production
• Document design choices, prompts, and evaluation metrics for traceability
• Audit smart contracts and enforce secure development practices
• Optimize API usage and model size to control inference costs

Example use cases

• Prototype a conversational agent using an LLM API and LangChain with a Pinecone vector index
• Train and fine-tune a transformer on domain data with Hugging Face and PyTorch
• Build a hybrid app that uses a retrieval layer for long-context question answering
• Create and deploy audited Solidity contracts with Hardhat or Foundry and integrate them via Ethers.js
• Set up CI pipelines to test model behavior, performance, and prompt regressions