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ai skill

This skill helps you design and implement AI, ML, and blockchain projects end-to-end, from model prompts to smart contracts, with best practices.

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SKILL.md

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---
name: ai-ml-technologies
description: Master AI, machine learning, LLMs, prompt engineering, and blockchain development. Use when building AI applications, working with LLMs, or developing smart contracts.
sasmp_version: "1.3.0"
bonded_agent: 05-ai-emerging-technologies
bond_type: PRIMARY_BOND
---

# AI & Emerging Technologies Skill

## Quick Start - OpenAI API

```python
from openai import OpenAI

client = OpenAI(api_key="sk-...")

# Simple completion
response = client.chat.completions.create(
  model="gpt-4",
  messages=[
    {"role": "system", "content": "You are helpful assistant"},
    {"role": "user", "content": "Explain machine learning"}
  ],
  temperature=0.7,
  max_tokens=500
)

print(response.choices[0].message.content)
```

## Core Technologies

### AI & LLMs
- OpenAI API (GPT-4)
- Claude API (Anthropic)
- Open-source LLMs (Llama, Mistral)
- LangChain for applications
- Vector databases (Pinecone, Weaviate)

### Machine Learning
- TensorFlow / PyTorch
- scikit-learn
- XGBoost
- Hugging Face Transformers

### Blockchain & Web3
- Solidity for smart contracts
- Web3.js / Ethers.js
- Hardhat development
- Foundry

### Game Engines
- Unity (C#)
- Unreal Engine (C++)
- Godot (GDScript)

## Best Practices

1. **AI Ethics** - Consider societal impact
2. **Testing** - Rigorous evaluation
3. **Monitoring** - Track model performance
4. **Documentation** - Clear decision records
5. **Security** - Smart contract auditing
6. **Cost Optimization** - Minimize API usage
7. **Version Control** - Track models and prompts
8. **Responsible AI** - Bias and fairness

## Resources

- [OpenAI Documentation](https://platform.openai.com/docs)
- [LangChain Documentation](https://langchain.readthedocs.io/)
- [Solidity Documentation](https://docs.soliditylang.org/)
- [Pytorch Documentation](https://pytorch.org/)

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

FAQ

Which LLMs and libraries should I start with?

Begin with a managed LLM API for fast prototyping (GPT-style or Claude) and use LangChain for orchestration; add open-source models and Hugging Face tools as you need customization.

How do I control costs when using LLMs?

Reduce token usage with concise prompts, use smaller models for non-critical tasks, cache responses, batch requests when possible, and monitor metrics to identify expensive calls.