Sagargupta16

AI Project Template — Testing skill for Claude Code

Testing community

Unified Python starter for AI projects: classical ML, deep learning, LLM apps, RAG, and agents.

How to install AI Project Template

This entry records only its repository, not the path inside it, so there is no exact command to give. Open Sagargupta16/ai-project-template and copy the folder into ~/.claude/skills/, or the file into ~/.claude/agents/.

What AI Project Template does

Unified Python starter for AI projects: classical ML, deep learning, LLM apps, RAG, and agents. uv + Ruff + pytest.

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README

AI Project Template

[![CI](https://github.com/Sagargupta16/ai-project-template/actions/workflows/ci.yml/badge.svg)](https://github.com/Sagargupta16/ai-project-template/actions/workflows/ci.yml) [![CodeQL](https://github.com/Sagargupta16/ai-project-template/actions/workflows/codeql.yml/badge.svg)](https://github.com/Sagargupta16/ai-project-template/actions/workflows/codeql.yml) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](LICENSE) [![Python 3.13+](https://img.shields.io/badge/python-3.13%2B-blue.svg)](https://www.python.org/downloads/)

A unified Python starter for **every AI use case** -- classical ML, deep learning, LLM apps, RAG, and agents. One layout, optional extras, opinionated tooling.

Why this template

Most AI templates pick a niche. Cookiecutter Data Science assumes sklearn + notebooks. Production RAG templates assume a FastAPI service with a vector store. This template assumes **your project will span several of those** (ML model → RAG retriever → agent tool → API) and gives each concern its own home without forcing you to install frameworks you don't need.

Quick start

# Install uv (https://docs.astral.sh/uv/)
curl -LsSf https://astral.sh/uv/install.sh | sh

# Install base + the extras you need
make setup                                    # base only
uv sync --extra ml --extra llm --extra rag    # targeted
make dev                                      # everything

# Run
make test              # tests
make api               # FastAPI dev server on :8000
make eval              # offline evals on the golden dataset
make notebook          # Jupyter