aidriven 0.1.3

A python CLI tool that helps developers to get and install useful AI resources

aidriven

aidriven is a Python library that helps developers discover and manage AI-oriented IDEs and install useful AI resources such as skills, agents, and specs.

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Features

• IDE Discovery — detects locally installed AI-oriented IDEs (VS Code, Cursor, Kiro) across Windows, macOS, and Linux
• Multi-installation support — handles multiple variants and channels (e.g., VS Code Stable and Insiders) as separate entries
• Graceful degradation — partial or corrupted installations are returned with a reduced confidence level rather than silently dropped
• Extensible providers — register custom IDE providers without touching built-in detection logic
• Zero dependencies — stdlib only (pathlib, shutil, platform, subprocess, dataclasses, enum)

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Installation

Requires Python 3.11+.

pip install aidriven

Or with uv:

uv add aidriven

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Usage

Discover installed IDEs

from aidriven.discovery import discover_ides

result = discover_ides()

for ide in result.detected_ides:
    print(ide.identifier, ide.display_name, ide.version, ide.confidence)

Each DetectedIDE entry contains:

Field	Type	Description
identifier	str	Normalized IDE key (e.g. vscode, cursor, kiro)
display_name	str	Human-readable name
install_path	Path	Path to the installation
version	str | None	Resolved version string, or None if unavailable
channel	str	Variant channel (e.g. stable, insiders)
confidence	ConfidenceLevel	HIGH, MEDIUM, or LOW
detected_platform	str	Platform the detection ran on

Register a custom provider

from aidriven.discovery import discover_ides, ProviderRegistry

registry = ProviderRegistry()
registry.register(MyCustomIDEProvider())

result = discover_ides(registry=registry)

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Development

This project uses uv for dependency management.

# Install dependencies (including dev extras)
uv sync

# Run tests with coverage
uv run pytest

# Lint
uv run ruff check .

# Format check
uv run ruff format --check .

# Type check
uv run mypy src/

# Serve docs locally
uv run mkdocs serve

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Contributing

1. Fork the repository and create a feature branch.
2. Install dev dependencies and set up pre-commit hooks:

uv sync
uv tool install pre-commit
pre-commit install

3. Make your changes, add tests, and ensure all checks pass:

pre-commit run --all-files
uv run pytest

4. Open a pull request against main.

Bug reports and feature requests are welcome via GitHub Issues.

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License

MIT — see LICENSE.