Can I use this to fix 'Does not appear to be a Python project' errors?

Yes, it contains specific debugging patterns to resolve missing pyproject.toml or setup.py issues by ensuring the installer runs from the correct repository root.

Is the AGILAB installation process idempotent?

Yes, a core rule of this skill is ensuring that rerunning the installer does not wipe existing user data or perform redundant downloads.

What is the primary purpose of the AGILAB Installer skill?

It provides specialized guidance for installing the AGILAB platform, managing app-specific pages, and troubleshooting installation or test failures.

Does this skill support non-interactive installations?

Yes, it includes guidance for running install scripts with flags like --non-interactive and --cluster-ssh-credentials for automated setups.

How does it handle Python dependency management?

The skill follows best practices using uv preview features to handle extra build dependencies and ensure stable, reproducible environments.

AGILAB Installer

Name: AGILAB Installer
Author: ThalesGroup

byThalesGroup

•

Deployment & DevOps

Automates the installation and configuration of the AGILAB engineering platform and its associated AI applications.

About

The AGILAB Installer skill provides specialized guidance for deploying and maintaining the AGILAB AI-driven engineering environment. It streamlines the execution of root installation scripts, app-specific setup processes, and data seeding protocols while ensuring idempotent deployments. This skill is particularly useful for debugging common environment issues, such as missing Python manifests or dataset extraction failures, and enforces best practices like using uv for dependency management and handling inter-app data dependencies through efficient symlinking.

Key Features

Advanced debugging patterns for Python project and manifest errors.
Inter-app dependency management via intelligent symlinking and post-install hooks.
Idempotent data seeding and dataset archive management.
Automated root and app-level installation guidance for macOS and Linux.
Optimized dependency resolution using uv preview features.
3 GitHub stars

Use Cases

Debugging failed application installations or missing worker/manager manifests.
Setting up a fresh AGILAB environment for AI engineering experimentation.
Configuring data dependencies between multiple engineering applications without duplicating storage.

About

Key Features

Advanced debugging patterns for Python project and manifest errors.
Inter-app dependency management via intelligent symlinking and post-install hooks.
Idempotent data seeding and dataset archive management.
Automated root and app-level installation guidance for macOS and Linux.
Optimized dependency resolution using uv preview features.
3 GitHub stars

Use Cases

Debugging failed application installations or missing worker/manager manifests.
Setting up a fresh AGILAB environment for AI engineering experimentation.
Configuring data dependencies between multiple engineering applications without duplicating storage.