How does it handle troubleshooting?

It provides specific checks for common issues like missing imports in worker scopes and virtual environment setup errors.

Can I use this skill for publishing packages?

Yes, it includes runbook commands for dry-running and executing releases to PyPI or TestPyPI while maintaining versioning standards.

Why does this skill require the uv package manager?

AGILab uses uv for high-performance dependency resolution and managed environments to ensure reproducibility across different AI engineering experiments.

What is the AGILab Runbook skill?

It is a specialized set of instructions for Claude Code to manage the AGILab repository, focusing on environment setup, UI development, and deployment.

How does this skill help with Streamlit development?

It enforces the use of modern Streamlit APIs and provides specific commands for launching the Dev UI with the correct app paths and configurations.

AGILab Runbook

Name: AGILab Runbook
Author: ThalesGroup

byThalesGroup

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Data Science & ML

Standardizes repository workflows for AGILab development, environment management, and AI engineering application deployment.

About

The AGILab Runbook skill provides Claude with domain-specific guidance for navigating and maintaining the AGILab repository, a platform dedicated to exploring AI-driven engineering methods. It ensures consistent use of the uv package manager, automates the generation of run-configuration wrappers, and enforces best practices for Streamlit UI development and multi-layered dependency management. This skill is essential for developers looking to contribute to the AGILab project while adhering to specific engineering policies, such as fail-fast API interactions and standardized publishing workflows.

Key Features

Automated uv environment management for Streamlit and Python execution
3 GitHub stars
Run-configuration wrapper regeneration for IDE parity
Dependency troubleshooting for multi-worker application architectures
Standardized Streamlit API implementation and UI smoke testing
Streamlined PyPI publishing workflows and release preparation

Use Cases

Launching the AGILab developer interface with specific API and app directory configurations
Debugging missing imports across manager and worker pyproject scopes
Synchronizing local run configurations after editing IDE project files

About

Key Features

Automated uv environment management for Streamlit and Python execution
3 GitHub stars
Run-configuration wrapper regeneration for IDE parity
Dependency troubleshooting for multi-worker application architectures
Standardized Streamlit API implementation and UI smoke testing
Streamlined PyPI publishing workflows and release preparation

Use Cases

Launching the AGILab developer interface with specific API and app directory configurations
Debugging missing imports across manager and worker pyproject scopes
Synchronizing local run configurations after editing IDE project files