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Found 121 Skills
Guidelines for Python dependency management using uv, the fast Python package installer and resolver.
Debugs and resolves common uv issues. Learn to diagnose dependency resolution failures, handle version conflicts, fix cache problems, troubleshoot Python environment issues, optimize performance, and solve platform-specific problems. Use when uv commands fail, dependencies won't resolve, cache is corrupted, Python installation issues occur, or performance is slow.
Optimize pyproject.toml and resolve complex dependency trees using modern tools like Poetry or uv. Use to modernize Python project management.
Generate AI-friendly Python CLIs using Click, Pydantic, and uv. Use when user wants to create a new CLI tool that follows best practices for agentic coding environments.
Use when working with Python projects that use uv for dependency management, virtual environments, project initialization, or package publishing. Covers setup, workflows, and best practices for uv-based projects.
Set up uv (Rust-based Python package manager) in CI/CD pipelines. Use when configuring GitHub Actions workflows, GitLab CI/CD, Docker builds, or matrix testing across Python versions. Includes patterns for cache optimization, frozen lockfiles, multi-stage builds, and PyPI publishing with trusted publishing. Covers GitHub Actions setup-uv action, Docker multi-stage production/development builds, and deployment patterns.
Initialize and configure new Python projects with uv, including creating projects, setting up pyproject.toml, managing dependency groups, and pinning Python versions. Use when starting new projects, configuring development environments, or standardizing project structure with uv. Covers `uv init`, `uv add`, `uv python pin`, and initial project scaffolding with proper dependency organization.
Bootstrap new Python projects: directory structure, pyproject.toml, pre-commit, uv sync. Use when creating a new project from scratch.
Guides the agent through running and configuring ASGI servers (Uvicorn, Granian, Hypercorn) for Python web applications. Triggered when users say "run a FastAPI app", "configure uvicorn", "set up ASGI server", "deploy with uvicorn", "configure workers", "set up SSL/TLS", "run development server", "configure hot reload", or mention ASGI server, production deployment, server configuration, uvicorn, granian, or hypercorn.
Expert guidance for building production-ready FastAPI applications with modular architecture where each business domain is an independent module with own routes, models, schemas, services, cache, and migrations. Uses UV + pyproject.toml for modern Python dependency management, project name subdirectory for clean workspace organization, structlog (JSON+colored logging), pydantic-settings configuration, auto-discovery module loader, async SQLAlchemy with PostgreSQL, per-module Alembic migrations, Redis/memory cache with module-specific namespaces, central httpx client, OpenTelemetry/Prometheus observability, conversation ID tracking (X-Conversation-ID header+cookie), conditional Keycloak/app-based RBAC authentication, DDD/clean code principles, and automation scripts for rapid module development. Use when user requests FastAPI project setup, modular architecture, independent module development, microservice architecture, async database operations, caching strategies, logging patterns, configuration management, authentication systems, observability implementation, or enterprise Python web services. Supports max 3-4 route nesting depth, cache invalidation patterns, inter-module communication via service layer, and comprehensive error handling workflows.
Upgrade Python dependencies using uv, then run post-upgrade checks to ensure nothing is broken.
Guide installing Earth2Studio via uv or pip, selecting model extras, and configuring the environment. Do NOT use for writing inference code, choosing models, or PhysicsNeMo questions.