Find the perfect capability for your agent.
This skill should be used when Claude Code needs to perform basic arithmetic calculations. It provides a Python script that safely evaluates mathematical expressions including addition, subtraction, multiplication, division, exponentiation, and square roots.
Implement AsyncIO patterns for I/O-bound tasks with asyncio.gather, semaphores, and FastAPI async endpoints. Use for concurrent operations, parallel HTTP fetches, rate-limited operations, or async API development. Achieves near-linear scaling with concurrent tasks and minimal overhead. Triggers on "async", "asyncio", "concurrent", "parallel requests", "FastAPI async", "aiohttp", "async Python".
Python environment setup with uv, shared utilities, and script structure guidelines.
Invoke IMMEDIATELY via python script when user requests codebase analysis, architecture review, security assessment, or quality evaluation. Do NOT explore first - the script orchestrates exploration.
Run Python code in lesson context with proper uv and venv handling for agent-spike project. Activate when user wants to run tests, demos, or CLI commands for lessons in lessons/ directories. Project-specific for agent-spike multi-agent learning.
Expert guidance for turning existing Snakemake analysis projects (with Snakefile + src/) into pip-installable CLI tools that can deploy the workflow to new projects. Use when the user has a working analysis project with Snakemake and R/Python scripts and wants to make it reusable/deployable. Creates CLI with init, validate, update, clean, info commands. Reference implementation at /Users/wolski/projects/ptm-pipeline.
CLI scripts/programs (python, js, bash, go, etc.) following modern best practices. Use when: (1) Creating new CLI tools, (2) Reviewing CLI UX/DX, (3) Implementing help/errors/output, (4) Adding flags/args/subcommands, (5) Ensuring robustness and future-proofing.
Master the uv package manager for fast Python dependency management, virtual environments, and modern Python backend project workflows. Use when setting up Python projects, managing dependencies, or optimizing Python development workflows with uv.
Python environment variable management using .env files and python-dotenv for simple projects
Build professional Python CLIs with modern UX patterns using pyfiglet (ASCII banners), typer (commands), questionary (interactions), and rich (formatting). Use when creating command-line tools, automation scripts with user interaction, data processing pipelines with CLI interfaces, or upgrading existing Python scripts to professional CLIs. Ideal for ETL workflows, GIS tools, data analysis utilities, and civic tech projects requiring reproducible, scriptable interfaces.
Invoke IMMEDIATELY via python script when user requests codebase analysis, architecture review, security assessment, or quality evaluation. Do NOT explore first - the script orchestrates exploration.
Run Python scripts with automatic dependency management using uv. Use this skill when you need to (1) run Python scripts with external dependencies without polluting system Python, (2) create standalone Python scripts with inline dependencies (PEP 723), (3) convert existing scripts to use uv with inline metadata, (4) handle ModuleNotFoundError by running scripts with uv run --with, or (5) create CLI tools, data processing scripts, or API clients that are self-contained and portable.
Use when writing async code, using asyncio.gather, handling concurrent operations. Triggers on "asyncio", "async", "await", "gather", "concurrent", "parallel", "fail-fast", "timeout", "race condition", "cancellation", "coroutine", "task", "CancelledError", or when writing async functions.
Master Python asynchronous programming with asyncio, async/await, and concurrent.futures. Use for async code and concurrency patterns.
Prepare pull request descriptions following project template. Activates on: prepare PR, create PR, pull request, ready for PR, draft PR, write PR
CDK8s for type-safe Kubernetes manifests using Python. Use when building complex K8s applications programmatically, generating manifests from code, creating reusable infrastructure patterns, or managing multi-environment deployments.
Testing best practices for modern Python development. Activated when working with tests, pytest, Hypothesis property-based testing, Atheris fuzz testing, test coverage, test organization, or mutation testing.
