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static-deploy

Standardize and execute static deploy tasks for Python CLIs.

515|208|Updated Jul 6, 2015
Search Tags:#automation#devops#python#cli#best-practices#static-deploy
One-click install
npx skills add https://github.com/Opentrons/opentrons --skill static-deploy
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: static-deploy
Source: https://github.com/Opentrons/opentrons/tree/main/.cursor/skills/static-deploy
Command: npx skills add https://github.com/Opentrons/opentrons --skill static-deploy

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Conventions for static deploy automation Python CLIs in scripts/static-deploy/. Use when working with deployment scripts, AWS S3/CloudFront automation, or Python CLIs in the static-deploy directory.

Core Features & Use Cases

  • Establishes typing everywhere (arg/return types, TypedDict/Protocol/Enum) and Google-style docstrings on public functions/classes to improve readability and tooling.
  • Enforces small, pure functions, frozen dataclasses for config, pathlib.Path usage, and deterministic subprocess.run invocations with structured results.
  • Provides a consistent CLI shape via argparse, a --dry-run option, AWS profile support, and CI-ready exit codes; includes architecture for Makefile entries and testability with pytest; isolates AWS calls for testability.
  • Applicable for deploying static assets to cloud storage, performing build-time validations, and safely executing deployment pipelines.

Quick Start

Run the static-deploy CLI against your scripts/static-deploy directory with --dry-run to test deployment workflows safely.

Frequently Asked Questions about static-deploy

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I standardize Python CLI scripts for static deployment to AWS S3 and CloudFront?▼

Automate static deploy tasks for Python CLIs by applying strict typing, frozen dataclasses, and deterministic subprocess execution for AWS S3 and CloudFront workflows. This ensures consistent configuration, readable code, and testable deployment pipelines.

What is the best way to test Python deployment scripts safely before running them against AWS S3?▼

Use the --dry-run option provided by the standardized argparse CLI to test static deployment workflows safely. AWS calls are isolated for testability, allowing you to validate build-time tasks and configurations with pytest before executing actual cloud operations.

How do I structure Python CLIs to ensure deterministic subprocess execution during build-time tasks?▼

Structure Python CLIs by enforcing small, pure functions, frozen dataclasses for configuration, and deterministic subprocess.run invocations with structured results. This approach guarantees predictable execution and improves testability for static deploy automation.

Can I use argparse and TypedDict to standardize static asset deployment configurations in Python?▼

Yes, standardize static asset deployment by establishing typing everywhere with TypedDict, Protocol, and Enum, alongside a consistent argparse CLI shape. This provides clear argument parsing and structured configuration management for AWS deployment workflows.

Does this static deploy standardization support CI-ready exit codes and AWS profile selection?▼

Yes, the standardized Python CLI shape includes AWS profile support and CI-ready exit codes. This allows safe integration into continuous deployment pipelines while maintaining strict typing and deterministic execution for S3 and CloudFront automation.

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