full-cycle-dev-workflow

A repeatable end-to-end software development workflow orchestrates planning, building, testing, reviewing, releasing, and reflecting into a seven-stage loop to reduce ambiguity and drift across project delivery.

You can structure feature development for Python and PyTorch ML projects by applying a seven-stage loop: Think, Plan, Build, Review, Test, Ship, and Reflect, emphasizing TDD and release discipline.

Yes, this dev workflow enforces testing and code review gates by applying verification checkpoints and release hygiene within its seven-stage loop before allowing any software to ship.

Yes, you can use this workflow for debugging sessions, PR reviews, and architecture planning, because it guides each phase with structured reflection and verification before proceeding.

The best way to conduct a software project retrospective after a release is to trigger the Reflect stage of the workflow loop, capturing lessons to improve future feature development and testing.

You need an orchestrated workflow for software delivery when your projects suffer from ambiguity and drift across planning, building, testing, reviewing, and releasing, requiring strict verification gates to maintain quality.