wu5 dev flow

Software projects often fail due to drift between specifications, tests, and implementation. This skill enforces a rigorous, state-machine driven workflow that prevents unverified code from reaching your main branch.

What it does

• Enforce SDD/TDD: Requires Software Design Documents and Test-Driven Development cycles for every functional change.
• Automated Gates: Uses a dedicated Python script to verify project state, approve baselines, and check commit readiness.
• Zero-Hardcode Policy: Scans and blocks implementation of hardcoded keys, environment-specific paths, or business thresholds.
• State Persistence: Tracks progress in state.json and status.md to maintain context across different sessions or agents.
• Atomic Commits: Handles local git staging and commits only after rigorous verification and review cycles.

How it works

1. Initialize: Run wu5_flow.py init to scaffold the specification directory and baseline the existing codebase.
2. Define: Create a new change slug and draft the proposal.md and plan.md for user approval.
3. Develop: Execute the Red-Green-Refactor loop, ensuring tests fail as expected before writing business logic.
4. Verify: Run automated checks for types, linting, and test coverage, followed by a two-stage manual review gate.
5. Ship: Archive the change, update the feature documentation, and prepare the final PR summary.

Frameworks & tools

Specifically designed for Python environments using pytest, pyright, and ruff. It integrates directly with Git for workflow enforcement and utilizes custom Python scripts for state management.

Why this beats prompting it yourself

Prompting an agent to "follow TDD" often results in shortcuts or forgotten steps over long sessions. This skill uses a hard-coded state machine and external script validation that refuses to proceed if specifications are unapproved or tests are missing.

Use cases

• Initializing a new Python project with strict architectural standards.
• Executing high-stakes refactoring where regression is not an option.
• Onboarding agents to existing repositories while maintaining a verifiable audit trail.
• Managing complex feature additions that span multiple work sessions.

Known limitations

Does not handle database schema migrations or irreversible data transformations. Requires local Python environment to run the gatekeeper scripts.