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name: django-spike-despike-workflow description: "Manage Django spikes and de-spiking with tests: branch experiments, exploratory code, learning capture, functional tests against spiked behavior, revert-and-rebuild, custom auth flows, email/token models, and dependency seams. Use when a Django feature was prototyped quickly and needs to be turned into production-quality tested code." license: MIT compatibility: Codex, Claude Code, and other Agent Skills-compatible clients. metadata: version: "0.1.0" displayName: Django Spike De-Spike Workflow category: Django tags: django,tdd,spikes,auth,testing

Django Spike De-Spike Workflow

Use this skill when exploratory Django code has answered a question but is not yet fit to keep. Preserve the learning, throw away accidental design, and rebuild the feature with tests that express the real behavior.

Source Traceability

Primary source: Harry Percival, Test-Driven Development with Python, 3rd ed. Guidance is transformed and paraphrased from chapters 19 and 20, especially passwordless authentication, branch-based spikes, de-spiking, custom user/token models, email flow tests, and introducing mocks only at external boundaries.

Workflow

1. Label the spike.

   • Identify what question it answered.
   • Separate facts learned from code to keep.
   • Save notes, screenshots, shell commands, or minimal examples if they matter.

2. Write behavior from the spike.

   • Convert the useful behavior into a functional or integration test.
   • Keep the test user-facing when the spike proved a workflow.
   • Add lower-level tests for token models, forms, views, or email boundaries.

3. Revert or quarantine the spike.

   • Revert the exploratory branch or isolate it from production code.
   • Rebuild in small red/green/refactor steps.
   • Keep commits narrow enough to review.

4. Introduce seams deliberately.

   • Use fakes or mocks only for external email/service boundaries.
   • Keep Django auth and model behavior real unless the test boundary says otherwise.

Read spike-despike-patterns.md for branch discipline, auth-flow slicing, and de-spiking checklists.

Decision Rules

• If the spike is mostly UI flow, start de-spiking from a functional test.
• If the spike proved a model or token rule, write model tests before rebuilding views.
• If the spike touched email, test the message boundary without hitting real email services.
• If the spike changed authentication models, keep migration and compatibility risks explicit.
• If the spike's code is messy but behavior is right, prefer rebuild over incremental cleanup.

Guardrails

• Do not merge spike code just because it works once.
• Do not preserve hardcoded secrets, magic tokens, or one-off settings from the spike.
• Do not mock away Django authentication behavior when auth integration is the point.
• Do not lose the learning when reverting the code.

Verification

Before finishing, report:

• Spike question and learning.
• Tests that capture intended behavior.
• What spike code was reverted, discarded, or rebuilt.
• External boundaries mocked or faked.
• Focused Django test command and result.