django-spike-despike-workflow

Development

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.

QUICK START

How to use this skill

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Source SKILL.md: https://github.com/hashgraph-online/awesome-codex-plugins/blob/HEAD/plugins/LVTD-LLC/skills/skills/django-spike-despike-workflow/SKILL.md

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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

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.
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.
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.
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.