| name | phase5-prove |
| description | The Mileva Method (CRISP) — Phase 5: Prove. Success validation against Phase R baseline metrics, test log review, and AI justification validation. Use after deployment. Triggers on "prove", "phase 5", "validate", "did it work", "success criteria", "measure results", or after go-live. |
Pacing Rule — One Section at a Time
Present one section, deliverable, or elicitation move at a time.
After presenting — stop. Wait for the client to respond.
Do not pre-fill and present multiple sections in one message.
Do not move to the next step until the client confirms, corrects, or gives a clear go-ahead.
The goal is a conversation, not a document dump.
If you've written more than one section without a client response in between — you've gone too far.
P — Prove: Success Validation
Did the needle move?
Project State
At the start of Phase P: read docs/crisp-state.json. Pull phases.R.baseline and phases.R.successTarget as the measurement baseline. Check phases.S.sprints to know what was built.
At the end of Phase P: update docs/crisp-state.json:
- Add
"P" to phases.complete
- Set
phases.P.complete to true
- Set
phases.P.outcome to "success", "partial", or "fail"
- Add summary notes to
phases.P.notes
This phase closes the loop back to Phase R. Three questions drive everything.
1. Did the needle move?
Pull the baseline and targets from docs/success-metrics.md and measure against them:
- Quantitative: Did each metric hit its target? (e.g. "quotes sent in 4min vs 4h before")
- Qualitative: Re-run the survey or interview from the Phase R baseline frustration score — did it improve?
- Second-order effects: Did the downstream impacts materialise? (e.g. cart abandonment dropped after checkout speed improved)
2. Was everything actually built and verified?
Read docs/test-log.md. For every sprint that shipped:
- Is there a test log entry?
- Did every specced behaviour (from the sprint's AI Spec test requirements) get a test written and run?
- Are there any ❌ failures that were never resolved?
A system where the metrics look good but tests weren't written is a system running on luck. The test log is how you know the difference between "it works" and "it worked that one time we checked."
If the test log has gaps — identify what wasn't verified and decide: is this a risk to the outcome measurement, or is it acceptable to close?
3. Did the AI justify itself?
Read docs/problem-statement.md — AI Justification section. The client agreed to custom AI because it could do something off-the-shelf products couldn't.
Ask: did that thing actually materialise?
"We said custom AI was justified because [X]. Did [X] happen? Is the client getting something from this that they couldn't get from Claude.ai at $20/month?"
If yes → close it. If no → document the gap honestly. It's a learning, not a failure — but it should be captured so the next project's justification gate is sharper.
Outcomes
Success → document what worked, close the project, capture learnings.
Partial → identify which metrics hit and which didn't. Was the target unrealistic, or did the solution miss?
Fail → return to Phase R. The problem may have been misdiagnosed, or the success criteria were wrong.
In Serbia, when things go sideways, we make jokes. It's not denial — it's perspective. Balkan humor is the ability to look at a disaster and find the absurdity before you find the culprit. Apply that here. If Phase P says fail, laugh once — then go back to Phase R with fresh eyes. A failed outcome is the most honest piece of data you'll collect in the whole project. The problem was misdiagnosed. Now you know exactly where. That's worth something.
Exit Checklist
