Pm Interview

pm-interview (wrapper)

Use Interview Kernel rules, state model, synthesis, and approval gate. Kernel-enforced: Question validity gate, DISCOVER vs DECIDE intent switch, Decisions table, and Assumptions register + approval.

What this wrapper optimizes for

• Minimal scope that proves value (MVP discipline)
• Measurable outcomes (primary metric + guardrail)
• Explicit non-goals
• Rollout and stakeholder tradeoffs

Interaction notes

• Must use AskUserQuestion-style multiple choice (3–5 options, include a recommended default).
• In Delta mode (existing PRD/notes), do not re-ask settled decisions; fill gaps and force tradeoffs.

User profile alignment (Jamie)

Follow ~/.codex/USER_PROFILE.md: single-threaded, explicit steps, low cognitive load. Keep one question per turn and map any free-text reply to the closest option with confirmation.

Philosophy

• Product clarity beats feature volume. Focus on the smallest scope that proves value.

Anti-patterns

• Expanding scope without a tradeoff or measurable outcome.
• Accepting “just one more” without cutting something else.
• Treating stakeholder requests as requirements without validation.
• Shipping without an explicit “out-of-scope” list.
• Letting roadmap items override core user value.

Variation

• Tailor questions to the product surface and user segment; avoid generic prompts.
• Force the tradeoff that matches the current dominant risk (time, UX, tech debt, adoption).

Empowerment

• Make tradeoffs explicit so stakeholders choose with full context.
• Provide a “defer to v2” path that records the decision and impact.
• Require stakeholders to pick what gets removed when scope expands.

Default mode + intent

• Mode: standard
• Intent: start DISCOVER, then DECIDE for scope/tradeoffs

Scope and triggers

• Defining product scope, value, and success metrics.
• Validating a roadmap item before committing engineering time.
• Forcing tradeoffs with stakeholders.

Constraints / Safety

• Redact secrets/PII by default.
• Do not expand scope without removing something else.
• Do not proceed without an explicit out-of-scope list.

PM spine (10 prompts)

Ask in order, skipping anything already answered by context.

1. Target user / segment

• Who is this for?

2. Situation / trigger

• When does the problem occur (what are they doing when this matters)?

3. PAS: Problem (observable)

• What is the observable problem?

4. PAS: Amplify (cost of problem)

• Options: time saved / revenue risk / churn / frustration / support load / compliance risk.

5. Value hypothesis

• What changes in user outcome if we succeed?

6. Success metric

• Pick one primary success metric + optionally one guardrail metric.

7. Activation / distribution path

• How will users discover/start using this? (in-product, docs, sales-led, ops process, other)

8. Scope boundary (MVP one sentence)

• Minimal version that delivers value.

9. Non-goals

• Name one thing we will NOT do in this iteration.

10. Tradeoff + rollout posture (DECIDE)

• Optimize for: fastest ship vs best UX polish vs most future-proof
• Release: everyone immediately vs staged rollout vs behind a flag

PM synthesis add-on (append after Kernel synthesis)

## PRD-lite Addendum
- Target user:
- Job-to-be-done:
- Problem (observable):
- Value hypothesis:
- Primary metric:
- Guardrail metric (optional):
- Activation/distribution:
- Release strategy:
- Open questions for later:

Required inputs

• User request details and any relevant files/links.

Deliverables

• Kernel synthesis + PRD-lite Addendum.
• Include schema_version: 1 if outputs are contract-bound.

Validation

• Fail fast and report missing inputs before proceeding.

Examples

• "Help me scope a new onboarding flow and define success metrics."
• "Pressure-test this roadmap item and force tradeoffs."

References

• references/contract.yaml (output contract)
• references/evals.yaml (quality checks)

Procedure

1. (Optional) Delta scan: extract what’s already decided.
2. Execute the kernel interview loop using the PM spine.
3. Synthesize outputs + approval gate.
4. Handoff to planning/execution using the approved PRD-lite.

Remember

The agent is capable of extraordinary work in this domain. These guidelines unlock that potential—they don't constrain it. Use judgment, adapt to context, and push boundaries when appropriate.

Philosophy and tradeoffs

• Use this skill when consistent decision-making matters more than one-off execution because project context should drive the approach.
• Principle and mindset: prioritize tradeoffs and constraints over rigid checklists; understand why each step exists.
• Ask this to keep outcomes robust: Why is this the right default, and what could change this outcome?
• How do we adapt if constraints shift?
• What evidence is needed before choosing one path over another?

Anti-patterns and caveats

• Avoid applying this playbook generically without checking repository-specific context.
• NEVER skip required validation gates when behavior changes.
• DO NOT use this skill as a rigid replacement for engineering judgment.
• DON'T ignore warnings or assume one pattern fits all repos.
• Common pitfall: treating anti-patterns as optional.
• Incorrect assumptions here can lead to fragile guidance.
• Warning: wrong sequencing can create avoidable regressions.

Variation and adaptation

• Vary the workflow by team size, risk, and deployment target.
• Use different strategies for small, medium, and large changes.
• Adapt recommendations to the specific environment and avoid repetitive templates.
• Avoid generic or cookie-cutter responses; craft context-specific alternatives.
• Keep outputs diverse and not repetitive.
• Converge on a custom path only after evidence review.
• Different constraints should produce different, non-generic recommendations.

Empowering execution style

• Be capable of exploring multiple options and enabling the team to make safe decisions.
• Unlock confidence by explaining options and tradeoffs clearly.
• Feel free to be creative while staying rigorous and precise.
• Push boundaries with practical alternatives when simple recipes fail.
• Enable outcomes-oriented problem solving.