pm-interview
Plan and review product scope, value, metrics, and rollout via a structured interview. Use when product direction or scope must be clarified.
Best use case
pm-interview is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Plan and review product scope, value, metrics, and rollout via a structured interview. Use when product direction or scope must be clarified.
Teams using pm-interview should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/pm-interview/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How pm-interview Compares
| Feature / Agent | pm-interview | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Plan and review product scope, value, metrics, and rollout via a structured interview. Use when product direction or scope must be clarified.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 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) ```md ## 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. <!-- skill-score-boost-v1 --> ## 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.
Related Skills
flow-interview
Interview user in-depth about a spec, bead, or feature idea to extract complete implementation details. Use when user wants to flesh out a spec, refine requirements, discuss technical details, clarify a feature before building, or asks to be interviewed about their idea. Triggers on "interview me", "ask me questions about", "flesh out this spec", "refine requirements", "help me think through".
async-interview-automation
Automate Async Interview tasks via Rube MCP (Composio). Always search tools first for current schemas.
agent-ops-interview
Conduct structured interviews with the user. Use when multiple decisions need user input: ask ONE question at a time, wait for response, record answer, then proceed to next question.
interviewing-stakeholders
Conducts comprehensive stakeholder interviews to gather detailed requirements and generate Gherkin (Given/When/Then) acceptance criteria for test-driven development. Use this skill when you need to: (1) Clarify ambiguities or gaps identified in the synthesizing-requirements output, (2) Interview users to understand functional requirements and system behaviors, (3) Explore non-functional requirements like performance, security, scalability, and reliability constraints, (4) Understand user personas, use cases, and user journeys in detail, (5) Identify technical constraints, integration requirements, and technology preferences, (6) Define data requirements including structures, relationships, and validation rules, (7) Generate comprehensive Gherkin scenarios for each feature ready for test implementation, or (8) Start from scratch when no prior synthesis exists and you need to gather requirements through conversation. This skill typically follows synthesizing-requirements in the Pact workflow but can be used independently.
spec-interview
通过系统性访谈完善技术规格文档,访谈完成后自动创建 OpenSpec proposal。适用于需求细化、技术方案设计、规范驱动开发等场景。
interview-skills
Frameworks for technical interviews and salary negotiation. Use for behavioral interview prep (STAR method), technical interview communication, offer evaluation, and compensation negotiation strategies.
interview-framework
Adaptive brainstorming-style dialogue for all spec phases (Understand, Propose Approaches, Confirm & Store)
discovery-interview
Deep interview process to transform vague ideas into detailed specs. Works for technical and non-technical users.
persona-interview
通过深度访谈生成用户"人格画像",让AI从被动工具变为主动协作者。当用户希望AI更好地理解自己、获得个性化建议、或让AI主动发现盲区时使用。核心价值:不是简历式自我介绍,而是诊断性画像+AI行动指南。适用于个人成长、职业规划、产品开发等场景。
interview-me
Deep-dive spec interviewer. Reads a file or requirement, analyzes it against the codebase, then conducts a rigorous 1-on-1 interview using AskUserQuestion to produce a comprehensive, opinionated specification document. Acts as a collaborative architect with active pushback.
bgo
Automated Blender build-go workflow. Automatically builds, removes old version, installs, enables, and launches Blender with your extension/add-on. Use when you want to quickly test changes, execute complete build-to-launch cycle, or run custom packaging scripts with automatic Blender launch.
jj-workflow
Jujutsu atomic workflow with full operational reference for jj-based repositories.