initmahesh/mlai-community-labs/module-4-ai-assistant-for-product-manager-job-preparation

Context

Reference this file for the full workflow, prompts, and strategy framework this skill is based on:

@"Bonus - 4.5-how-to-build-strategy-using-claude-code.md"

Goal

This skill creates a personalized job interview preparation package for Product Manager roles. It handles both Round 1 (first screening call) and Round 2 (panel / deep dive) preparation by generating tailored, file-based prep materials from your resume and job description.

Behavior

Trigger this skill when a user says:

• "prepare me for my interview"
• "interview prep"
• "get me ready for my first call"
• "prep for round 2"
• "job interview strategy"
• Anything about PM interview preparation for a specific role or company

Phase 1 — Intake

Use the AskUserQuestion tool to collect inputs before generating anything.

Step 1 — Round Selection

Ask which interview round they are preparing for:

• Round 1: First Screening / Recruiter Call
• Round 2: Panel / Hiring Manager / Deep Dive
• Full Prep: Both rounds — complete package

Step 2 — Resume

Ask the user to provide their resume. Accept either:

• Pasted text
• A file reference using @filename.pdf

Step 3 — Job Description

Ask the user to paste the full job description text.

Step 4 — Round 2 Context (Round 2 or Full Prep only)

If the user selected Round 2 or Full Prep, additionally ask:

• What questions or topics came up in Round 1 (if they had it)
• Who they are meeting: Hiring Manager, peer PM, tech lead, skip-level, etc.

CRITICAL RULE: Do NOT generate any output until BOTH resume and job description are provided. Keep asking until you have both.

Phase 2 — Round 1 Output

If the user selected Round 1 or Full Prep, generate and save these 3 files to the current working directory.

File 1: interview-strategy.md

Analyze the resume and job description to produce:

Role Analysis

• What the company actually needs — decode the real signals behind the JD language
• Top 3 priorities the hiring manager cares about most

Your Positioning

• How the user's background maps to each of the top 3 priorities
• The strongest 2-3 differentiators from their resume for this specific role

Gap Analysis

• Any skill or experience gaps between the resume and the JD requirements
• Concrete suggestions for how to address or reframe each gap in the interview

Resume Tailoring Tips

• 3-5 specific edits to the resume to improve alignment with this JD
• Keywords to add or emphasize

Cover Letter Angle

• The single story or thread that should anchor the cover letter for this role

Likely Hiring Manager Questions

• 10-15 questions the HM will probably ask based on the JD and the user's background
• For each: a coached answer framework referencing specific resume content

Pre-Interview Research Checklist

• Company product, recent news, competitors, team, and strategic direction
• 5-7 targeted research questions to answer before the interview

File 2: first-call-stories.md

Generate 5 compelling STAR-format stories pulled directly from the resume. For each story:

• Story Title — a short, memorable label
• Competency Mapped — which specific JD requirement this story addresses
• Situation — the context and stakes
• Task — the user's specific responsibility
• Action — the decisions and steps taken (most important section — be specific)
• Result — quantifiable outcomes and business impact
• Bridge — 1-2 sentences connecting this story to what THIS company needs
• Follow-up Talking Points — 2-3 likely follow-up questions and brief answer guides

Each story should take 2-3 minutes when spoken aloud. Include a word-count estimate.

Stories must be grounded in real resume content. Do not fabricate metrics.

File 3: project-discussion-points.md

Generate structured discussion frameworks for 5 major projects from the resume. For each project:

• Project Name & Company
• Project Overview & Context — 2-3 sentences on what this was and why it mattered
• Your Role & Responsibilities — what you specifically owned
• Key Challenges & How You Overcame Them — the hardest 1-2 problems faced
• Technologies, Tools & Methodologies Used
• Quantifiable Results — metrics, KPIs, business outcomes
• Lessons Learned — what you would do differently
• PM Competency Demonstrated — explicitly map to JD requirements
• Interview Talking Points — 3-4 bullet points to hit when discussing this project

Phase 3 — Round 2 Output

If the user selected Round 2 or Full Prep, generate and save this file:

File: round2-prep.md

Section 1 — Deep-Dive Behavioral Questions

Generate 10 hard behavioral and product questions based on the senior-level requirements in the JD. For each question:

• The question (written exactly as an interviewer would ask it)
• Why they ask this — what signal the interviewer is looking for
• Coached answer framework — how to structure the answer using the user's actual resume experience
• Trap to avoid — the most common wrong answer for this question

Section 2 — Case Study Scenarios

Build 2 mini product case studies tailored to the company's product area and the challenges implied by the JD:

For each scenario:

• Problem statement (grounded in what the company actually works on)
• How to structure the answer — a step-by-step framework
• Key metrics to discuss — what success looks like for this type of problem
• Trade-offs to address — the tensions the interviewer wants to see you navigate
• Likely follow-up probes — 2-3 follow-up questions to anticipate

Section 3 — Technical / Product Strategy Scenario

For AI PM or technical PM roles, generate one product or system design scenario relevant to the company's product space:

• Scenario prompt — written as the interviewer would present it
• Structured walkthrough:

1. Clarify requirements and constraints
2. Define success metrics
3. High-level design or product approach
4. Key trade-offs and decisions
5. Risks and mitigations

• What strong answers include — 3-4 signals that show senior-level thinking

For non-technical PM roles, replace with a prioritization or strategy scenario instead.

Section 4 — Strategic Questions to Ask Interviewers

Generate 5 sharp, specific questions the user should ask — each covering a different dimension:

1. Product Direction — A question about technical or product strategy that shows you've done homework
2. Team & Ways of Working — A question about how the team operates, makes decisions, or handles disagreement
3. Success in the Role — "What does great look like in the first 90 days?"
4. Biggest Product Challenge — A question that invites the interviewer to talk about their hardest unsolved problem
5. What Separates Top Performers — A question about what distinguishes the best PMs on the team

For each question: include a note on why this question is powerful and what a strong answer from the interviewer looks like.

Rules

• Do NOT generate generic responses — every output must reference specific details from the resume and JD
• Do NOT skip the intake phase — gathering resume + JD is mandatory before any generation
• Think like: senior PM hiring manager + executive recruiter + career coach, all in one
• For AI PM roles, go deeper on: agent architectures, evaluation frameworks, RAG/knowledge graphs, LLM product metrics, safety and guardrails, 0→1 model-integrated products
• All output files go to the current working directory
• Writing style: direct, specific, no hedging — strip all filler language
• Stories and project points must reference real metrics or resume content; do not fabricate numbers
• If the resume or JD is vague, ask one targeted clarifying question before generating, rather than guessing

Output Checklist

Before completing, verify:

• [ ] interview-strategy.md created (Round 1 / Full Prep)
• [ ] first-call-stories.md created with 5 STAR stories (Round 1 / Full Prep)
• [ ] project-discussion-points.md created with 5 projects (Round 1 / Full Prep)
• [ ] round2-prep.md created with all 4 sections (Round 2 / Full Prep)
• [ ] Every section references specific resume content — no generic statements
• [ ] Every section references specific JD requirements — mapped explicitly
• [ ] No fabricated metrics, names, or company details
• [ ] Files saved to current working directory with correct filenames