{"id":"543dfd67-3526-46cd-b767-c748b916a061","shortId":"REafUK","kind":"skill","title":"behavioral-pm","tagline":"Structured behavioral PM framework for AI product roles. Covers: leadership stories, conflict resolution, stakeholder management.","description":"# Behavioral PM Skill\n\nApply a structured framework to PM behavioral questions targeting AI product roles.\n\n## When to Use\n- User asks \"Tell me about a time when...\"\n- User asks about conflict, failure, leadership, influence, ambiguity\n- User asks \"Why this company?\" or \"Why PM?\" or \"Why AI?\"\n- User says `/behavioral-pm` followed by a question\n- Any behavioral, situational, or \"tell me about yourself\" question\n\n## Context\n- **Tuned for**: AI product roles at frontier AI companies\n- **What matters**: Intellectual humility, comfort with ambiguity, collaborative leadership, and genuine passion for AI's impact on the world.\n- **Key difference from big tech**: AI companies care less about \"driving results at scale\" and more about \"navigating uncertainty with good judgment\" and \"working effectively with researchers.\"\n\n## Values by AI Company Archetype\n\n### The Capability-Focused Lab\n- Bias toward action and ambition\n- Move fast, be bold, push the frontier of what's possible\n- Comfort with rapid pivots and high-stakes decisions\n- Collaborative with researchers\n\n### The Safety-Focused Lab\n- Safety-first mindset, intellectual rigor\n- Careful, principled, thoughtful approach\n- Willingness to slow down when safety demands it\n- Strong opinions loosely held\n\n### The Research-First Lab\n- Scientific rigor, research excellence\n- Solve fundamental problems, then apply them broadly\n- Bridging research and product\n- Long-term thinking over short-term wins\n\n## Framework: Enhanced STAR\n\n### Structure (Proportions Matter)\n- **Situation** (10%): Set the scene concisely. Company, role, stakes.\n- **Task** (10%): Your specific responsibility. What was YOUR job here?\n- **Action** (60%): The meat. What YOU specifically did. Decisions, trade-offs, influence tactics.\n- **Result** (15%): Quantifiable outcomes. Business impact. What changed.\n- **+ Reflection** (5%): What you learned. What you'd do differently. How it shaped your PM philosophy.\n\n### The Reflection Step\nAfter every STAR answer, add one of:\n- **Growth signal**: \"If I faced this again, I'd...\"\n- **Pattern recognition**: \"This taught me a general principle about...\"\n- **Company connection**: \"This is why I'm drawn to [company] — because...\"\n\n## Common Behavioral Categories\n\n### 1. Leadership & Influence (No Authority)\n- How you aligned cross-functional teams\n- Influencing engineers/researchers who disagreed\n- Driving decisions when you weren't the decision-maker\n- *In AI orgs*: Working with PhD researchers who have deep domain expertise\n\n### 2. Conflict & Difficult Stakeholders\n- Navigating disagreements with senior leaders\n- Managing competing priorities across teams\n- Saying no to important people\n- *In AI orgs*: Balancing safety concerns vs. shipping pressure\n\n### 3. Failure & Learning\n- A time something went wrong and how you recovered\n- Making a bad product decision and what you learned\n- A project that got killed or pivoted\n- *In AI orgs*: Intellectual humility and learning velocity matter most\n\n### 4. Ambiguity & Strategy\n- Making decisions with incomplete information\n- Defining a product direction in a new space\n- Navigating rapidly changing technical landscape\n- *In AI orgs*: The field changes weekly — staying calibrated matters\n\n### 5. Technical Collaboration\n- Working closely with ML engineers or researchers\n- Translating technical constraints into product decisions\n- Building trust with deeply technical teams\n- *In AI orgs*: PMs must earn credibility with researchers\n\n### 6. Impact & Execution\n- Shipping something that moved a key metric significantly\n- Scaling a product from 0→1 or 1→100\n- Making trade-offs between speed and quality\n- *In AI orgs*: Operating at startup speed with enterprise stakes\n\n## Anti-Patterns to Avoid\n- **Too generic**: \"I communicated clearly and it worked out\" — be SPECIFIC\n- **Hero narrative**: \"I single-handedly saved the project\" — show collaboration\n- **No numbers**: Always quantify results (users, revenue, latency, accuracy)\n- **No vulnerability**: Especially at safety-focused labs — show intellectual humility\n- **Recency bias**: Have stories from different roles/contexts ready\n- **No \"why AI\"**: Every answer should subtly reinforce why you belong at an AI company\n\n## Reusable Story Themes\nStrong behavioral answers draw from a bank of 6-8 real experiences that map to multiple categories:\n\n| Story Theme | Maps To |\n|------------|---------|\n| Navigating conflict with senior stakeholder | Leadership, Conflict, Influence |\n| Shipping under extreme ambiguity | Ambiguity, Execution, Strategy |\n| Technical deep-dive that changed direction | Technical Collaboration, Learning |\n| Product failure and recovery | Failure, Resilience, Growth |\n| Cross-functional alignment on hard trade-off | Leadership, Strategy, Execution |\n| Going deep on AI/ML to earn researcher trust | Technical, Why AI, Collaboration |\n\n## Output Format\nStructure as a polished narrative. The enhanced STAR format should feel natural, not mechanical. Aim for ~400-500 words. Include the reflection/growth signal at the end.\n\n## Research-First Workflow\nBefore generating the answer:\n1. **Research** — Search for the specific company's leadership principles, recent blog posts about culture, and interview tips from current/former employees.\n2. **Tailor** — Map the story to the specific company's values.\n3. **Display** the complete enhanced STAR answer.\n\n## What Good Looks Like\n- Story is specific with real details (names/roles can be anonymized)\n- Action section is 60%+ of the answer\n- Results are quantified\n- Shows self-awareness and growth\n- Connects naturally to why this company/role\n- Demonstrates the specific leadership quality being tested\n- Shows comfort working with deeply technical 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