aroyburman-codes/pm-skills Skills

Framework for navigating AI safety, ethics, and capability trade-off discussions. Covers responsible scaling, content policy, bias, privacy, dual-use, and alignment.

Real-time competitive analysis of the AI market. Covers foundation models, products, pricing, moats, and strategic positioning across major AI labs and emerging players.

Structured teardown of AI products (ChatGPT, Claude, Gemini, Perplexity, Copilot, etc.). Analyzes product decisions, UX patterns, technical architecture, business model, and competitive positioning.

Structured analytical and metrics framework for AI product roles. Covers: metrics, goal-setting, root-cause analysis, trade-offs, A/B tests.

Structured behavioral PM framework for AI product roles. Covers: leadership stories, conflict resolution, stakeholder management.

Generate launch readiness checklists for product releases. Covers engineering, QA, design, legal, marketing, support, and rollback planning. Adapts to launch size (feature flag, beta, GA) and AI/ML-specific requirements.

Design metric dashboards and KPI tracking plans for products and features. Defines what to measure, how to measure it, alert thresholds, and dashboard layout. Covers product, business, and technical metrics.

Interactive PM mock interview simulator for AI product roles. Plays the role of interviewer, asks follow-ups, scores answers against hiring rubrics, and provides detailed feedback. Supports all round types: product sense, strategy, analytical, technical, behavioral.

Generate end-to-end PM case studies from real AI product launches, pivots, and decisions. Analyzes what happened, why, what the PM likely decided, trade-offs made, and lessons learned.

Rewrite or generate PM documents with extreme clarity. Applies principles of canonical docs, numbered lists over bullets, force-ranked priorities, BLUF structure, and visual-first communication. Transforms vague PM writing into precise, actionable docs.

Generate structured Product Requirements Documents (PRDs) from a feature brief, idea, or problem statement. Covers problem definition, user stories, requirements, success metrics, and launch plan. Adapts to AI/ML product contexts.

Score and rank features or initiatives using RICE, ICE, weighted scoring, or custom frameworks. Generates a prioritized backlog with clear rationale. Use for: sprint planning, roadmap decisions, feature trade-offs.

Structured product sense/design framework for AI product roles. Covers: design a product, improve X, productize a capability.

Structured product strategy framework for AI product roles. Covers: market entry, competitive positioning, build-vs-buy, long-term vision.

Write exec-ready stakeholder updates, decision documents, and status reports. Adapts tone and detail level for different audiences (exec team, engineering, cross-functional). Structured for fast decision-making.

Structured technical PM framework for AI product roles. Covers: RLHF, evals, RAG, LLM deployment, system design, API design.

Synthesize user interviews, survey results, feedback, and support tickets into structured insights. Identifies themes, pain points, and opportunities. Generates research reports ready for stakeholders.