Product Analytics Agent Skills

Provides strategic product leadership guidance for CPOs and product leaders, covering vision, portfolio management, product-market fit, org design, and metrics. Includes diagnostic questions, frameworks, and scripts for scoring PMF and analyzing portfolios.

Compute fully-loaded cost-to-serve per channel, channel ROI under cash/LTV/marginal lenses, and optimal channel mix subject to strategic constraints for quarterly channel reviews.

Define, track, and interpret product metrics across discovery, growth, and mature product stages using frameworks like AARRR, North Star, and HEART.

Analyze sprint data with Python scripts for velocity forecasting, health scoring, and retrospective insights to coach agile teams.

Design, prioritize, and evaluate product experiments with clear hypotheses and defensible decisions, including A/B testing, sample size estimation, and statistical interpretation.

Analyzes competitor products by synthesizing data from pricing pages, app store reviews, job postings, SEO signals, and social media into structured competitive intelligence. Produces feature comparison matrices, SWOT analyses, positioning maps, UX audits, pricing breakdowns, action plans, and presentations.

Document end-to-end business processes in BPMN-style notation, measure cycle times by stage, surface where work spends most of its time waiting vs. being worked, and quantify the gap between processing time and total elapsed time.

Analyze user engagement and retention patterns by cohort to identify trends in user behavior, feature adoption, and long-term engagement.

Conduct a structured SWOT analysis for a product, identifying strengths, weaknesses, opportunities, and threats with actionable strategic recommendations.

Identify a North Star Metric and 3-5 supporting input metrics, classify the business game (Attention, Transaction, Productivity), and validate against seven criteria for an effective North Star.

Design a detailed value proposition using a 6-part Jobs-to-be-Done template covering who, why, current situation, solution, improved outcome, and alternatives.

Design a comprehensive product metrics dashboard with key metrics, data sources, visualization types, and alert thresholds.

Provides evidence-first revenue, pricing, refunds, team-billing, and billing-model truth workflows for sales snapshots, pricing comparisons, duplicate-charge diagnosis, or code-backed billing reality.

Audit and optimize App Store and Google Play listings to lift installs and conversion.

Design and optimize revenue operations systems that connect marketing, sales, and customer success into a unified revenue engine.

Analyze AI disruption pressure across a business, map competitive exposure, and produce a 90-day defensive action plan.

Pressure-test any business idea with a five-angle adversarial panel (Critic, Champion, Analyst, Investigator, Customer) that returns a single GO/RESHAPE/KILL verdict with the cheapest test to de-risk it.

Design value-based pricing, packaging, and monetization strategies for SaaS products, including tier structures, value metrics, and price optimization.

Set up, improve, or audit analytics tracking and measurement for marketing and product decisions.

Generates a concise, executive-ready performance summary covering key metrics, trends, and what's driving movement, using Adobe Analytics data.

Analyze Sentry session replays to surface UX patterns, pain points, and user journeys for a given product area.

Design MVPs, validated learning experiments, and pivot-or-persevere decisions using the Build-Measure-Learn loop and innovation accounting.

Choose and audit startup metrics using the Lean Analytics framework: separate actionable metrics from vanity metrics, identify the One Metric That Matters for your business model and stage, set targets, and plan instrumentation.

Diagnose and fix retention problems using the Fogg Behavior Model (B=MAP), covering motivation, ability, prompts, and tiny habits.

Define product positioning by mapping competitive alternatives, unique attributes, and best-fit customers to the right market category using a structured canvas and 5-step process.

Build a weekly cadence of customer touchpoints using Opportunity Solution Trees, assumption mapping, and interview snapshots to keep product decisions grounded in evidence.

Design products and pricing around validated willingness to pay, using the framework from Ramanujam & Tacke's "Monetizing Innovation" to avoid common monetization failures.

Review designs, products, and features using Steve Jobs' standards of ruthless simplicity, focus, and end-to-end excellence.

Formulate and audit real strategy using Richard Rumelt's framework: an honest diagnosis, a guiding policy, and coherent action instead of goals, vision, and wishful thinking.

Create data-backed customer personas with market research, demographics, psychographics, jobs-to-be-done, journey mapping, and avatar generation.

Guide product managers through a complete discovery cycle from problem hypothesis to validated solution using problem framing, customer interviews, synthesis, and experimentation.

Guide product managers through discovering and articulating product positioning by asking adaptive questions about target customers, unmet needs, product category, benefits, and competitive differentiation.

Diagnose which organic growth path to pursue by identifying where the growth constraint lives, using a fast triage based on the McKinsey Growth Pyramid.

Calculate TAM, SAM, and SOM with explicit assumptions, methods, and caveats for market sizing.

Identify what to measure, test, or track to de-risk a product or AI idea across internal and external risk dimensions.

Evaluate feature investments using revenue impact, cost structure, ROI, and strategic value to make data-driven build/don't-build decisions.