Skill UI

A sophisticated tool for commercial leaders preparing board-ready forecasts. It goes beyond single-number projections by decomposing pipeline into commit, best-case, and pipe-only tiers. It also projects cohort-level Net/Gross Revenue Retention (NRR/GRR) to detect leaky cohorts and scores per-stage funnel confidence, ensuring all assumptions are explicit for executive review.

This skill surfaces and prioritizes hidden assumptions embedded in product briefs, PRDs, or concept descriptions. It assesses assumptions across Desirability, Feasibility, Viability, and Usability, scoring them by Confidence and Impact to create a structured, prioritized map. It is essential for product risk auditing, validating product plans, and ensuring the robustness of early-stage concepts.

This comprehensive skill applies industry-standard prioritization frameworks, including RICE, MoSCoW, Kano, and ICE, to rank product features and backlog items. It generates a scored, defensible list that includes a recommended build order, explicitly deprioritized items, and documented assumptions. Essential for Product Managers and teams needing to make data-driven, strategic decisions about the product roadmap and manage scope trade-offs.

A comprehensive framework for creating defensible, bottom-up sales forecasts. This model guides users through building detailed pipeline stage models, conducting scenario analysis (Upside, Base, Downside), and logging key assumptions. Ideal for sales and revenue leaders who need a systematic, data-driven approach to projecting future revenue, moving beyond simple guesswork.

This skill transforms AI from a validating assistant into a relentless adversary. Ideal for stress-testing high-stakes decisions, business plans, or core assumptions. It forces the model to produce the strongest case against your position, identifying potential failure points and required proofs, ensuring you confront the risks you might otherwise ignore.

Before You Build acts as a crucial checkpoint in the AI coding workflow. It prevents premature implementation by rigorously assessing product risk—analyzing market demand, existing user workflows, switching costs, and distribution channels. Instead of writing code, it guides the user to define core assumptions and propose the smallest, most effective validation test (e.g., landing page test, user interview) before committing resources.

A comprehensive guide for creating a consistent, credible, and highly defensible suite of investor-facing materials. This tool ensures alignment across pitch decks, financial models, one-pagers, and memos by enforcing a single source of truth for all metrics, assumptions, and use of funds, significantly boosting fundraising credibility.

Provides a structured guide for positioning academic manuscripts within ongoing theoretical debates. It teaches authors how to move beyond simple 'gap-spotting' by identifying tensions, challenging assumptions (problematization), or testing boundaries between established theories. Essential for improving the theoretical rigor and argumentation of submissions to top-tier management journals.

This skill guides researchers on how to position their academic paper for top-tier journals in entrepreneurship. Instead of merely identifying 'gaps,' the focus is on joining an active scholarly conversation, problematizing established assumptions, and engaging with multiple disciplinary lenses (e.g., economics, psychology, sociology). It ensures the manuscript's contribution is clearly framed against the existing literature.

This guide teaches scholars how to strategically position their research paper within the ongoing international business (IB) academic conversation. Instead of merely identifying a research gap, it instructs users to problematize core assumptions, join established academic debates, and structure their arguments to demonstrate clear intellectual intervention. Essential for refining paper introductions before submission to top-tier journals.

This guide provides a framework for authors to strategically position their empirical research within the ongoing academic debates of Operations and Supply Chain Management (OM/SCM). It teaches authors to move beyond merely identifying knowledge gaps by problematizing shared assumptions, explicitly distinguishing empirical observation from analytical modeling, and aligning the contribution with the target department's specific mission.

Guides the rigorous development of theoretical models and research frameworks for Production and Operations Management (POM) manuscripts. It covers analytical, empirical, and behavioral tracks, ensuring that all assumptions and mechanisms are deeply grounded in real-world operational constraints and decision-making processes, thereby building a defensible academic argument.