Skill UI

A comprehensive guide for systematic statistical analysis, designed for academic research. This skill assists in selecting the correct hypothesis tests (t-test, ANOVA, regression, chi-square), checking assumptions (normality, homogeneity), performing advanced analyses (Bayesian methods, power analysis), calculating effect sizes, and generating full APA-formatted reports for publication.

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.

Guides PMs through a full discovery cycle—from framing a hypothesis to validating solutions—by combining problem definition, customer interviews, synthesis, and experiments so teams can test assumptions before investing in build work.

Guides product leaders through Jeff Gothelf's Lean UX Canvas v2 to turn business problems into aligned experiments, surface assumptions, and document learning goals before investing in features.

Guides product teams through MITRE's Problem Framing Canvas with structured inward/outward/reframe questions so you clarify assumptions, surface stakeholders, and create an actionable HMW statement before ideating.

This framework guides users through a pre-mortem analysis, systematically imagining a plan has failed and working backward to identify critical weaknesses. It surfaces underlying assumptions, dependencies, and potential failure points. Ideal for teams before committing significant resources, presenting to investors, or operating under high pressure.

An adversarial thinking partner designed for founders and C-suite executives. It stress-tests business plans, simulates difficult board meetings, and facilitates brutally honest post-mortems, ensuring vulnerabilities are found before competitors or boards do. Use when you need to challenge assumptions and face hard decisions.

This methodology provides a structured framework for stress-testing critical business assumptions before they fail in the market. Learn to systematically identify, challenge, and model the downside risk associated with market size, revenue projections, competitive moats, and customer behavior. It moves beyond optimism to achieve crucial business calibration.

A set of behavioral guidelines derived from Andrej Karpathy's observations to help users writing, reviewing, or refactoring code with Large Language Models (LLMs). It guides developers to avoid overcomplication, make surgical changes, explicitly surface assumptions, and define verifiable success criteria, promoting clean, simple, and robust code development.

A systematic framework for launching and validating new products and business models. It guides users through the Build-Measure-Learn loop, emphasizing validated learning over vanity metrics, and structuring Minimum Viable Product (MVP) experiments to test core assumptions and determine if a pivot or perseverance is necessary.

A structured methodology for de-risking product ideas and validating market opportunities. This framework guides teams through defining measurable outcomes, building Opportunity Solution Trees (OST), mapping critical assumptions (Desirability, Viability, Feasibility), and planning rigorous experiments (prototyping, interviews, usability tests) before committing to full development resources. Ideal for discovery sprints and pre-build validation.

Produces structured proof packages for ML/AI theory claims by extracting statements, assumptions, and sketches, then drafting a dependency-aware proof or blockage report ensuring every step is justified before updating PROOF_PACKAGE.md.