Skill UI

This tool systematically structures and derives complex research formulas and theoretical models. It takes scattered equations, messy notes, and ambiguous ideas, transforming them into a coherent, paper-ready derivation package. Use it when you need to build a rigorous theory line, organize explicit assumptions, or clarify the conceptual object underlying a set of formulas.

This skill helps refine vague, raw ideas into sharp, actionable concepts. It guides users through structured divergent thinking (expanding options, generating variations) and convergent thinking (stress-testing assumptions, clustering directions). The process culminates in a detailed one-pager including Problem Statements, MVP Scope, and key assumptions to validate, ensuring focus and clarity before development.

Build interconnected Excel financial models (income statement, balance sheet, cash flow, DCF, cap table, scenarios) driven by an assumptions sheet so each output updates automatically from provided inputs.

Spec-Driven Development (SDD) is a structured methodology that mandates defining comprehensive specifications before writing any code. It guides users through a gated process: SPECIFY -> PLAN -> TASKS -> IMPLEMENT. This workflow ensures all assumptions are surfaced, requirements are concrete, and the final product has clear success criteria, minimizing ambiguity and architectural risk in large projects.

This meta-skill set provides a structured framework covering the entire software development lifecycle (SDLC). It guides developers through every phase, from initial concept refinement and requirement specification to incremental implementation, testing, code review, and deployment. It enforces critical professional behaviors like surfacing assumptions, managing ambiguity, and maintaining scope discipline to ensure high-quality, robust engineering outcomes.

Behavioral guidelines for writing, reviewing, and refactoring code with LLMs, emphasizing minimalism, clear assumptions, surgical edits, and verifiable success criteria to avoid common machine-assisted coding mistakes.

This skill guides the systematic execution of complex development plans. It mandates a critical review of the plan for dependencies, ambiguous conditions, and environmental assumptions before execution. The process involves incremental task completion, rigorous testing, structured committing, and final reporting, ensuring adherence to established development best practices.

This guide outlines a rigorous, technical framework for receiving and responding to code review feedback. It emphasizes that code review is a technical assessment, not an emotional exchange. Users learn how to verify assumptions, ask clarifying questions, professionally refute incorrect suggestions, and implement changes iteratively, ensuring code quality and architectural integrity.

A structured methodology for precisely identifying specific geographic locations or historical landmarks. This technique systematically narrows the search scope, moving from broad regions (countries/former states) down through administrative divisions, city centers, and finally to specific monuments or structures. It requires utilizing diverse clues like historical events, census data, and distance constraints, while critically verifying assumptions at each step to ensure accuracy.

This guide outlines a mandatory, evidence-based protocol for declaring any work complete, fixing a bug, or submitting a pull request. It strictly mandates that all claims of success must be supported by fresh, verifiable evidence, such as passing test outputs, successful build logs, or comprehensive checklist verification. It eliminates reliance on assumptions, gut feelings, or trusting proxy reports.

This skill acts as a collaborative research ideation partner, guiding users through a structured brainstorming process. It helps generate novel hypotheses, explore interdisciplinary connections, challenge underlying assumptions, and identify potential research gaps. Use it when starting a project without specific observations, or when overcoming creative blocks in scientific problem-solving.

This skill guides users through the entire statistical analysis workflow, covering everything from framing research questions and checking assumptions to running complex models (t-tests, ANOVA, regression, Bayesian methods). It emphasizes calculating effect sizes and generating fully formatted results compatible with APA guidelines, ensuring rigorous and reproducible research reporting.