Skill UI

This skill guides the systematic process of identifying and verifying candidate football entities (players or clubs). Use it when initial search results suggest a lead, or when the question provides enough detailed constraints (e.g., nationality, era, league) to hypothesize a candidate. The technique involves constructing targeted name-based searches and cross-checking every constraint (birth year, career timeline, etc.) against the found profile to ensure accuracy.

This technique is designed for entity recognition and hypothesis generation when identifying Indian or Bollywood actors based on indirect clues. Instead of running broad searches, users should hypothesize a small set of candidate names (1-3) using the provided clues (e.g., awards, family history, controversies). Subsequently, targeted verification searches must be executed for each constraint against the candidate names to confirm the correct identity.

This skill teaches the methodology of forming concrete hypotheses about specific entities (such as film titles, director names, or historical events) when presented with constraints. Instead of performing broad, generic searches, users should leverage their world knowledge to narrow down possibilities, turning a wide query into a precise, verifiable search query. This is particularly effective for academic or historical research.

Plans, audits, and safely runs ChicagoHAI HypoGeniC/HypoRefine workflows for LLM-assisted hypothesis generation from labeled text datasets, with pinned installs, config validation, dataset leakage checks, and bounded local review before any model call.

This skill helps formulate evidence-bounded scientific questions and candidate hypotheses. It turns observations into transparent, testable research plans without treating hypotheses as facts. It distinguishes between observations, mechanisms, and predictions, ensuring rigorous research design and safety compliance.

A comprehensive tutor designed to build robust statistical thinking. It guides users through the entire data analysis lifecycle, from exploratory data visualization and descriptive statistics to complex hypothesis testing and regression modeling. The focus is on understanding the 'why' and 'how' of statistical logic using real datasets and practical problems, rather than just memorizing formulas. It is tool-agnostic, supporting concepts learned in Python, R, SPSS, or Stata.

A comprehensive coach designed to prepare individuals for top-tier strategy consulting interviews (MBB, Big Four). It covers structured problem-solving, mastering core consulting frameworks (MECE, Hypothesis-driven thinking), building compelling narratives, and handling specific case types (Profitability, M&A, Market Sizing). Ideal for candidates aiming to master the entire consulting recruiting funnel, from behavioral interviews to complex case analysis.

A powerful cognitive tool that moves beyond surface-level brainstorming. It forces users to systematically deconstruct complex problems down to their 'irreducible truths.' The skill identifies 8-12 hidden assumptions (Surface, Middle, Deep), classifies them (Fact, Convention, Belief, Interest-Driven), and ranks them by Fragility × Impact. This process allows users to stress-test their premises and rebuild conclusions based only on verified realities, making it invaluable for major life decisions or business hypothesis testing.

This structured mode acts as an experiment design partner for Product Managers. It guides users through formulating testable, falsifiable hypotheses, designing rigorous A/B tests (including sample size, duration, and guardrails), and interpreting results honestly. It helps PMs move from vague beliefs to clear, data-backed decisions: Ship, Iterate, or Kill.

This skill provides deep, decision-grade research dossiers on any specific entity (company, person, or organization). Unlike generic profiles, it mandates the user to state a clear hypothesis upfront. The resulting dossier rigorously tests this hypothesis, providing a conclusive verdict, detailed activity timelines, network signals, red flags, and a source-provenance audit log. Ideal for due diligence, investment analysis, or high-stakes meeting preparation.

This skill autonomously optimizes artifacts like code or models using Hypothesis Tree Refinement. It runs iterative experiments with dev/test evaluators to prevent overfitting, coordinating subagents in isolated worktrees. Ideal for improving training recipes, agents, or pipelines without manual supervision.

This skill outlines a systematic, multi-phase workflow for diagnosing hard bugs and performance regressions in complex codebases. It emphasizes the critical necessity of building a tight, deterministic feedback loop (Phase 1) before moving to hypothesis generation. The process guides users through reproducing the failure, minimizing the scope of the issue, and forming actionable, ranked hypotheses.