Loading...
Loading...
Found 78 Skills
Use when asked to calculate statistical power, determine sample size, or plan experiments for hypothesis testing.
Systematic visual geolocation reasoning from images. [VAD] Analyzes photos, street views, or satellite imagery to determine location. Uses visual clue extraction, hypothesis formation, and web verification. [NÄR] Use when: geolocate, identify location, where is this, find this place, geographic analysis, location from image, OSINT geolocation [EXPERTISE] Visual analysis, geographic indicators, verification strategies
Plan and lead execution when outcomes are uncertain and requirements are ambiguous. Produces an Uncertainty Planning Pack (uncertainty map, hypotheses + experiments, buffers + triggers, cadence + comms). Use for ambiguity, unknowns, hypothesis-driven planning, experimentation, contingency planning.
Property-based testing with Hypothesis for discovering edge cases automatically. Use when testing invariants, finding boundary conditions, implementing stateful testing, or validating data transformations.
Probability, distributions, hypothesis testing, and statistical inference. Use for A/B testing, experimental design, or statistical validation.
McKinsey-style issue tree framework for breaking down complex problems into MECE (Mutually Exclusive, Collectively Exhaustive) components. Use when users need to decompose strategic questions, structure analysis, create work plans, or prepare for case interviews. Apply hypothesis-driven approach to problem-solving.
Comprehensive statistical analysis for research, experiments, and data science. Covers hypothesis testing, effect sizes, confidence intervals, Bayesian methods, regression, and advanced techniques. Emphasizes correct interpretation and avoiding common statistical mistakes. Use when ", " mentioned.
Document debugging sessions with hypothesis tracking and knowledge base
Methodology for debugging non-trivial problems systematically. This skill should be used automatically when investigating bugs, test failures, or unexpected behavior that isn't immediately obvious. Emphasizes hypothesis formation, parallel investigation with subagents, and avoiding common anti-patterns like jumping to conclusions or weakening tests.
Systematic debugging with hypothesis-driven investigation. Use when something is broken, tests are failing, unexpected behavior occurs, or errors need investigation. Triggers on: 'this is broken', 'debug', 'why is this failing', 'unexpected error', 'not working', 'bug', 'fix this issue', 'investigate', 'tests failing', 'trace the error', 'use debug mode'. Full access mode - can run commands, add logging, and fix issues.
Structured hypothesis formulation from observations. Use when you have experimental observations or data and need to formulate testable hypotheses with predictions, propose mechanisms, and design experiments to test them. Follows scientific method framework. For open-ended ideation use scientific-brainstorming; for automated LLM-driven hypothesis testing on datasets use hypogenic.
Automated LLM-driven hypothesis generation and testing on tabular datasets. Use when you want to systematically explore hypotheses about patterns in empirical data (e.g., deception detection, content analysis). Combines literature insights with data-driven hypothesis testing. For manual hypothesis formulation use hypothesis-generation; for creative ideation use scientific-brainstorming.