What problem does it solve? Engineers and analysts often jump to conclusions based on anecdotal evidence, confirmation bias, or untested assumptions, leading to wrong root-cause diagnoses and poor decisions. This Skill provides a structured scientific method framework for forming testable hypotheses, evaluating evidence quality, and avoiding cognitive biases. ## Core Features & Use Cases - Hypothesis Framework: Templates for writing testable, falsifiable, specific, and measurable hypotheses with a three-phase workflow (observation, experiment design, analysis). - Evidence Hierarchy & Bias Checklists: Ranks evidence from controlled experiments down to authority opinions, and lists common biases (confirmation, anchoring, sunk cost) and logical fallacies with prevention strategies. - Decision Matrix: Weighted scoring tables for comparing options on cost, duration, and risk. - Use Case: When debugging a flaky production issue, use the hypothesis template to state a falsifiable theory, design an isolated test with a control group, and verify statistical significance before committing to a fix. ## Quick Start Use the scientific thinking skill to help me form a falsifiable hypothesis and design a controlled test for why our API latency spikes at night.
