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hypothesis-generationlisted

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.
Yuuqq/research-grade-skills · ★ 0 · AI & Automation · score 76
Install: claude install-skill Yuuqq/research-grade-skills
# Scientific Hypothesis Generation ## Overview Hypothesis generation is a systematic process for developing testable explanations. Formulate evidence-based hypotheses from observations, design experiments, explore competing explanations, and develop predictions. Apply this skill for scientific inquiry across domains. ## When to Use This Skill This skill should be used when: - Developing hypotheses from observations or preliminary data - Designing experiments to test scientific questions - Exploring competing explanations for phenomena - Formulating testable predictions for research - Conducting literature-based hypothesis generation - Planning mechanistic studies across scientific domains ## Visual Enhancement with Scientific Schematics **⚠️ MANDATORY: Every hypothesis generation report MUST include at least 1-2 AI-generated figures using the scientific-schematics skill.** This is not optional. Hypothesis reports without visual elements are incomplete. Before finalizing any document: 1. Generate at minimum ONE schematic or diagram (e.g., hypothesis framework showing competing explanations) 2. Prefer 2-3 figures for comprehensive reports (mechanistic pathway, experimental design flowchart, prediction decision tree) **How to generate figures:** - Use the **scientific-schematics** skill to generate AI-powered publication-quality diagrams - Simply describe your desired diagram in natural language - Nano Banana Pro will automatically generate, review, and refine the schema