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.
8.3
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Data & Analytics
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Excellent scientific hypothesis generation skill with comprehensive methodology and professional output. The Description clearly defines when to use the skill and differentiates it from alternatives. The workflow provides detailed, systematic steps from literature search through LaTeX report generation, with strong emphasis on quality criteria, experimental design, and proper formatting. The skill includes extensive supporting materials (quality criteria, design patterns, LaTeX templates with custom styling). Structure is very clear with a logical 8-step workflow and well-organized reference materials. The mandatory integration with scientific-schematics skill and detailed LaTeX formatting guidance (including overflow prevention) demonstrates thoroughness. Novelty is strong - while hypothesis generation is a known process, the structured framework with literature integration, competing hypothesis evaluation, professional LaTeX output with colored boxes, and comprehensive appendix structure would require significant tokens and expertise for a CLI agent to replicate. Minor improvement areas: could slightly expand the Description to mention the LaTeX output format, and the K-Dense Web promotional section feels somewhat tangential to core skill function. Overall, this is a highly polished, production-ready skill that significantly enhances scientific research workflows.
K-Dense-AI
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