hypothesis-generation

You can generate testable hypotheses by inputting your observations or data and requesting three to five hypotheses with predictions and an experimental plan. This automates structured scientific inquiry to produce systematic, testable hypotheses with mechanistic explanations.

Structured hypothesis generation is the process of turning observations into testable hypotheses with predictions and experimental designs. You need it for systematic scientific inquiry, literature-based hypothesis generation, and automated testing on datasets across research domains.

Yes, this supports literature-based hypothesis generation and automated testing on datasets. It produces competing explanations with predictions and test plans, applying the scientific method to experimental design across various research domains.

Yes, the Skill generates a LaTeX-formatted hypothesis report with appendices using provided templates. It requires YAML frontmatter with a name and description, and supports optional scripts, references, and assets to structure the final document.

The best way to formulate competing explanations is to provide your observations and request multiple testable hypotheses. The system generates competing explanations complete with predictions, mechanistic explanations, and structured experimental test plans.

Automated hypothesis generation requires structured observations or datasets as input and utilizes YAML frontmatter for configuration. It is limited to producing LaTeX-formatted reports and does not execute the experimental plans it generates.