domains / families / science / hypothesis-generation

hypothesis-generation

combined

Formulates evidence-bounded scientific questions, candidate hypotheses, rival explanations, causal or associational claims, discriminating predictions, measurements, and preregistration-ready analysis plans. Used when turning observations or preliminary findings into transparent, testable research plans without treating hypotheses as facts. Structured scientific hypothesis formulation from observations; use when you have experimental observations or preliminary data and need testable hypotheses with predictions, mechanisms, and validation experiments.

When to use

• Use this skill when the request matches its documented task boundary.
• Use it when the user can provide the required inputs and expects a structured deliverable.
• Prefer this skill for repeatable, checklist-driven execution rather than open-ended brainstorming.
• 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

When not to use

• Do not use this skill when the required source data, identifiers, files, or credentials are missing.
• Do not use this skill when the user asks for fabricated results, unsupported claims, or out-of-scope conclusions.
• Do not use this skill when a simpler direct answer is more appropriate than the documented workflow.

Skill page

Conflict

The copies overlap on the core scientific workflow (observation, literature search, competing hypotheses, quality evaluation, experimental design, and predictions), but they disagree on scope, safety constraints, and required outputs. The K-Dense-AI copy is the most rigorous, adding non-negotiable safety boundaries, a detailed object-distinction table, and a scope/safety gate that the others lack. The Vibe-Skills and brycewang-stanford copies are nearly identical but include a mandatory requirement for AI-generated schematics (via a separate skill) and specific tool usage (WebFetch/WebSearch) that the K-Dense copy does not. The aipoch copy adds specific LaTeX packaging and formatting requirements. Because the K-Dense copy's safety-first approach conflicts with the others' focus on visual deliverables and specific tooling, and because the others contain material steps (schematics, LaTeX) that the K-Dense copy omits, crowning one would hide real alternatives.