hypothesis-generation

Trigger phrases

Phrases that activate this skill when typed to Claude Code:

• generate hypotheses from this data
• formulate a testable hypothesis
• what mechanisms could explain
• design an experiment to test
• propose a hypothesis for

What it does

hypothesis-generation is a Claude Code skill from K-Dense AI’s scientific-agent-skills repo. It turns Claude into a hypothesis formulator that takes experimental observations or preliminary data and applies the scientific method framework to produce: clearly stated testable hypotheses with explicit predictions, proposed mechanistic explanations, and experimental designs for testing each hypothesis.

A session produces structured hypothesis cards: null hypothesis, alternative hypothesis, predicted outcome if true, predicted outcome if false, and a proposed experimental design with key controls.

When to use it

Reach for it when:

• You have preliminary or pilot data showing an unexpected result and need to articulate what mechanisms could explain it
• You’re writing the hypothesis statement for a grant application and need to make the logic from observation to prediction explicit
• You’re designing a follow-up experiment and want to enumerate alternative hypotheses before committing to one

When not to reach for it:

• Open-ended ideation without specific observations — use scientific-brainstorming
• Automated LLM-driven hypothesis testing on datasets — use hypogenic (a separate, more automated skill)

Install

Copy the SKILL.md from K-Dense AI’s hypothesis-generation folder into .claude/skills/hypothesis-generation/ in your project.

Trigger phrases: “generate hypotheses from this data”, “formulate a testable hypothesis”, “what mechanisms could explain”, “design an experiment to test”.

What a session looks like

A typical session has three phases:

1. Observation intake. Describe the experimental observations — what you expected vs. what you saw, with any relevant context about the system and prior literature. Claude asks clarifying questions about controls and conditions.
2. Hypothesis enumeration. Claude generates 3–5 candidate hypotheses ordered from most to least parsimonious, each with a proposed mechanism and explicit testable predictions.
3. Experimental design. For each top hypothesis, Claude proposes a minimal experiment to test it — key manipulations, controls, and the result that would support vs. refute the hypothesis.

Receipts

Where it works well:

• Generating alternative hypotheses you hadn’t considered — Claude’s breadth of biological and physical mechanisms surfaces plausible alternatives that a specialist locked into their own model can miss
• Making hypothesis logic explicit for grant writing — the null/alternative/prediction format directly maps to what grant reviewers expect

Where it backfires:

• Highly system-specific mechanistic hypotheses (e.g., specific protein interaction networks) may be superficially plausible but technically implausible in your specific model system — domain expert review is essential
• The skill generates hypotheses, not priority rankings; deciding which hypothesis is most worth testing requires your experimental judgment

Pattern that works: generate 5 candidate hypotheses, then apply scientific-critical-thinking to each proposed experimental design to identify the design flaws before committing to an experiment.

Source and attribution

Originally authored by K-Dense Inc.. The canonical SKILL.md lives in the hypothesis-generation folder of their public scientific-agent-skills repository.

License: MIT. Install, adapt, and redistribute with attribution preserved.

This page documents the skill from a practitioner’s perspective. For the formal spec and any updates, defer to the source repo.