Generate testable hypotheses. Formulate from observations, design experiments, explore competing explanations, develop predictions, propose mechanisms, for scientific inquiry across domains.
NewMaintained
Automatic
◆Research pack
Last verified 8/21/2026
What it changes
Passed 11 of 17 test cases with the skill, and 12 of 17 without it.
Without skill68%
With skill64%
Checks it passed that the model alone missed
Outlines experimental designs incorporating specific control groups.
Structures the output to include explicit quality assessment sections.
Provides specific quantitative predictions for empirical falsification.
3 fixed11 passed either way4 still failing4 broke
Pass-rate lift
-5 pp
Tokens
+75%
Agent turns
0%
Response time
+45%
Model gemini-3.6-flashMeasured 8/21/2026v114 of 22 cases passed with skill · 17 comparable
Tokens, turns and time describe resource use, not a dollar price.
See it in action
One of 3 test cases the skill fixed. Same model, same prompt, with and without the skill.
1 of 3
Prompt
Transient paternal exposure to high-fat diet in C. elegans results in metabolic dysfunction in F2 offspring, even when F1 males are raised on standard diets and have normal transcriptomes. Formulate competing epigenetic mechanisms for this inheritance, evaluate them, and outline experimental tests.
What was checked, without and with the skill
Checked
Without skill
With skill
Generates distinct non-genetic mechanistic explanations for the transmitted offspring phenotype.
Passed
Passed
Outlines experimental designs incorporating specific control groups.
Missed
Passed
case-18 · gemini-3.6-flash · 8/21/2026 · answers are capped and redacted
What's inside
The instructions your model reads when the skill loads.
SKILL.md150 lines5 files
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
Workflow
Follow this systematic process to generate robust scientific hypotheses:
1. Understand the Phenomenon
Start by clarifying the observation, question, or phenomenon that requires explanation:
Identify the core observation or pattern that needs explanation
Define the scope and boundaries of the phenomenon
Note any constraints or specific contexts
Clarify what is already known vs. what is uncertain
Identify the relevant scientific domain(s)
2. Conduct Comprehensive Literature Search
Search existing scientific literature to ground hypotheses in current evidence. Use both PubMed (for biomedical topics) and general web search (for broader scientific domains):
For biomedical topics:
Use WebFetch with PubMed URLs to access relevant literature
Search for recent reviews, meta-analyses, and primary research
Look for similar phenomena, related mechanisms, or analogous systems
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