aviskaar/hypothesis-generation
skills/hypothesis-generation/SKILL.md
Use this skill when generating, refining, or stress-testing research hypotheses for AI/ML problems. Produces well-formed, falsifiable hypotheses grounded in existing literature and mechanistic reasoning.
$ install --global
skillsauthnpx skillsauth add aviskaar/open-org hypothesis-generationInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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SKILL.md
- name:
- hypothesis-generation
- description:
- Use this skill when generating, refining, or stress-testing research hypotheses for AI/ML problems. Produces well-formed, falsifiable hypotheses grounded in existing literature and mechanistic reasoning.
- license:
- Apache-2.0
- author:
- aviskaar
- version:
- 1.0
- tags:
- hypothesis, research, ideation, theory, ML
Hypothesis Generation
Generate and rigorously evaluate novel research hypotheses.
Process
Step 1 — Problem Framing
Clarify the research context:
- What phenomenon is being investigated?
- What is already known? (Ask the user to provide key papers or summarize the state of the field.)
- What surprising or unexplained observation prompted this inquiry?
Step 2 — Generate Candidate Hypotheses
Produce 3–5 distinct hypotheses. For each:
- State it as a precise, falsifiable claim.
- Identify the underlying mechanism or assumption.
- Describe a minimal experiment that would confirm or refute it.
Use diverse generation strategies:
- Analogy: Does a known mechanism from a related domain apply here?
- Inversion: What if the conventional assumption is reversed?
- Scaling: Does the behavior hold at different data/model scales?
- Ablation-driven: What specific component, if removed, would eliminate the effect?
Step 3 — Stress-Test Each Hypothesis
For each candidate, answer:
- Falsifiability: Can this be proven false? If not, revise it.
- Prior literature: Does any existing work already test this?
- Confounds: What alternative explanations could produce the same observation?
- Scope: Under what conditions does this hypothesis break down?
- Cost to test: Estimate compute and data requirements.
Step 4 — Prioritize
Rank hypotheses by:
- Impact (if true, how much does it matter?)
- Testability (how quickly and cheaply can it be tested?)
- Novelty (how differentiated from existing work?)
Recommend the top hypothesis with a justification.
Step 5 — Refine
Take the top hypothesis and sharpen it into a paper-ready claim:
"We hypothesize that [specific mechanism] causes [observed phenomenon] in [specific setting], and that this can be demonstrated by [experiment]."
Output Format
Produce a markdown document with all five steps. Number each hypothesis. Use a comparison table in Step 4.
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