hypothesis-generation
Formulate evidence-bounded scientific questions, candidate hypotheses, rival explanations, causal or associational claims, discriminating predictions, measurements, and preregistration-ready analysis plans. Use when turning observations or preliminary findings into transparent, testable research plans without treating hypotheses as facts.
npx skills add K-Dense-AI/claude-scientific-writer --skill hypothesis-generation --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Scientific Hypothesis Generation Turn an observation into a transparent set of candidate explanations and tests. A hypothesis is a proposal to be challenged, not a finding, fact, diagnosis, or recommendation. ## Non-negotiable boundaries Before using unpublished, sensitive, controlled, personal, proprietary, export-controlled, or security-relevant material: 1. Confirm authorization and the applicable institutional, funder, publisher, data-use, privacy, and AI policies. 2. Keep the material local unless an authorized human explicitly approves a named external destination and data scope. 3. Minimize inputs. Do not place sensitive or unpublished data in web searches or external AI systems without authorization. 4. Stop at the appropriate human, animal, biosafety, dual-use, data-governance, or regulatory gate. Never: - present a hypothesis, mechanism, causal effect, citation, or apparent pattern as established evidence; - claim novelty because a quick search found nothing; - infer causation from association, temporal order alone, predictive accuracy, or model output; - supply patient-specific diagnosis, treatment, dose, prognosis, or other clinical advice; - provide harmful experimen
- Non-negotiable boundaries
- Keep the objects distinct
- Workflow
- 1. Run the scope and safety gate
- 2. Freeze the observation
- 3. Frame the research question
- 4. Establish a dated evidence boundary
- 5. Generate rivals before choosing tests
- 6. Declare the claim type and estimand
- 7. Derive discriminating predictions
- 8. Operationalize and validate measurement
- 9. Match design and analysis to the claim
- 10. Prevent HARKing and expose deviations
- 11. Plan replication and updating
python3 scripts/check_operationalization.py local-operationalization.json
What does the hypothesis-generation skill do?
Formulate evidence-bounded scientific questions, candidate hypotheses, rival explanations, causal or associational claims, discriminating predictions, measurements, and preregistration-ready analysis plans. Use when turning observations or preliminary findings into transparent, testable research plans without treating hypotheses as facts.
How do I install it?
Run `npx skills add K-Dense-AI/claude-scientific-writer --skill hypothesis-generation --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From K-Dense-AI/claude-scientific-writer, a repository with 2,169 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.