Hypothesis GenerationSAFE
Kosmos: An AI Scientist for Autonomous Discovery - An implementation and adaptation to be driven by Claude Code or API - Based on the Kosmos AI Paper - https://arxiv.org/abs/2511.02824
Overview
Kosmos: An AI Scientist for Autonomous Discovery - An implementation and adaptation to be driven by Claude Code or API - Based on the Kosmos AI Paper - https://arxiv.org/abs/2511.02824
9be77d361822OBSERVED · 2026-10-14What it tells the agent
The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.
--- name: hypothesis-generation description: "Generate testable hypotheses. Formulate from observations, design experiments, explore competing explanations, develop predictions, propose mechanisms, for scientific inquiry across domains." --- # 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 **For all scientific domains:** - Use WebSearch to find recent papers, preprints, and reviews - Search for established theories, mechanisms, or frameworks - Identify gaps in current understanding **Search strategy:** - Begin with broad searches to understand the landscape - Narrow to specific mechanisms, pathways, or theories - Look for contradictory findings or unresolved debates - Consult `references/literature_search_strategies.md` for detailed search techniques ### 3. Synthesize Existing Evidence Analyze and integrate findings from literature search: - Summarize current understanding of the phenomenon - Identify established mechanisms or theories that may apply - Note conflicting evidence or alternative viewpoints - Recognize gaps, limitations, or unanswered questions - Identify analogies from related systems or domains ### 4. Generate Competing Hypotheses Develop 3-5 distinct hypotheses that could explain the phenomenon. Each hypothesis should: - Provide a mechanistic explanation (not just description) - Be distinguishable from other hypotheses - Draw on evidence from the literature synthesis - Consider different levels of explanation (molecular, cellular, systemic, population, etc.) **Strategies for generating hypotheses:** - Apply known mechanisms from analogous systems - Consider multiple causative pathways - Explore different scales of explanation - Question assumptions in existing explanations - Combine mechanisms in novel ways ### 5. Evaluate Hypothesis Quality Assess each hypothesis against established quality criteria from `references/hypothesis_quality_criteria.md`: **Testability:** Can the hypothesis be empirically tested? **Falsifiability:** What observations would disprove it? **Parsimony:** Is it the simplest explanation that fits the evidence? **Explanatory Power:** How much of the phenomenon does it explain? **Scope:** What range of observations does it cover? **Consistency:** Does it align with established principles? **Novelty:** Does it offer new insights beyond existing explanations? Explicitly note the strengths and weaknesses of each hypothesis. ### 6. Design Experimental Tests For each viable hypothesis, propose specific experiments or studies to test it. Consult `references/experimental_design_patterns.md` for common approaches: **Experimental design elements:** - What would be measured or observed? - What comparisons or controls are needed? - What methods or techniques would be used? - What sample sizes or statistical approaches are appropriate? - What are potential confounds and how to address them? **Consider multiple approaches:** - Laboratory experiments (in vitro, in vivo, computational) - Observational studies (cross-sectional, longitudinal, case-control) - Clinical trials (if applicable) - Natural experiments or quasi-experimental designs ### 7. Formulate Testable Predictions For each hypothesis, generate specific, quantitative predictions: - State what should be observed if the hypothesis is correct - Specify expected direction and magnitude of effects when possible - Identify conditions under which predictions should hold - Distinguish predictions between competing hypotheses - Note predictions that would falsify the hypothesis ### 8. Present Structured Output Use the template in `assets/hypothesis_output_template.md` to present hypotheses in a clear, consistent format: **Standard structure:** 1. **Background & Context** - Phenomenon and literature summary 2. **Competing Hypotheses** - Enumerated hypotheses with mechanistic explanations 3. **Quality Assessment** - Evaluation of each hypothesis 4. **Experimental Designs** - Proposed tests for each hypothesis 5. **Testable Predictions** - Specific, measurable predictions 6. **Critical Comparisons** - How to distinguish between hypotheses ## Quality Standards Ensure all generated hypotheses meet these standards: - **Evidence-based:** Grounded in existing literature with citations - **Testable:** Include specific, measurable predictions - **Mechanistic:** Explain how/why, not just what - **Comprehensive:** Consider alternative explanations - **Ri
Trust audit
SAFEgrade B · trust 89/100 Nothing in the source contradicts what it says it does. Grade A is reserved for packages that have also passed the behavioural sandbox.
| Layer | What it checks | Result |
|---|---|---|
| L0 | Provenance & inventory | PASS |
| L1 | Static analysis of the code | NA |
| L2 | Instruction surface (what it tells the agent) | PASS |
| L3 | Class-specific surface | PASS |
| L4 | Behavioural (sandbox) | SKIPPED |
What the source does
- Filesystem
- none-observed
- Network
- none-observed
- Shell
- none-observed
- Dependencies
- pinned
- Secrets in source
- none-found
Findings (0)
No findings outside the package's declared scope.
Gates applied: no_behavioural_pass.
9be77d361822full audit observations/trust-audit/skill/jimmc414__hypothesis-generation.json · Report an issue / request a re-scanAudit history
Every audit this skill has had.
| Date | Source | Verdict | Grade | Score | Change |
|---|---|---|---|---|---|
| 2026-10-14 | 9be77d361822 | SAFE | B | 89 | first audit |
Questions
What does the Hypothesis Generation skill do?
Kosmos: An AI Scientist for Autonomous Discovery - An implementation and adaptation to be driven by Claude Code or API - Based on the Kosmos AI Paper - https://arxiv.org/abs/2511.02824
Is Hypothesis Generation safe to install?
The audit found nothing in the source that contradicts what it says it does, and graded it B (89/100). Grade A is held back for packages that have also passed a sandboxed behavioural run, which is why a clean skill reads B.
What can Hypothesis Generation access on my machine?
The audit observed no filesystem, network or shell use at all in its source.
How current is this page?
The grade is for one exact copy of the source (9be77d361822), read on 2026-10-14. The repository is watched, and a new audit runs when it changes — this is the first audit.