hypothesis-generation
No security findings. Scanned 1 time, most recently 2026-10-21.
Second scanner: NVIDIA SkillSpector
The two scanners disagree: Cisco found nothing and SkillSpector says do not install. One of them has a blind spot here; read the skill before trusting either. How the two scanners compare across the whole corpus: Two scanners, one corpus.
What this skill does
Formulates evidence-bounded scientific questions, candidate hypotheses, rival explanations, causal or associational claims, discriminating predictions, measurements, and preregistration-ready analysis plans. Used when turning observations or preliminary findings into transparent, testable research plans without treating hypotheses as facts.
- Package
- 27 files, 260 KB
- SKILL.md
- 1983 words, 12+ sections
- Code
- Python (8)
- License
- MIT
- Compatibility
- Python 3.11+ standard library. Bundled CLIs are deterministic and local-only; they accept bounded JSON, CSV, or Markdown and require no network, credentials, models, image services, or external packages.
SKILL.md covers: Scientific Hypothesis Generation · 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
Findings
No findings. SAFE
What is in the package
27 files, 260 KB. Show the list.
| File | Kind | Size |
|---|---|---|
SKILL.md | doc | 16 KB |
assets/evidence_ledger_template.csv | data | 581 B |
assets/falsification_controls_template.json | config | 5.7 KB |
assets/hypothesis_record_template.json | config | 13 KB |
assets/operationalization_template.json | config | 2.2 KB |
assets/prediction_rival_matrix_template.csv | data | 1.6 KB |
assets/preregistration_scaffold_template.md | doc | 5.0 KB |
assets/search_boundary_template.json | config | 1.1 KB |
assets/source_ledger.csv | data | 15 KB |
references/causal_inference_and_claims.md | doc | 7.8 KB |
references/concepts_and_workflow.md | doc | 7.2 KB |
references/ethics_safety_and_ai.md | doc | 10 KB |
references/experimental_design_patterns.md | doc | 9.5 KB |
references/hypothesis_quality_criteria.md | doc | 7.8 KB |
references/literature_search_strategies.md | doc | 7.9 KB |
references/preregistration_and_open_science.md | doc | 7.6 KB |
references/security_validation.md | doc | 4.1 KB |
references/source_ledger.md | doc | 9.8 KB |
references/tool_reference.md | doc | 8.2 KB |
scripts/_common.py | script | 15 KB |
scripts/audit_evidence_ledger.py | script | 12 KB |
scripts/check_falsification_controls.py | script | 16 KB |
scripts/check_operationalization.py | script | 7.9 KB |
scripts/generate_preregistration_scaffold.py | script | 14 KB |
scripts/lint_causal_claims.py | script | 6.6 KB |
scripts/validate_hypothesis_schema.py | script | 38 KB |
scripts/validate_prediction_matrix.py | script | 9.6 KB |
External hosts referenced
grants.nih.gov www.cos.io osp.od.nih.gov www.science.org www.whitehouse.gov arxiv.org docs.python.org help.osf.io pmc.ncbi.nlm.nih.gov www.bmj.com www.hhs.gov arriveguidelines.org beta.nih.gov books.google.com database.ich.org doi.org export.arxiv.org info.arxiv.org miguelhernan.org pubmed.ncbi.nlm.nih.gov unesdoc.unesco.org www.acpjournals.org www.amstat.org www.aspr.gov www.cdc.gov www.cochrane.org www.nationalacademies.org www.nist.gov www.unesco.org www.who.int
Hostnames that appear in the skill's files. Referencing a host is not a finding; compare against the findings above and the skill's stated purpose.
Package contents recorded 2026-10-22 at commit 593377bb0487. The scan above ran against the version current at scan time; the two can differ if the skill has since been updated.
What we checked
- Static analyzer. Pattern rules over SKILL.md and bundled files: known-bad commands, exfiltration URLs, secrets, obfuscation.
- Bytecode analyzer.
- Pipeline analyzer.
- Correlation analyzer.
- Behavioral analyzer. Checks what the skill asks the agent to do against what its description claims it does.
- Trigger analyzer. Looks for instructions that fire on conditions the user did not ask for.
What we did not check
- Runtime behaviour. The skill was not executed; findings come from reading its files.
- Anything fetched at install or run time, including remote scripts, package dependencies, and URLs the skill tells the agent to open.
- Content the analyzers cannot read: images, binaries, and some non-English text.
- Known scanner bypass classes. See the methodology and limitations page.
Sources
- Repository: https://github.com/K-Dense-AI/scientific-agent-skills
- Raw scan result: skills-hypothesis-generation-scan.json
- Badge for your README:
- Request a re-scan