How to use
- Copy the installation command and review the source package before installing it in your own agent environment.
- Check the source document for required tools, credentials and supported hosts.
- Describe your input and desired deliverable; review the output against the source material.
npx skills add https://github.com/yogsoth-ai/de-anthropocentric-research-engine --skill audit-study-validityBefore you start
- Installation does not include a model subscription, API allowance or permission to access your files.
- Repository stars and installation counts describe activity, not an independent quality or security test.
Rendered from the source repository. Headings, examples, code, tables, links, and referenced images are preserved.
audit-study-validity
Purpose
Audit a study against an appropriate methodological-quality and risk-of-bias rubric and return domain judgments with evidence and confidence.
Input contract
required: [study_record, validity_rubric]
optional: [protocol, analysis_plan, supplementary_materials]
constraints: [rubric applicability and judgment rationale must be explicit]Procedure
- Select and scope the rubric for the study design.
- Judge each domain from reported methods and supporting material.
- Record signaling evidence, uncertainty, and direction of likely bias.
- Aggregate domain judgments without hiding critical domain failures.
If the validity-screened corpus may already support a stopping decision, consider assess-evidence-saturation as the next tactic. If validity differs materially across designs, populations, or conditions, consider analyze-heterogeneity as the next tactic. If the conclusion depends on study exclusions or uncertain validity judgments, consider assess-sensitivity as the next tactic.
Output contract
produces: [domain_judgments, risk_of_bias_profile, evidence_basis, overall_confidence, applicability_notes]
delta_fields: [findings, evidence_updates, uncertainties, decisions, open_questions]Quality gates
- No overall label is emitted without domain-level evidence.
- Rubric choice and missing-data handling are recorded.
Failure and counterexamples
Do not average incompatible domains into a false precision score or treat unreported methods as low risk.
Provenance map
resolved: knowledge-acquisition-quality-assessmentresolved: knowledge-acquisition-risk-of-bias-assessmentresolved: knowledge-acquisition-quality-assessment-protocolintermediate: Pass3/assess-study-qualityintermediate: Pass3/assess-risk-of-bias


