yogsoth-ai/de-anthropocentric-research-engine

audit-study-validity

Audit a study against an appropriate methodological-quality and risk-of-bias rubric; return domain-level judgments, evidence, and overall confidence.

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How to use

  1. Copy the installation command and review the source package before installing it in your own agent environment.
  2. Check the source document for required tools, credentials and supported hosts.
  3. 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-validity

Before 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.
Original skill document

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

yaml
required: [study_record, validity_rubric]
optional: [protocol, analysis_plan, supplementary_materials]
constraints: [rubric applicability and judgment rationale must be explicit]

Procedure

  1. Select and scope the rubric for the study design.
  2. Judge each domain from reported methods and supporting material.
  3. Record signaling evidence, uncertainty, and direction of likely bias.
  4. 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

yaml
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-assessment
  • resolved: knowledge-acquisition-risk-of-bias-assessment
  • resolved: knowledge-acquisition-quality-assessment-protocol
  • intermediate: Pass3/assess-study-quality
  • intermediate: Pass3/assess-risk-of-bias

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