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.

Vedi sorgente

Come usare

  1. Copia il comando e controlla il pacchetto prima di installarlo nel tuo ambiente agente.
  2. Controlla strumenti, credenziali e ambienti richiesti nel documento originale.
  3. Descrivi input e risultato desiderato; confronta l’output con le fonti.
npx skills add https://github.com/yogsoth-ai/de-anthropocentric-research-engine --skill audit-study-validity

Prima di iniziare

  • L’installazione non include abbonamenti, quote API o accesso ai file.
  • Stelle e installazioni indicano attività, non un test indipendente di qualità o sicurezza.
Documento Skill originale

Contenuto dal repository con titoli, esempi, codice, tabelle, link e immagini preservati.

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

Correlati per attività

dallo stesso repository

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