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.

Zobacz źródło

Jak używać

  1. Skopiuj polecenie instalacji i sprawdź źródła przed instalacją we własnym środowisku agenta.
  2. W oryginale sprawdź wymagane narzędzia, dane dostępowe i obsługiwane środowiska.
  3. Opisz dane wejściowe i oczekiwany rezultat; porównaj wynik ze źródłami.
npx skills add https://github.com/yogsoth-ai/de-anthropocentric-research-engine --skill audit-study-validity

Przed rozpoczęciem

  • Instalacja nie obejmuje subskrypcji modelu, limitu API ani uprawnień do plików.
  • Gwiazdki i instalacje pokazują aktywność, a nie niezależny test jakości lub bezpieczeństwa.
Oryginalny dokument Skill

Treść z repozytorium z zachowaniem nagłówków, przykładów, kodu, tabel, linków i obrazów.

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

Powiązane zadania

z tego samego repozytorium

Więcej Skills

Wszystkie Skills