Cómo usar
- Copia el comando y revisa el paquete antes de instalarlo en tu entorno de agente.
- Consulta las herramientas, credenciales y entornos necesarios en el documento original.
- Describe las entradas y el resultado deseado; contrasta la salida con las fuentes.
npx skills add https://github.com/yogsoth-ai/de-anthropocentric-research-engine --skill audit-study-validityAntes de empezar
- La instalación no incluye suscripción al modelo, cuota API ni permisos sobre tus archivos.
- Las estrellas e instalaciones reflejan actividad, no una prueba independiente de calidad o seguridad.
Documento original del Skill
Contenido del repositorio de origen con títulos, ejemplos, código, tablas, enlaces e imágenes preservados.
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
- 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
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-assessmentresolved: knowledge-acquisition-risk-of-bias-assessmentresolved: knowledge-acquisition-quality-assessment-protocolintermediate: Pass3/assess-study-qualityintermediate: Pass3/assess-risk-of-bias
