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.

소스 보기

사용 방법

설치 명령을 복사하고 자신의 에이전트 환경에 설치하기 전에 소스 패키지를 확인하세요.
필요한 도구, 인증 정보, 지원 환경은 원본 문서에서 확인하세요.
입력과 원하는 산출물을 설명하고 출처와 결과를 대조하세요.

npx skills add https://github.com/yogsoth-ai/de-anthropocentric-research-engine --skill audit-study-validity

시작 전 확인

설치에는 모델 구독, API 한도, 파일 접근 권한이 포함되지 않습니다.
별점 수와 설치 수는 활동 지표이며 독립적인 품질·보안 검증이 아닙니다.

원본 Skill 문서

원본 저장소의 제목, 예시, 코드, 표, 링크, 이미지를 유지해 표시합니다.

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]