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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使い方

インストールコマンドをコピーし、自分の環境へ導入する前にソースを確認します。
必要なツール、認証情報、対応環境を原文で確認します。
入力と成果物を指定し、出力を資料と照合します。

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

開始前の確認

導入にはモデル契約、API枠、ファイルへのアクセス許可は含まれません。
スター数や導入数は活動の指標であり、独立した品質・安全性試験ではありません。

リポジトリの原文

見出し、例、コード、表、リンク、参照画像を含む原文を表示しています。

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]