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.

查看源码

使用方法

复制安装命令，在自己的智能体环境安装前先检查源码包。
从原始文档核对所需工具、凭据和支持的宿主。
说明输入材料与期望交付物，并对照来源检查输出。

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

开始前需了解

安装不包含模型订阅、API 额度或访问文件的权限。
仓库星数和安装量反映活跃情况，不等于独立质量或安全测试。

仓库原始内容

按源仓库内容呈现，保留标题、案例、代码、表格、链接以及原文引用的演示图片。

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]