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 specify-reproducibility-protocol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。
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。
specify-reproducibility-protocol
Purpose
Define exact/statistical/conceptual reproduction target and the controls needed to test it.
Input contract
required: [reproducibility_target, experiment_specification, available_controls]
optional: [evidence, assumptions, prior_results]
constraints: [use named scientific objects; retain provenance and missingness; $\alpha$ = 0.05 and power = 0.8 where applicable]Procedure
- Validate the typed inputs and state the decision this operation must support.
- Apply the declared operation to the named object; record intermediate values that affect interpretation.
- Check boundary conditions and counterexamples, then emit the result with uncertainty and source links.
Output contract
produces: [specify_reproducibility_protocol_result, evidence_trace, uncertainties]
delta_fields: [evidence_updates, uncertainties]Quality gates
- Inputs are named scientific objects with compatible schemas.
- Every material result has a derivation or source reference.
- Fixed statistical criteria remain exact where applicable: $\alpha$ 0.05 and power 0.8.
Failure and counterexamples
Return a failed operation with the violated precondition when inputs are incomplete, assumptions are unsupported, or a counterexample defeats the result.
Provenance map
- intermediate: experiment-execution/reproducibility-protocol [tactic]



