yogsoth-ai/de-anthropocentric-research-engine

list-undesirable-effects

List observable undesirable effects with evidence and severity as starting points for constraint/root-cause analysis.

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 list-undesirable-effects

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。

list-undesirable-effects

Purpose

List observable undesirable effects with evidence and severity as starting points for constraint/root-cause analysis.

Input contract

yaml

required: [system_observations, effect_evidence, severity_scale]
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

yaml

produces: [list_undesirable_effects_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.