yogsoth-ai/de-anthropocentric-research-engine

define-evidence-protocol

Define research questions, evidence scope, queries/concepts, inclusion/exclusion rules, provenance requirements, and stopping evidence for an acquisition task.

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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 define-evidence-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。

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。

define-evidence-protocol

Purpose

Define research questions, eligible evidence scope, query concepts, inclusion/exclusion rules, provenance requirements, and stopping evidence.

Input contract

yaml

required: [research_question, evidence_scope, inclusion_rules]
optional: [exclusion_rules, query_concepts, quality_rubric, stopping_policy]
constraints: [eligible universe, provenance fields, and stopping evidence must be explicit]

Procedure

State the question, target population or domain, comparison, outcomes, and time boundaries.
Define eligible evidence, exclusion rules, source-independence requirements, and extraction fields.
Specify batch boundaries, novelty dimensions, and relative stopping evidence.
Record unresolved scope choices before acquisition begins.

If the protocol fixes inclusion, exclusion, and stopping rules, consider select-seed-evidence as the next tactic.

Output contract

yaml

produces: [evidence_protocol, eligible_universe, query_concepts, screening_rules, provenance_requirements, stopping_evidence]
delta_fields: [decisions, assumption_updates, uncertainties, open_questions]

Quality gates

The eligible universe and exclusion rules are operational.
Stopping evidence names numerator, denominator, batch increment, source references, direction, threshold, and rationale when relative quantities apply.