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 normalize-gap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。
normalize-gap
Purpose
Normalize heterogeneous research-gap records into a comparable schema before prioritization or decision operations.
Input contract
yaml
required: [gap_records, gap_schema]
optional: [evidence_records, domain_taxonomy, coding_rules]
constraints: [normalization preserves source wording, uncertainty, and gap type]Procedure
- Map each record to problem, evidence, population, mechanism, and consequence fields.
- Normalize terminology, units, and scope without erasing source distinctions.
- Mark unknown, conflicting, and inferred fields.
- Emit comparable gap records with traceability to originals.
Output contract
yaml
produces: [normalized_gap_records, field_mapping, conflict_log, traceability_map]
delta_fields: [findings, evidence_updates, uncertainties, open_questions]Quality gates
- Every normalized field traces to source text or is marked inferred.
- Conflicting gap definitions remain visible.
Failure and counterexamples
Do not collapse different gaps because they share a label or convert missing evidence into a low-priority score.
Provenance map
resolved: normalize-gap
