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    "summary_en": "Provide qualitative-first, evidence-traceable developmental review of scholarly works and audit low-stakes research-assessment rubrics with optional local quality controls. Never use for ranking people or consequential decisions.",
    "summary_zh": "提供以定性为先、证据可追踪的学术作品发展性评审，并可对低风险研究评估量规进行审计，附带可选的本地质量控制。切勿用于人员排名或具有重大影响的决策。",
    "description_en": "This skill provides qualitative-first, evidence-traceable developmental review of scholarly works (papers, drafts, protocols, literature syntheses, research ideas) and audits low-stakes research-assessment rubrics. It includes optional local quality checks via bundled Python scripts (validate rubric, calculate scores, check traceability, summarize agreement, weight sensitivity, check process, generate report scaffold). It enforces a hard safety boundary: never use for hiring, promotion, tenure, admissions, grants, prizes, discipline, or any high-impact personnel decision; never rank people or reduce them to composite scores. It prohibits scoring based on metrics like impact factor, h-index, citations, or prestige. Data handling is strictly local: scripts accept only JSON/CSV with pseudonymous IDs and bounded ratings; no network, credentials, external models, or subprocesses. The skill references ScholarEval as an experimental framework, not validated psychometrics, and requires human review before releasing organizational reports.",
    "description_zh": "该技能提供以定性为先、证据可追踪的学术作品（论文、草稿、方案、文献综述、研究想法）发展性评审，并可对低风险研究评估量规进行审计，附带可选的本地质量控制。它通过捆绑的 Python 脚本（验证量规、计算分数、检查可追溯性、汇总一致性、权重敏感性、检查流程、生成报告框架）提供可选的本地质量检查。它强制执行硬性安全边界：切勿用于招聘、晋升、终身教职、招生、资助、奖项、纪律处分或任何高影响力的人事决策；切勿对人员进行排名或将其简化为综合分数。它禁止基于影响因子、h指数、引用次数或声望等指标进行评分。数据处理严格本地化：脚本仅接受包含假名ID和有界评分的JSON/CSV；无网络、凭据、外部模型或子进程。该技能将ScholarEval引用为实验性框架，而非经过验证的心理测量学，并在发布组织报告前要求人工审查。",
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      "signals": {
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