Assign project judges by analyzing expert information and group directions.
npx skills add https://github.com/kmoonn/skills --skill judge-assignment
Please help me install this Agent Skill. Skill: judge-assignment Source: https://github.com/kmoonn/skills/tree/main/skills/judge-assignment Command: npx skills add https://github.com/kmoonn/skills --skill judge-assignment
本 Skill 通过读取专家信息与项目分组数据，提供半自动的评委分配工作流，提升分组评审的学科匹配度和效率，并给出可验证的结果。
将专家信息Excel命名为 专家信息.xlsx，分组信息Excel命名为 项目分组.xlsx，然后运行分配脚本得到初步分配结果。
High-intent search queries and answers about installing and using this skill.
To assign judges, parse expert information and project group Excel files to calculate disciplinary matching scores, then apply AI-driven allocation to output a multi-sheet Excel with assignment details and statistics.
Balancing internal and external experts in academic review groups is achieved by an AI allocation rule that prioritizes optimal matches for external experts first, then fills remaining slots with internal experts.
Yes, you can use pandas to automate judge assignments by processing expert and group data, generating a color-coded multi-sheet Excel file that includes assignment details, expert breakdowns, and statistical summaries.
Yes, the judge assignment workflow supports configurable targets per group, allowing you to specify exact reviewer quantities before the AI-driven allocation processes the disciplinary matching and expert balancing.
Disciplinary matching scores work by applying a mapping table to analyze expert information and group directions, maximizing disciplinary alignment to ensure the highest relevance between assigned judges and project topics.
Input Excel files for the judge assignment workflow should be named 专家信息.xlsx for expert information and 项目分组.xlsx for project groups, containing the data needed to calculate matching scores and perform allocation.
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