Module: Abstract
Trigger: abstract, 摘要, abstract structure, 摘要结构, check abstract, polish abstract, abstract diagnosis, 润色摘要, abstract review
Commands
uv run python -B scripts/analyze_abstract.py main.tex # thesis 骨架诊断(默认)
uv run python -B scripts/analyze_abstract.py main.tex --degree master # 硕士字数阈值
uv run python -B scripts/analyze_abstract.py main.tex --bilingual # + 中英摘要一致性
uv run python -B scripts/analyze_abstract.py main.tex --max-chars 1500 # 覆盖字数上界
uv run python -B scripts/analyze_abstract.py main.tex --model five --lang en --max-words 250
uv run python -B scripts/analyze_abstract.py main.tex --jsonDetails
默认 --model thesis:诊断中文学位论文摘要骨架(对象定位首句 → 痛点段 → 总起句冒号 收束 → 编号工作段 → 可选收尾段),13 项 T-* 检查见 ../writing/abstract-structure.md 的「学位论文摘要骨架(thesis 模型)」节。本技能只服务学位 论文,故 thesis 为默认;--model five 保留会议论文口径的五要素模型(Background/Objective/ Methods/Results/Conclusion)作后备(五要素对博士摘要会系统性误报,如 Results 无数值判 MISSING, 而合规博士摘要常定性收口)。
字数阈值对齐 check_spec 的燕山校规常量:--degree doctor(默认)900~1200 字、 --degree master 500~650 字;--max-chars 显式传入时覆盖上界。两处常量一致性由单测锁定。
中英摘要一致性(--bilingual):thesis 模式下额外比对英文 Abstract 与中文摘要—— B-ORD(序词对齐)/ B-NUM(数值集合一致,Error)/ B-ENUM(编号条数一致)/ B-LEN(英摘缺失 过短)为 [Script];B-SEM(逐句语义对应)为 [LLM] lane。时态/语态不在此实现,报告尾注 指路 deai 模块的英文摘要区域门控时态检测(deai trace 不流入本模块)。
For Chinese thesis writing, also check whether abstract, innovation/contribution claims, and conclusion form a three-way closure. See ../writing/thesis-writing-guide.md.
thesis 模式逐项输出检查码 + 级别 + 证据引文 + 建议;--model five 逐要素输出 PRESENT / VAGUE / MISSING。
Skill-layer response:
- Format the diagnosis as a structured report with ✅ / ⚠️ / ❌ markers
- Provide specific revision suggestions for VAGUE or MISSING elements
- If the user requests polishing, generate a revised abstract with [REVISED: ...] annotations
- Never fabricate data or add claims not in the original
Thesis-specific closure:
- 摘要:研究问题、方法、结果、意义是否完整。
- 创新点/主要贡献:是否与摘要中的方法和结果一致。
- 总结与展望:是否回应摘要和绪论中的贡献,并给出局限边界。
See also: abstract-structure.md for the 学位论文摘要骨架(thesis 模型)section (T-/B- checks) and the legacy five-element model with detection heuristics. 结论章内容检查见 conclusion.md。