Comparative Synthesis
Use this skill when the user wants to compare, contrast, or synthesize findings across multiple completed DeepScan runs rather than monitor a single active job.
Workflow
- 1. Use
summarize_evidence to pull cross-report summaries from the user's DeepScan history. - If the user references specific runs, use
get_deepscan_report for each to get full report data. - Identify overlapping papers, conflicting findings, and complementary themes across runs.
- Use
run_python_plot to visualize comparisons when the data supports it.
Output Style
Structure the synthesis around:
- - Common ground — papers, methods, or findings that appear across multiple runs
- Divergences — where different runs reached different conclusions or surfaced different literature
- Gaps — topics or questions that no run adequately covered
- Trends — temporal patterns, emerging methods, or shifting consensus visible across runs
Keep sections short and reference specific papers by title and year.
Tool Guidance
Use summarize_evidence
Call this first. It aggregates across the user's stored DeepScan history and is the fastest way to get a cross-run view.
Use for:
- - "What do my recent DeepScans say about X?"
- "Summarize everything I've researched on topic Y"
- "Compare findings across my last three runs"
Use get_deepscan_report
Call for specific runs when the user wants:
- - side-by-side comparison of two named runs
- detailed data from a particular session that
summarize_evidence condensed too aggressively
Use run_python_plot
Use after you have structured data from reports. Good comparison plots include:
- - paper overlap Venn or bar chart across runs
- citation count distributions side by side
- publication year histograms per run
- venue frequency comparison
- topic/method co-occurrence heatmap
Only plot when there is enough data to be meaningful. Say so if the data is too sparse.
Do NOT use
- -
run_deepscan — this skill synthesizes completed runs, not starts new ones - INLINECODE8 — use the existing DeepScan data, not new searches
Examples
- - User asks: "Compare my DeepScan on transformer efficiency with the one on model distillation."
- User asks: "What themes keep showing up across all my recent research sessions?"
- User asks: "Plot the publication year distribution from my last two DeepScans side by side."
- User asks: "Synthesize everything I've researched on protein folding this month."
比较综合
当用户希望比较、对比或综合多个已完成DeepScan运行的结果,而非监控单个进行中的任务时,请使用此技能。
工作流程
- 1. 使用summarizeevidence从用户的DeepScan历史中提取跨报告摘要。
- 如果用户提及特定运行,使用getdeepscanreport获取每个运行的完整报告数据。
- 识别各运行之间重叠的论文、冲突的发现以及互补的主题。
- 当数据支持时,使用runpython_plot可视化比较结果。
输出风格
围绕以下内容构建综合报告:
- - 共同基础——跨多个运行出现的论文、方法或发现
- 分歧点——不同运行得出不同结论或呈现不同文献的地方
- 空白点——任何运行都未充分涵盖的主题或问题
- 趋势——跨运行可见的时间模式、新兴方法或共识变化
保持各节简短,并引用具体论文的标题和年份。
工具指南
使用summarize_evidence
首先调用此工具。它汇总用户存储的DeepScan历史,是获取跨运行视图的最快方式。
适用于:
- - 我最近的DeepScan对X有什么看法?
- 总结我研究过的关于主题Y的所有内容
- 比较我最近三次运行的发现
使用getdeepscanreport
当用户需要特定运行的数据时调用:
- - 两个指定运行的并排比较
- summarize_evidence过度压缩的特定会话的详细数据
使用runpythonplot
在从报告中获取结构化数据后使用。适合比较的图表包括:
- - 跨运行的论文重叠韦恩图或柱状图
- 引用次数分布并排显示
- 每次运行的发表年份直方图
- 发表场所频率比较
- 主题/方法共现热力图
仅在数据足够有意义时绘制图表。如果数据过于稀疏,请明确说明。
请勿使用
- - rundeepscan——此技能综合已完成运行,而非启动新运行
- searchliterature——使用现有DeepScan数据,而非进行新搜索
示例
- - 用户问:比较我关于Transformer效率的DeepScan和关于模型蒸馏的DeepScan。
- 用户问:哪些主题在我最近的所有研究会话中反复出现?
- 用户问:并排绘制我最近两次DeepScan的发表年份分布。
- 用户问:综合我本月研究过的关于蛋白质折叠的所有内容。