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Comparative Synthesis

核心功能:聚合多份已完成的DeepScan报告,提取共识、分歧、研究缺口、趋势,支持生成对比可视化图表。典型场景:对比特定主题的多份研究、汇总同主题研究发现、可视化文献数据差异等。输入为用户的DeepScan历史报告(可指定特定报告),输出按共识、分歧等板块结构化呈现,标注文献标题年份,按需附图表。

papersareflowing
papersareflowing
热度 62.74
最近更新 2026-05-11 17:30:39
分类:数据依赖:MCP 服务 / 代码执行 / 内部调用大模型应用场景:研究 / 报告撰写 / 数据处理

安装技能

复制下面的话术发给你的 Agent,它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill: npx secure-skills-cli add comparative-synthesis
Metadata
name
comparative-synthesis
description
Compare and synthesize findings across multiple completed DeepScan reports. Use when the user wants cross-run analysis, trend comparison, or a unified summary from several research sessions.

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.
  2. If the user references specific runs, use get_deepscan_report for each to get full report data.
  3. Identify overlapping papers, conflicting findings, and complementary themes across runs.
  4. 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
  • search_literature — 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."