Comparative Synthesis
/install comparative-synthesis
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
- Use
summarize_evidenceto pull cross-report summaries from the user's DeepScan history. - If the user references specific runs, use
get_deepscan_reportfor each to get full report data. - Identify overlapping papers, conflicting findings, and complementary themes across runs.
- Use
run_python_plotto 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_evidencecondensed 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 onessearch_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."
- 确保已安装 OpenClaw(本地或 Docker 部署)
- 在对话框中输入安装命令:
/install comparative-synthesis - 安装完成后,直接呼叫该 Skill 的名称或使用
/comparative-synthesis触发 - 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
Comparative Synthesis 是什么?
Compare and synthesize findings across multiple completed DeepScan reports. Use when the user wants cross-run analysis, trend comparison, or a unified summar... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 261 次。
如何安装 Comparative Synthesis?
在 OpenClaw 或 Claude Code 对话框中运行命令「/install comparative-synthesis」即可一键安装,无需额外配置。
Comparative Synthesis 是免费的吗?
是的,Comparative Synthesis 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。
Comparative Synthesis 支持哪些平台?
Comparative Synthesis 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。
谁开发了 Comparative Synthesis?
由 PapersFlow(@papersareflowing)开发并维护,当前版本 v0.1.0。