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Comparative Synthesis

by PapersFlow · GitHub ↗ · v0.1.0 · MIT-0
cross-platform ✓ Security Clean
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Install in OpenClaw
/install 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 summar...
README (SKILL.md)

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."
Usage Guidance
This skill appears coherent for cross-run synthesis and asks for nothing outside that scope. Before installing, confirm what the helper tools do: (1) verify summarize_evidence/get_deepscan_report access is limited to your DeepScan history and not arbitrary filesystem or unrelated services, (2) check run_python_plot's execution environment (can it run arbitrary Python, access files, or send data externally?), and (3) consider that DeepScan reports may contain sensitive or unpublished data—ensure any generated plots or summaries are stored or shared only where you expect. Also note the skill source is unknown/no homepage; if provenance matters, try to get or review the implementations of the referenced helper tools.
Capability Analysis
Type: OpenClaw Skill Name: comparative-synthesis Version: 0.1.0 The comparative-synthesis skill bundle provides instructions for an AI agent to analyze and visualize data from existing research reports. The workflow involves using standard tools like summarize_evidence and run_python_plot to identify trends and gaps across datasets, with no evidence of malicious code, data exfiltration, or harmful prompt injection in SKILL.md or _meta.json.
Capability Assessment
Purpose & Capability
Name/description match the instructions: the skill uses DeepScan-specific helpers (summarize_evidence, get_deepscan_report, run_python_plot) to aggregate and visualize cross-run findings. It does not request unrelated binaries, env vars, or config paths.
Instruction Scope
Instructions stay within the stated scope (aggregate DeepScan history, fetch specific runs, synthesize themes, optionally plot). Minor caveat: the skill delegates plotting to run_python_plot and data aggregation to summarize_evidence/get_deepscan_report — the security properties depend on those tools' implementations (e.g., whether they access other files or external networks).
Install Mechanism
Instruction-only skill with no install steps or code files; nothing is written to disk by the skill itself.
Credentials
No environment variables, credentials, or config paths are requested; requested access is proportional to the task of reading stored DeepScan reports.
Persistence & Privilege
always:false and user-invocable; the skill does not request permanent/always-on presence or elevated agent privileges.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install comparative-synthesis
  3. After installation, invoke the skill by name or use /comparative-synthesis
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v0.1.0
Comparative Synthesis skill (v0.1.0) – New skill for cross-report DeepScan analysis: - Enables comparison and synthesis across multiple completed DeepScan reports. - Provides workflows to summarize evidence, compare specific runs, and visualize trends. - Structured output highlights common findings, divergences, gaps, and trends across research sessions. - Tool guidance clarifies when to use summarization, detailed retrieval, and plotting functions. - Focuses strictly on completed DeepScan data—does not initiate new scans or searches.
Metadata
Slug comparative-synthesis
Version 0.1.0
License MIT-0
All-time Installs 1
Active Installs 1
Total Versions 1
Frequently Asked Questions

What is Comparative Synthesis?

Compare and synthesize findings across multiple completed DeepScan reports. Use when the user wants cross-run analysis, trend comparison, or a unified summar... It is an AI Agent Skill for Claude Code / OpenClaw, with 261 downloads so far.

How do I install Comparative Synthesis?

Run "/install comparative-synthesis" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is Comparative Synthesis free?

Yes, Comparative Synthesis is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does Comparative Synthesis support?

Comparative Synthesis is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created Comparative Synthesis?

It is built and maintained by PapersFlow (@papersareflowing); the current version is v0.1.0.

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