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papersflow-aipapersflow-ai/papersflow-skills/skills/comparative-synthesis/SKILL.md

comparative-synthesis

Skill4 starsChanged 8 months agoMITView on GitHub

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.

The file

We split this skill into the rules it gives an agent

9 rules. About 0 are common, 6 are distinctive, and 3 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈6

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 3

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Use this skill when the user wants to compare, contrast, or synthesize findings across multiple completed DeepScan runs rather than monitor …

    line 8
  2. Very specific:

    Use summarize_evidence to pull cross-report summaries from the user's DeepScan history.

    line 12
  3. Very specific:

    If the user references specific runs, use get_deepscan_report for each to get full report data.

    line 13
  4. Very specific:

    Use run_python_plot to visualize comparisons when the data supports it.

    line 15
  5. Distinctive:

    Keep sections short and reference specific papers by title and year.

    line 26
  6. Distinctive:

    Call this first. It aggregates across the user's stored DeepScan history and is the fastest way to get a cross-run view.

    line 32
  7. Distinctive:

    Call for specific runs when the user wants:

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  8. Distinctive:

    Use after you have structured data from reports.

    line 49
  9. Distinctive:

    Only plot when there is enough data to be meaningful.

    line 57

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
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."

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