Comparative Synthesis
SkillDev toolsCompare 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.
Use Comparative Synthesis in Claude, ChatGPT or Ahel Desktop
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Also: Claude Code · Cursor · Codex
Then ask your AI: use the Comparative Synthesis skill
Details
Instructions available. Your AI can read the instructions. Execution depends on the setup they require.
Account requirements not reviewed. Check the skill instructions before use; Ahel provides instructions and does not run this skill.
No other account needed.
Add Ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.
What this skill tells your AI
The instructions your AI receives, as published by hashgraph-online/awesome-codex-plugins in plugins/papersflow-ai/papersflow-codex-plugin/skills/comparative-synthesis/SKILL.md and read by Ahel’s review.
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."
Signals
- GitHub stars
- 1k
- Forks
- 316
- Last commit
- Oct 2026
Advanced
- Item type
- skill
- Key
comparative-synthesis- Source
- github.com/hashgraph-online/awesome-codex-plugins
github.com/hashgraph-online/awesome-codex-plugins
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