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Deepscan monitor

Skill papersflow-ai/papersflow-skills/skills/deepscan-monitor

Reusable PapersFlow research skills for literature search, citation graph exploration, and DeepScan monitoring powered by papersflow-mcp.

Install
npx -y skills add papersflow-ai/papersflow-skills --skill deepscan-monitor

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Run and monitor PapersFlow DeepScan jobs. Use when the user wants long-running research progress, intermediate findings, final reports, or plotting from a completed run.

SKILL.md

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DeepScan Monitor

Use this skill when the user wants Claude to manage a longer-running PapersFlow research workflow instead of a single search call.

Workflow

  1. Use run_deepscan to start the job.
  2. Immediately tell the user that the run is asynchronous.
  3. Poll with get_deepscan_live_snapshot for the best live view of:
    • progress
    • status message
    • checkpoint state
    • top papers
    • partial summary
    • key findings
  4. Fall back to get_deepscan_status if the user only wants lightweight progress checks.
  5. Once finalReportAvailable is true or the run is completed, call get_deepscan_report.
  6. Use summarize_evidence when the user wants a cross-report summary from stored DeepScan history.
  7. Use run_python_plot only after you have stable report data worth plotting.

Important Behavior

  • Do not imply the MCP server will push completion notifications into Claude automatically.
  • Poll deliberately and explain that the run is being checked.
  • Prefer get_deepscan_live_snapshot over get_deepscan_status when the user wants richer live information.
  • If a report is not ready yet, say that clearly and keep the next action obvious.

Progress Update Style

When a run is still active, summarize:

  • current status
  • progress percentage
  • current stage or status message
  • any checkpoint question
  • notable live papers
  • key findings if available

Keep updates brief unless the user asks for more detail.

Plotting Guidance

Use run_python_plot only for meaningful visualizations after you have stable report outputs, for example:

  • papers by year
  • citation distribution
  • venue distribution
  • grouped comparison across a small number of finished runs

Do not generate plots for sparse or obviously low-quality data without saying so.

Examples

  • User asks: "Run a DeepScan on evaluation benchmarks for agentic retrieval systems and keep me posted."
  • User asks: "Check how my DeepScan is progressing and tell me the key findings so far."
  • User asks: "The run is finished, summarize the final report and plot papers by year."
  • User asks: "Summarize the evidence from my recent DeepScan reports on protein structure prediction."

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