L’interface est localisée ; les présentations et documents des Skills sont traduits automatiquement par IA dans votre langue. Le code, les commandes, chemins et termes techniques restent en version originale, et le document de l’auteur fait foi.
Explorer les Skills
Données et recherche · SKILL.md

deepscan-monitor

deepscan-monitor

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.

Independent GitHub snapshot · Automated classification · Original documentation governs

Documentation de la Skill

GitHub

name: deepscan-monitor description: 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.

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."
Skills enregistrées