🧪 Skills
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 co...
v0.1.0
Description
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
- Use
run_deepscanto start the job. - Immediately tell the user that the run is asynchronous.
- Poll with
get_deepscan_live_snapshotfor the best live view of:- progress
- status message
- checkpoint state
- top papers
- partial summary
- key findings
- Fall back to
get_deepscan_statusif the user only wants lightweight progress checks. - Once
finalReportAvailableis true or the run is completed, callget_deepscan_report. - Use
summarize_evidencewhen the user wants a cross-report summary from stored DeepScan history. - Use
run_python_plotonly 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_snapshotoverget_deepscan_statuswhen 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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