DeepScan Monitor

SkillMonitoring & ops

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.

Use DeepScan Monitor in Claude, ChatGPT or Ahel Desktop

Free. Sign in, add DeepScan Monitor and connect your AI. About a minute.

Also: Claude Code · Cursor · Codex

Then ask your AI: use the DeepScan Monitor skill

Details

Instructions available. Your AI can read the instructions. Execution depends on the setup they require.

Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.

DeepScan MonitorStart free

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/deepscan-monitor/SKILL.md and read by ahel’s review.

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."

Signals

GitHub stars
1k
Forks
316
Last commit
Oct 2026
Advanced
Item type
skill
Key
deepscan-monitor
Source
github.com/hashgraph-online/awesome-codex-plugins