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papersareflowing

Deepscan Monitor

by PapersFlow · GitHub ↗ · v0.1.0 · MIT-0
cross-platform ✓ Security Clean
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Install in OpenClaw
/install 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 co...
README (SKILL.md)

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."
Usage Guidance
This skill appears to be what it says: a set of instructions for monitoring DeepScan jobs that uses platform-provided operations. Before installing, confirm that your agent environment actually exposes the named operations (run_deepscan, get_deepscan_live_snapshot, etc.) and that any PapersFlow API credentials (if required) are provided separately and scoped appropriately. Be aware the skill will poll the service for updates (causing repeated API calls) and may run plotting via run_python_plot — confirm plotting is performed in a safe sandbox and that no private data will be sent to unknown external endpoints.
Capability Analysis
Type: OpenClaw Skill Name: deepscan-monitor Version: 0.1.0 The deepscan-monitor skill bundle provides standard operational instructions for an AI agent to manage long-running research workflows. The SKILL.md file outlines a legitimate workflow for starting jobs, polling for status updates via get_deepscan_live_snapshot, and generating visualizations with run_python_plot once data is available. No indicators of data exfiltration, malicious execution, or harmful prompt injection were found.
Capability Assessment
Purpose & Capability
Name/description (monitoring DeepScan jobs) match the SKILL.md steps (start run, poll live snapshots, fetch reports, summarize, plot). The SKILL.md expects the agent to call named tool-like operations (run_deepscan, get_deepscan_live_snapshot, get_deepscan_status, get_deepscan_report, summarize_evidence, run_python_plot), which is consistent with a monitoring skill.
Instruction Scope
Instructions are narrowly scoped to managing and reporting on DeepScan runs (polling, summarizing, plotting). They do not instruct the agent to read arbitrary files, export secrets, or contact unrelated endpoints. The polling guidance implies repeated API calls, which is expected for long-running job monitoring.
Install Mechanism
No install spec and no code files — this is instruction-only, so nothing is written to disk or downloaded. Lowest-risk install footprint.
Credentials
The skill declares no environment variables or credentials. That is coherent if the platform already exposes the referenced deepscan tool operations to the agent; if PapersFlow requires API keys, those are not declared here — confirm where/how authentication to PapersFlow is provided to the agent.
Persistence & Privilege
always is false (no permanent auto-inclusion). Model invocation is allowed (default) which is normal for skills. The skill does not request any system-wide config changes or elevated privileges.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install deepscan-monitor
  3. After installation, invoke the skill by name or use /deepscan-monitor
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v0.1.0
Initial release of DeepScan Monitor skill. - Enables running and monitoring of long-running PapersFlow DeepScan research jobs. - Supports asynchronous job management with live progress polling and status updates. - Provides users with intermediate findings, top papers, and key summaries during ongoing runs. - Offers comprehensive final report retrieval and evidence summarization across jobs. - Includes guidance for generating plots only when report data is stable and meaningful.
Metadata
Slug deepscan-monitor
Version 0.1.0
License MIT-0
All-time Installs 0
Active Installs 0
Total Versions 1
Frequently Asked Questions

What is 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... It is an AI Agent Skill for Claude Code / OpenClaw, with 240 downloads so far.

How do I install Deepscan Monitor?

Run "/install deepscan-monitor" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is Deepscan Monitor free?

Yes, Deepscan Monitor is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does Deepscan Monitor support?

Deepscan Monitor is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created Deepscan Monitor?

It is built and maintained by PapersFlow (@papersareflowing); the current version is v0.1.0.

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