ResearchMonitor

Monitors research topics for new papers, conferences, and journals.

7 stars

Best use case

ResearchMonitor is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Monitors research topics for new papers, conferences, and journals.

Teams using ResearchMonitor should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/researchassistant/SKILL.md --create-dirs "https://raw.githubusercontent.com/Demerzels-lab/elsamultiskillagent/main/public/skills/eksubin/researchassistant/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/researchassistant/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How ResearchMonitor Compares

Feature / AgentResearchMonitorStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Monitors research topics for new papers, conferences, and journals.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# ResearchMonitor

This skill helps you keep the user updated on their specific research field.

## Workflow

1.  **Check Configuration**:
    -   Read `research_config.json` in this directory to find the user's research topics and last checked date.
    -   If the file doesn't exist or topics are empty, ask the user what research topics they are interested in and save them using `scripts/daily_briefing.py --add-topic "topic"`.

2.  **Daily Check**:
    -   Get the current date.
    -   Compare with `last_checked` in `research_config.json`.
    -   If already checked today, do nothing unless explicitly asked.

3.  **Perform Search**:
    -   For each topic, use `search_web` to look for:
        -   "new research papers [topic] [current month/year]"
        -   "upcoming conferences [topic] [current year]"
        -   "new journal issues [topic] [current month/year]"
        -   Check specialized platforms like arXiv, IEEE Xplore, Google Scholar (via web search), or X (Twitter) if relevant.

4.  **Filter & Analyze**:
    -   For each potential item found, use `scripts/daily_briefing.py --check-seen "URL or Unique Title"`.
    -   If it returns "true", SKIP IT.
    -   Compare found items with what might have been seen yesterday (this requires some memory or just checking if the publication date is very recent, e.g., last 24-48 hours).
    -   **CRITICAL**: If there is *nothing significantly new* (no new major papers, no new conference announcements), **DO NOT BOTHER THE USER**.

5.  **Report**:
    -   If new items are found, compile a brief markdown report.
    -   Include:
        -   **Title**: News/Paper Title
        -   **Source**: URL/Journal Name
        -   **Summary**: 1-sentence summary of why it's relevant.
    -   Present this to the user.
    -   Mark the items as seen using `scripts/daily_briefing.py --mark-seen "URL or Unique Title"`.
    -   Update the `last_checked` date using `scripts/daily_briefing.py --update-date`.

## Scripts

-   `python scripts/daily_briefing.py --add-topic "topic"`: Adds a new research topic.
-   `python scripts/daily_briefing.py --list-topics`: Lists current topics.
-   `python scripts/daily_briefing.py --update-date`: Updates the last checked timestamp to now.
-   `python scripts/daily_briefing.py --check-seen "ID"`: Checks if an item ID (URL/Title) is already in memory.
-   `python scripts/daily_briefing.py --mark-seen "ID"`: Marks an item ID as seen.

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