dashboard-design-for-trials

What This Skill Does

The dashboard-design-for-trials skill is an automated tool engineered to generate high-fidelity HTML dashboard prototypes for clinical trial monitoring. By leveraging a streamlined Python script, this skill translates clinical trial parameters—such as site counts, target enrollment, and adverse event (AE) metrics—into visually organized, responsive web layouts. It provides an immediate, low-code solution for clinical researchers, data managers, and study leads who require quick visualization of complex study performance indicators. The generated dashboard includes critical components like study overview cards, recruitment progress bars, demographic distribution charts, AE monitoring panels, and study timeline trackers, all encapsulated within a single HTML file without requiring external CSS or JS frameworks.

Installation

To integrate this skill into your environment, use the OpenClaw command-line interface. Ensure you have Python 3.7 or higher installed on your system. Run the following command in your terminal:

clawhub install openclaw/skills/skills/aipoch-ai/dashboard-design-for-trials

Use Cases

• Pre-Study Planning: Visualize proposed site distribution and recruitment targets before final protocol approval.
• Weekly Progress Reporting: Generate rapid snapshots of clinical trial metrics for internal project reviews or sponsor communications.
• Data Monitoring Committees (DMC): Create quick-look HTML pages for preliminary review of AE rates and site compliance.
• Presentation Support: Quickly sketch out dashboard prototypes to communicate requirements to technical web development teams.

Example Prompts

1. "Design a dashboard for the 'HEART-STUDY-2024' with 20 active sites, targeting 500 patients, current enrollment at 210, and an AE count of 5. Output as heart_study_overview.html."
2. "I need a quick sketch of a clinical trial dashboard for a Phase II study named 'Onco-Guard' with 8 sites and 80 patients currently enrolled out of 200. Save it to my workspace as onco_progress.html."
3. "Generate a dashboard file for a new trial with default settings to test the layout formatting."

Tips & Limitations

• No External Dependencies: Since the tool relies on the standard Python library, it is highly portable but does not pull live data from external databases; it is a design and prototyping tool.
• File Management: Always specify a clear output path in your workspace to avoid overwriting existing reports.
• Scope: While the layout is responsive, it is intended for sketching and visualization purposes; for production-grade production environments with live database connectivity, custom front-end development is recommended.
• Validation: Ensure that integer inputs for site counts and enrollment are positive numbers to prevent formatting issues in the resulting HTML output.