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Install in OpenClaw
/install dashboard-design-for-trials
Description
Design dashboard layout sketches for clinical trials showing enrollment progress and adverse event rates
README (SKILL.md)
Dashboard Design for Trials
Design layout sketches for clinical trial data monitoring panels, displaying recruitment progress, AE incidence rates, and other key metrics.
Features
- Generate HTML layout sketches for clinical trial Dashboards
- Support multiple chart types: progress bars, line charts, pie charts, bar charts, etc.
- Customizable study protocol, site count, key metrics
- Responsive design, adaptable to different screen sizes
Usage
python scripts/main.py [options]
Parameters
| Parameter | Type | Default | Required | Description |
|---|---|---|---|---|
--study-id |
string | STUDY-001 | No | Study ID |
--study-name |
string | Clinical Trial A | No | Study Name |
--sites |
int | 10 | No | Number of sites |
--target-enrollment |
int | 100 | No | Target enrollment count |
--current-enrollment |
int | 45 | No | Current enrollment count |
--ae-count |
int | 12 | No | Adverse event count |
--output |
string | dashboard.html | No | Output HTML file path |
Examples
# Generate default Dashboard
python scripts/main.py
# Customize study parameters
python scripts/main.py \
--study-id "PHASE-III-2024" \
--study-name "Phase III Clinical Trial of New Drug for Type 2 Diabetes" \
--sites 15 \
--target-enrollment 300 \
--current-enrollment 120 \
--ae-count 25 \
--output my_dashboard.html
Output
Generates an HTML Dashboard containing the following modules:
- Study Overview Card - Study ID, name, status
- Recruitment Progress - Overall progress bar, site-by-site progress comparison
- Subject Distribution - Gender, age distribution pie charts
- AE Monitoring - Adverse event incidence rate, severity distribution
- Data Quality - CRF completion rate, query count
- Timeline - Study milestones, estimated completion date
Dependencies
- Python 3.7+
- No additional dependencies (pure standard library generates HTML/CSS/JS)
Author
Skill ID: 194
Risk Assessment
| Risk Indicator | Assessment | Level |
|---|---|---|
| Code Execution | Python/R scripts executed locally | Medium |
| Network Access | No external API calls | Low |
| File System Access | Read input files, write output files | Medium |
| Instruction Tampering | Standard prompt guidelines | Low |
| Data Exposure | Output files saved to workspace | Low |
Security Checklist
- No hardcoded credentials or API keys
- No unauthorized file system access (../)
- Output does not expose sensitive information
- Prompt injection protections in place
- Input file paths validated (no ../ traversal)
- Output directory restricted to workspace
- Script execution in sandboxed environment
- Error messages sanitized (no stack traces exposed)
- Dependencies audited
Prerequisites
No additional Python packages required.
Evaluation Criteria
Success Metrics
- Successfully executes main functionality
- Output meets quality standards
- Handles edge cases gracefully
- Performance is acceptable
Test Cases
- Basic Functionality: Standard input → Expected output
- Edge Case: Invalid input → Graceful error handling
- Performance: Large dataset → Acceptable processing time
Lifecycle Status
- Current Stage: Draft
- Next Review Date: 2026-03-06
- Known Issues: None
- Planned Improvements:
- Performance optimization
- Additional feature support
Usage Guidance
This appears to be a straightforward local dashboard generator. Before installing/running: (1) review the full script yourself (or have an engineer do so) if you plan to feed it sensitive patient data — the tool writes files to the workspace; (2) run it first with non-sensitive sample inputs to confirm behavior; (3) open the generated HTML offline and inspect it for any external resource links (CDNs or trackers) before viewing in a browser; (4) run in a sandboxed environment if you have concerns about execution. If you need to use real clinical data, ensure compliance with your organization’s data protection policies and consider sanitizing or aggregating inputs first.
Capability Analysis
Type: OpenClaw Skill
Name: dashboard-design-for-trials
Version: 0.1.0
The skill bundle is a legitimate tool designed to generate HTML dashboard sketches for clinical trial monitoring. The core logic in 'scripts/main.py' uses standard Python libraries to generate mock data and format it into a responsive HTML/CSS layout, which is then saved to a local file. There is no evidence of data exfiltration, malicious command execution, or prompt injection attempts in either the code or the documentation.
Capability Assessment
Purpose & Capability
Name/description match the code and SKILL.md. The included Python script generates mock clinical-trial dashboard HTML from CLI arguments; no unrelated services, binaries, or credentials are required.
Instruction Scope
SKILL.md instructs only to run the local Python script with CLI parameters to produce an HTML file. The instructions do not request reading unrelated files, accessing environment secrets, or sending data to external endpoints.
Install Mechanism
No install spec is provided and there are no external downloads. The skill includes a single Python script that uses only the standard library, which is proportionate to the stated task.
Credentials
No environment variables, credentials, or config paths are required. The script accepts CLI parameters only, which is appropriate for a local dashboard generator.
Persistence & Privilege
Skill is not always-on and does not request persistent system privileges or modify other skills. It writes an output HTML file to the workspace (as expected) but does not request elevated permissions.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install dashboard-design-for-trials - After installation, invoke the skill by name or use
/dashboard-design-for-trials - Provide required inputs per the skill's parameter spec and get structured output
Version History
v0.1.0
Initial release: generate clinical trial dashboard layout sketches.
- Creates HTML dashboards showing enrollment, adverse event rates, and key trial metrics.
- Supports multiple chart types (progress bars, line, pie, and bar charts).
- Customizable parameters for study details and output path.
- Pure Python, no extra dependencies required.
- Responsive design for various devices.
- Includes security and risk assessment guidelines.
Metadata
Frequently Asked Questions
What is Dashboard Design For Trials?
Design dashboard layout sketches for clinical trials showing enrollment progress and adverse event rates. It is an AI Agent Skill for Claude Code / OpenClaw, with 246 downloads so far.
How do I install Dashboard Design For Trials?
Run "/install dashboard-design-for-trials" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is Dashboard Design For Trials free?
Yes, Dashboard Design For Trials is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does Dashboard Design For Trials support?
Dashboard Design For Trials is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created Dashboard Design For Trials?
It is built and maintained by AIpoch (@aipoch-ai); the current version is v0.1.0.
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