How do I customize which metrics appear in the Summary?

Modify the dashboard script or template to include or exclude metrics; ensure you reference the same domains and layers and that the data sources align with your model.

What if dashboard.html is not generated?

Check for errors from scripts/generate_dashboard.py, ensure Python dependencies are installed, and confirm you have write permissions and a valid data source.

Can this dashboard be integrated into CI/CD?

Yes. Invoke the generator in the pipeline, then archive the dashboard.html as an artifact and optionally publish a summary to release notes or dashboards.

dashboard

Scanned

npx machina-cli add skill ea-toolkit/architecture-catalog/dashboard --openclaw

Files (1)

SKILL.md

1.1 KB

Generate Architecture Dashboard

Generate the HTML dashboard and summarize key metrics.

Workflow

Run: python scripts/generate_dashboard.py
Confirm dashboard was generated at dashboard.html
Summarize key metrics from the dashboard

Response Format

**Dashboard Generated:** dashboard.html

**Summary:**
| Domain | Maturity | Elements |
|--------|----------|----------|
| customer-management | X/5 | X |
| ... | ... | ... |

**Layer Distribution:**
| Layer | Count |
|-------|-------|
| Application | X |
| Technology | X |
| Business | X |
| ... | ... |

**Health Indicators:**
- Validation errors: X
- Orphan elements: X

Open dashboard.html in a browser to view the full interactive report.

Notes

The dashboard provides a visual overview of model health
Maturity is based on required views per domain (application-landscape, technology-landscape, data-model)
Suggest opening in browser for full experience

Source

git clone https://github.com/ea-toolkit/architecture-catalog/blob/main/.claude/skills/dashboard/SKILL.mdView on GitHub

Overview

Generates an HTML dashboard that visualizes architecture model health, domain maturity, layer statistics, and orphan elements. This provides a concise, shareable view to track progress and spot issues across domains and layers.

How This Skill Works

A Python script (scripts/generate_dashboard.py) builds dashboard.html and the system summarises key metrics from the generated report. It uses domain maturity views (application-landscape, technology-landscape, data-model) and a layer distribution to populate sections like Domain Maturity, Layer Distribution, and Health Indicators for quick assessment.

When to Use It

When you need a quick, visual health snapshot of the architecture model
After updating domain or layer data to verify consistency across views
To identify orphan elements and validation errors for remediation
For stakeholder reporting on architecture maturity and health
When validating the dashboard generation workflow or sharing a dashboard with the team

Quick Start

Step 1: Run python scripts/generate_dashboard.py to build the dashboard
Step 2: Open dashboard.html in your browser to view the report
Step 3: Review the Summary, Layer Distribution, and Health Indicators to interpret results

Best Practices

Run generate_dashboard.py with the latest data before sharing or presenting
Verify dashboard.html exists and reflects current metrics after generation
Ensure Domain Maturity views (application-landscape, technology-landscape, data-model) are included in data sources
Refresh underlying data sources regularly to keep metrics up-to-date
Open dashboard.html in a browser to review interactivity and accuracy of the report

Example Use Cases

A platform team delivers a weekly dashboard showing health, maturity, and orphan counts to leadership
An architecture group validates changes after a major refactor using the dashboard
A project uses the dashboard to track domain maturity across customer-management and other domains
Developers use the Summary section to communicate status in standups
CI/CD pipelines generate and archive dashboard.html for release notes

Frequently Asked Questions

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