What is considered a validation error?

Validation errors occur when diagrams reference elements that are not registered.

How do I get JSON output?

Run python scripts/validate.py --format json.

Are orphans always a problem?

Orphans are normal for incomplete models; they indicate elements registered but not diagrammed. Validation errors indicate cross-check failures that should be addressed.

validate

Scanned

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

Files (1)

SKILL.md

1.2 KB

Validate Architecture Model

Run the validation script and interpret results for the user.

Workflow

Run: python scripts/validate.py
Parse the output
Summarize for the user:
- Total elements in registry
- Validation errors (elements in diagrams but not registered)
- Orphan elements (registered but not in any diagram)
- Domain maturity scores

Response Format

**Validation Results**

| Metric | Count |
|--------|-------|
| Registry entries | X |
| Validation errors | X |
| Orphan elements | X |

**Errors (if any):**
[List each error with file location]

**Orphans by layer:**
[Group orphans by ArchiMate layer]

**Recommendations:**
[Suggest fixes for errors, note that orphans are expected for incomplete models]

Notes

Orphan elements are normal for incomplete models - don't alarm the user
Validation errors are more serious - these indicate diagrams reference unregistered elements
If user asks for JSON output, run: python scripts/validate.py --format json

Source

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

Overview

Runs the architecture validation script to compare registry entries against diagrams, reporting orphans, errors, and domain maturity scores. This helps ensure diagram-consistent governance and highlights gaps for remediation.

How This Skill Works

Execute python scripts/validate.py to perform the checks, then parse the output to extract metrics such as total registry entries, validation errors, orphan elements, and domain maturity scores. For automation, use python scripts/validate.py --format json to obtain machine-readable results.

When to Use It

Before sharing or auditing your architecture model to confirm consistency.
When diagrams reference elements not present in the registry (validation errors).
When identifying elements registered but not included in any diagram (orphans).
For ongoing health checks and to track domain maturity scores over time.
When you need a machine-readable report for dashboards or CI pipelines.

Quick Start

Step 1: Run python scripts/validate.py to generate results.
Step 2: (Optional) Run python scripts/validate.py --format json to obtain machine-readable output.
Step 3: Review the Errors and Orphans sections and update the registry or diagrams accordingly.

Best Practices

Run validation after every major diagram update.
Review 'Errors' with file locations to quickly locate source issues.
Group 'Orphans by layer' to prioritize remediation.
Keep the registry synchronized with diagrams; resolve orphans that are unintended.
Use domain maturity scores to drive modeling improvements and gating.

Example Use Cases

Total elements in registry: 120; Validation errors: 6; Orphan elements: 14; Domain maturity scores indicate gaps in the 'Implementation' layer.
Orphans grouped by layer: 9 in Technology, 5 in Business, 0 in Motivation; remediation planned by priority.
CI integration: Running validate in CI outputs JSON payload that feeds a dashboard.
Validation errors point to diagrams referencing unregistered elements; after registration updates, errors drop to zero.
Ongoing health check reduces orphan counts from 20 to 8 over 3 sprints with registry-diagram alignment.

Frequently Asked Questions

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