standards-compliance-checker
Check data compliance with construction standards. Validate data against ISO 19650, IFC, COBie, UniFormat standards.
Why use this skill?
Automate your construction data compliance with OpenClaw. Validate ISO 19650, IFC, and COBie standards instantly to ensure project data accuracy.
Install via CLI (Recommended)
clawhub install openclaw/skills/skills/datadrivenconstruction/standards-compliance-checkerWhat This Skill Does
The standards-compliance-checker is a specialized AI agent skill designed to automate the verification of construction data against industry-standard protocols. In an industry defined by complex regulatory frameworks, this tool ensures that your project data aligns with international requirements such as ISO 19650, IFC (Industry Foundation Classes), COBie (Construction Operations Building information exchange), UniFormat, OmniClass, and MasterFormat. By automating the validation process, the skill eliminates human error, ensures data interoperability, and prevents costly downstream issues caused by non-compliant documentation.
The skill operates by scanning input datasets or file metadata against a robust library of predefined rules. It returns a detailed ComplianceReport that categorizes results by severity, identifying non-compliant fields, missing required attributes, or incorrect naming conventions before they ever enter the Common Data Environment (CDE).
Installation
To install the skill, use the following command in your terminal or OpenClaw interface:
clawhub install openclaw/skills/skills/datadrivenconstruction/standards-compliance-checker
Use Cases
- Automated Validation of BIM deliverables: Ensure all uploaded IFC files meet organizational naming and structure requirements.
- Pre-submission Audit: Run a check on COBie spreadsheets before delivering data to the client to ensure all facility information is present.
- Automated Governance: Enforce ISO 19650 naming conventions across all project files in a CDE to maintain consistent file structures throughout the project lifecycle.
- Data Cleaning: Identify objects within IFC models that lack mandatory ObjectTypes or naming attributes before running cost estimation or scheduling simulations.
Example Prompts
- "Analyze this IFC file and generate a compliance report against the current IFC standard, specifically checking for required Name and ObjectType fields."
- "Check the current project folder for any files that violate the ISO 19650 naming convention and provide a list of the specific files that need renaming."
- "Validate this COBie dataset against standard facility requirements and highlight any missing mandatory fields in the component tab."
Tips & Limitations
- Tip: Use the compliance report severity levels to prioritize fixes. Address 'MAJOR_ISSUES' before 'MINOR_ISSUES' to ensure critical regulatory compliance first.
- Tip: Integrate this skill into your CI/CD pipelines for construction data to catch errors at the time of upload.
- Limitation: The skill currently requires well-structured input data. While it validates formats effectively, it cannot 'guess' missing data that is architecturally ambiguous or incomplete in the source model.
- Limitation: Ensure your model exported formats match the versions supported by the checker to avoid schema mismatch errors during analysis.
Metadata
Not sure this is the right skill?
Describe what you want to build — we'll match you to the best skill from 16,000+ options.
Find the right skillPaste this into your clawhub.json to enable this plugin.
{
"plugins": {
"official-datadrivenconstruction-standards-compliance-checker": {
"enabled": true,
"auto_update": true
}
}
}Tags(AI)
Flags: file-read
Related Skills
data-lineage-tracker
Track data origin, transformations, and flow through construction systems. Essential for audit trails, compliance, and debugging data issues.
cwicr-cost-calculator
Calculate construction costs using DDC CWICR resource-based methodology. Break down costs into labor, materials, equipment with transparent pricing.
data-anomaly-detector
Detect anomalies and outliers in construction data: unusual costs, schedule variances, productivity spikes. Statistical and ML-based detection methods.
historical-cost-analyzer
Analyze historical construction costs for benchmarking, trend analysis, and estimating calibration. Compare projects, track escalation, identify patterns.
df-merger
Merge pandas DataFrames from multiple construction sources. Handle different schemas, keys, and data quality issues.