Input Validation

What This Skill Does

The Input Validation skill for OpenClaw is a robust framework designed to ensure the integrity of data entering your construction workflows. In the construction industry, data quality is paramount; processing inaccurate cost estimates, flawed project schedules, or corrupted BIM exports can lead to cascading delays and significant financial risks. This skill provides a programmable validation engine that acts as a gatekeeper, inspecting incoming data against domain-specific business logic before it reaches your core processing pipeline.

Built upon a dataclass-driven architecture, the skill classifies issues into Errors (blocking), Warnings (advisory), and Info (supplementary). By leveraging structured validators for CSI divisions, date ranges, and industry-standard formatting, the agent can automatically flag anomalies that human eyes might miss. It provides clear, actionable feedback and suggestions for remediation, enabling project teams to maintain a 'clean room' approach to data management.

Installation

To install the Input Validation skill into your OpenClaw environment, execute the following command via your terminal:

clawhub install openclaw/skills/skills/datadrivenconstruction/input-validation

Ensure that your OpenClaw CLI is updated to the latest version to maintain compatibility with the validation framework dependencies.

Use Cases

• Cost Estimation: Cross-reference line-item costs against historical square-foot averages based on CSI MasterFormat divisions to detect outliers or data entry errors.
• Schedule Integrity: Validate CPM schedules to ensure that activity durations are logical, dependencies are sequenced correctly, and milestone dates fall within contract constraints.
• BIM Data Auditing: Parse BIM exports to verify that required object parameters are populated and follow project-specific naming conventions before integrating with cost management tools.
• Field Reporting: Automatically sanitize and validate daily logs from the field to ensure that worker hours and equipment usage data match expected project scopes.

Example Prompts

1. 'Validate the attached Excel cost estimate against the current project budget guidelines and flag any CSI division deviations exceeding 15%.'
2. 'Check this Primavera P6 schedule export for any missing successor relationships or illogical constraints on the critical path.'
3. 'Review the incoming field data for the concrete pour; alert me if the total cubic yardage reported deviates from the BIM model volume.'

Tips & Limitations

• Proactive Thresholds: Set your severity levels appropriately. Use 'Error' for data that would cause a crash or invalid invoice, and 'Warning' for estimations that simply fall outside standard historical norms.
• Continuous Learning: The current implementation uses static ranges (e.g., CSI cost buckets). Consider augmenting this skill with your own organization’s historical project benchmarks to increase accuracy.
• Limitations: This skill focuses on the syntax and structural integrity of data. It does not perform physical site verification or replace the necessity of professional engineering judgment for complex design validation.