A specialized diagnostic tool that identifies, maps, and prioritizes the remediation of disconnected data repositories in construction organizations.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install data-silo-detection
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install data-silo-detection using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Data Silo Detection?
Data Silo Detection is an advanced technical skill designed to map the complex data architecture of construction firms. Based on the Data-Driven Construction (DDC) methodology, it identifies where critical information becomes trapped in isolated systems, spreadsheets, or personal silos. By integrating this capability into your Openclaw Skills workflow, you can systematically uncover integration gaps between domains like Design, Cost, and Site operations.
The skill provides a framework to quantify organizational connectivity through automated analysis. It doesn't just find gaps; it calculates a connectivity score and identifies master data sources to reduce redundancy. This makes it an essential asset for digital transformation officers and BIM managers looking to build a unified data environment using Openclaw Skills.
Data Silo Detection Use Cases
- Identifying disconnected Excel spreadsheets that house critical project cost estimates.
- Detecting gaps in data flow between BIM models and procurement systems.
- Mapping personal data stores that pose a risk to organizational knowledge retention.
- Auditing master data redundancy for project entities like budget codes and subcontractors.
- Generating a prioritized technical roadmap for system integrations.
How Data Silo Detection Works
- The user defines a catalog of organizational data sources including their domain, ownership, and existing API capabilities.
- The detection engine builds a connectivity graph to visualize how data flows (or fails to flow) between systems.
- Cross-domain relationships are compared against the DDC reference model to identify missing architectural links.
- The system scans for duplicate data entities across different platforms to determine where data friction occurs.
- A comprehensive analysis report is generated, featuring a connectivity score and a phased integration roadmap.
Data Silo Detection Setup
This skill requires Python 3 and is optimized for win32 environments.
# Ensure python3 is installed
python3 --version
# Install the skill through your Openclaw Skills manager
openclaw install data-silo-detection
Data Silo Detection Data Schema & Taxonomy
The skill utilizes a structured metadata taxonomy to classify the construction data environment:
| Component | Description |
|---|---|
| DataSource | Records system type, domain (Design, Cost, etc.), and access levels. |
| DataSilo | Captures detected isolation issues, severity levels, and impact descriptions. |
| DuplicateData | Tracks entity redundancy across different repositories and identifies master sources. |
| SiloAnalysis | The root object containing the connectivity score, flow gaps, and priority actions. |
Data Silo Detection Advanced Features
- Custom Domain Relationship Mapping to tailor analysis to specific organizational structures.
- Severity Classification Engine that prioritizes silos based on business impact and affected user counts.
- Automated Integration Roadmap Generation covering quick wins, core integration, and long-term optimization.
- Master Data Management (MDM) gap analysis for critical construction entities like RFIs and Change Orders.
SKILL.md
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METADATA
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