An expert-level big data engineering skill for multi-engine SQL development, data warehousing, and automated knowledge base maintenance.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install data-analysis-sql
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-analysis-sql 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 Analysis SQL?
The Data Analysis SQL skill is a comprehensive toolkit designed for big data engineers and data analysts. It provides advanced capabilities for writing, debugging, and optimizing complex SQL queries across a wide array of engines including Hive, SparkSQL, ClickHouse, and BigQuery. By leveraging this Openclaw Skills asset, developers can ensure high-performance data processing while adhering to strict architectural standards.
Beyond simple querying, this skill automates critical data engineering tasks such as dimension modeling (ODS/DWD/DWS/ADS), ETL pipeline orchestration, and data quality monitoring. It serves as a bridge between raw data and actionable business metrics, providing structured workflows for knowledge base generation and comprehensive technical documentation.
Data Analysis SQL Use Cases
- Developing and optimizing complex SQL queries for multi-engine environments like Presto, Doris, or Hive.
- Designing robust data warehouse architectures using ODS, DWD, DWS, and ADS layering.
- Implementing ETL pipelines with full, incremental, or CDC data capture strategies.
- Performing automated data quality checks to identify null rates, duplication, and anomalies.
- Generating technical documentation and relational schemas automatically from SQL or Excel sources.
How Data Analysis SQL Works
- Requirement Clarification: The skill initiates by defining business metrics, data sources, and exclusion logic to ensure alignment with goals.
- Engine Selection: It identifies the optimal data engine based on the use case, such as ClickHouse for real-time OLAP or SparkSQL for batch processing.
- Structured Development: SQL is authored using Common Table Expressions (CTE) and validated against a comprehensive safety checklist (tenant keys, delete flags, etc.).
- Performance Tuning: It analyzes data skew, JOIN explosion factors, and full-table scan risks to optimize execution.
- Validation & Deployment: Cross-verifies output with existing reports and triggers automated documentation generation for the knowledge base.
Data Analysis SQL Setup
To integrate this skill into your environment, ensure you have Python installed and clone the repository into your Openclaw Skills directory:
git clone <repository-url>
cd data-analysis-sql
pip install -r requirements.txt
You can then use the provided utility scripts for formatting or diffing:
python scripts/sql_formatter.py --file your_query.sql
python scripts/sql_diff.py --old old_v1.sql --new new_v2.sql
Data Analysis SQL Data Schema & Taxonomy
The skill organizes data and metadata through a structured taxonomy in the references/ directory:
| Category | Component | Description |
|---|---|---|
| SQL Standards | sql-guide.md |
Rules for CTE usage, naming conventions, and formatting. |
| Safety | join-rules.md |
Checklists for tenant isolation and null handling. |
| Modeling | schema-guide.md |
Architecture for data warehouse layering (ODS to ADS). |
| Pipelines | pipeline-patterns.md |
Design patterns for ETL orchestration and recovery. |
| Quality | data-quality.md |
Standards for anomaly detection and data probing. |
Data Analysis SQL Advanced Features
- Multi-Engine Adaptation: Seamlessly migrates and adapts SQL dialects between PostgreSQL, Oracle, and cloud-native engines like BigQuery.
- Automated Knowledge Base: Generates full Markdown documentation from
.xlsxtable structures and extracts table relations directly from SQL files. - SCD Management: Built-in logic for handling Slowly Changing Dimensions and snapshot modeling.
- Indicator Design Templates: Standardized templates for business metrics like Flow Time, lead times, and defect density.
- Interactive Doc Generation: Supports CLI-based interactive tools to generate table schema documentation on the fly.
SKILL.md
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