Data Lineage Agent Skill
Prompt from prompts.chat: Data Lineage Agent Skill
Prompt Content
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name: data-lineage-agent description: A skill for creating an agent to analyze data lineage and linkage across database scripts and stored procedures.
Data Lineage Agent Skill
Purpose
This skill assists in creating an agent that can analyze and report on the data lineage and linkage within a database system. It is ideal for understanding how changes to tables can affect the overall system and helps in uncovering the dependencies across different platforms.
Steps to Create the Agent
-
Access the Repository:
- Link to the GitHub repository: GitHub Repo
- Clone the repository to access all database scripts and stored procedures.
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Analyze Data Lineage:
- Use tools to parse SQL scripts to identify table relationships and dependencies.
- Map out the data flow from source tables to final tables.
-
Identify Changes Impact:
- Implement logic to trace changes in intermediate tables to see which final tables are affected.
- Use graph databases or lineage analysis tools for better visualization and impact assessment.
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Host the Agent:
- Choose a hosting platform (e.g., AWS, Azure) to deploy the agent for continuous analysis and reporting.
Use Cases
- Impact Analysis: Determine the impact of changes in any table across the system.
- Data Flow Mapping: Visualize how data moves through the system from source to final tables.
- Dependency Reporting: Generate reports on table dependencies and affected platforms.
Additional Features
- Automated Alerts: Notify users when potential impacts are detected.
- Version Control Integration: Link changes to specific commits in the repository for traceability.
Example Variables
${repositoryUrl}: The URL of the GitHub repository.${platforms}: List of platforms involved in the data flow.
This skill provides a structured approach to building an agent capable of comprehensive data lineage analysis, which can be crucial for database management and optimization tasks.
Use Cases
Reference Output
No standard answer available; manual review by scoring dimensions is recommended.
Scoring Rubric
Focus on evaluating executability, factual accuracy, boundary control, and structural completeness.
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