Load company ontologies and metric definitions to clarify data queries.
npx skills add https://github.com/appunite/skills --skill shugyo-mcp
Please help me install this Agent Skill. Skill: shugyo-mcp Source: https://github.com/appunite/skills/tree/main/shugyo-mcp Command: npx skills add https://github.com/appunite/skills --skill shugyo-mcp
This Skill helps users understand their company's data model and metrics before executing queries, preventing misinterpretation and errors in data retrieval.
Ask the user what they want to know about their company data, ensuring clarity before proceeding with data queries.
High-intent search queries and answers about installing and using this skill.
To clarify business metrics and terminology before querying company data, load the company's ontology and processes to understand data meaning, identify ambiguities in user queries, and ask precise follow-up questions to ensure accurate data retrieval.
Misinterpretation errors in SQL queries for business reporting occur when the data model and business definitions are not understood. You can prevent these errors by loading company-specific ontologies and metric definitions to build accurate business context before querying.
Business ontology understanding improves data query accuracy by applying vetted metrics, tables, and business definitions to the query generation process. This ensures the analytics workflow uses the correct business context, preventing data misinterpretation and retrieval errors.
Yes, you need company-specific ontologies and metric definitions to use this data modeling approach. The process requires integration with these definitions through API calls to build the necessary business context for accurate data queries and analytics workflows.
The best way to build business context for data query clarification is to load the company's data models, metrics, and terminology first. This approach identifies gaps in user queries and asks precise follow-up questions to ensure reliable data retrieval.
Querying company data without understanding the business ontology leads to misinterpretation and errors in data retrieval. Without loading company-specific metrics and terminology, the generated SQL queries will lack the necessary business context to produce reliable analytics results.
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