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Guided workflow for SQL data analysis using db_tools
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Basé sur la classification professionnelle SOC
Generate MetricFlow metrics from natural language business descriptions
Author MetricFlow semantic model YAML from database tables with validation and Knowledge Base publishing
Optional semantic-model profiling workflow that mines historical SQL and bounded column distributions before YAML authoring
Build the project's vector-indexed knowledge base from files plus database metadata — optionally scoped to specific files / tables / datasources / domains. Scan the in-scope material, classify it into business domains, explore each domain's tables and docs in parallel with explore subagents (the validated-query SQL corpus is enumerated directly, no explore needed), then (after the user confirms a generation manifest — or directly, in the same turn, when the user has waived confirmation) route every artifact to its store via storage-classify, generating semantic_models / metrics / reference_sql (and mining any extra knowledge), and refresh AGENTS.md's KB index. The lightweight /init handles the AGENTS.md inventory plus file-based knowledge/memory; this skill owns the heavy vector-store generation.
Create new Datus skills from scratch. Use when users want to build a new skill, scaffold a skill directory, or capture a workflow as a reusable skill. Trigger phrases include "create a skill", "make a skill for", "turn this into a skill", "new skill".
Activate when the gen_job agent detects that the source and target databases differ. Covers cross-database transfer lifecycle - type mapping via adapter Mixin hints, DDL generation, data transfer via transfer_query_result, and lightweight reconciliation.
| name | sql-analysis |
| description | Guided workflow for SQL data analysis using db_tools |
| tags | ["sql","analysis","workflow"] |
| version | 1.0.0 |
This skill guides you through a structured data analysis workflow using database tools.
First, understand the database schema:
Use db_tools.list_tables() to see available tables
Use db_tools.describe_table(table_name) for each relevant table
Explore the data with sample queries:
Use db_tools.execute_sql("SELECT * FROM {table} LIMIT 10")
Based on the user's question, construct and execute the analysis query.
Analyze the results and provide insights.