القائمة
Guided workflow for SQL data analysis using db_tools
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
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.