data-context-extractor-th

Language: All user-facing output — responses, summaries, and any text the user will read — must be written in Thai (ภาษาไทย). Internal logic, file paths, code snippets, and technical values remain in English.

Data Context Extractor

A meta-skill that extracts company-specific data knowledge from analysts and generates tailored data analysis skills.

How It Works

This skill has two modes:

1. Bootstrap Mode: Create a new data analysis skill from scratch
2. Iteration Mode: Improve an existing skill by adding domain-specific reference files

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Bootstrap Mode

Use when: User wants to create a new data context skill for their warehouse.

Phase 1: Database Connection & Discovery

Step 1: Identify the database type

Ask: "What data warehouse are you using?"

Common options:

• BigQuery
• Snowflake
• PostgreSQL/Redshift
• Databricks

Use ~~data warehouse tools (query and schema) to connect. If unclear, check available MCP tools in the current session.

Step 2: Explore the schema

Use ~~data warehouse schema tools to:

1. List available datasets/schemas
2. Identify the most important tables (ask user: "Which 3-5 tables do analysts query most often?")
3. Pull schema details for those key tables

Sample exploration queries by dialect:

-- BigQuery: List datasets
SELECT schema_name FROM INFORMATION_SCHEMA.SCHEMATA

-- BigQuery: List tables in a dataset
SELECT table_name FROM `project.dataset.INFORMATION_SCHEMA.TABLES`

-- Snowflake: List schemas
SHOW SCHEMAS IN DATABASE my_database

-- Snowflake: List tables
SHOW TABLES IN SCHEMA my_schema

Phase 2: Core Questions (Ask These)

After schema discovery, ask these questions conversationally (not all at once):

Entity Disambiguation (Critical)

"When people here say 'user' or 'customer', what exactly do they mean? Are there different types?"

Listen for:

• Multiple entity types (user vs account vs organization)
• Relationships between them (1:1, 1:many, many:many)
• Which ID fields link them together