data-context-extractor-phase-1-database-connection-discovery

name	data-context-extractor-phase-1-database-connection-discovery
description	Sub-skill of data-context-extractor: Phase 1: Database Connection & Discovery (+3).
version	1.0.0
category	data-analytics
type	reference
scripts_exempt	true

Phase 1: Database Connection & Discovery (+3)

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

Primary Identifiers

"What's the main identifier for a [customer/user/account]? Are there multiple IDs for the same entity?"

Listen for:

• Primary keys vs business keys
• UUID vs integer IDs
• Legacy ID systems

Key Metrics

"What are the 2-3 metrics people ask about most? How is each one calculated?"

Listen for:

• Exact formulas (ARR = monthly_revenue x 12)
• Which tables/columns feed each metric
• Time period conventions (trailing 7 days, calendar month, etc.)

Data Hygiene

"What should ALWAYS be filtered out of queries? (test data, fraud, internal users, etc.)"

Listen for:

• Standard WHERE clauses to always include
• Flag columns that indicate exclusions (is_test, is_internal, is_fraud)
• Specific values to exclude (status = 'deleted')

Common Gotchas

"What mistakes do new analysts typically make with this data?"

Listen for:

• Confusing column names
• Timezone issues
• NULL handling quirks
• Historical vs current state tables

Phase 3: Generate the Skill

Create a skill with this structure:

[company]-data-analyst/
├── SKILL.md
└── references/
    ├── entities.md          # Entity definitions and relationships
    ├── metrics.md           # KPI calculations
    ├── tables/              # One file per domain
    │   ├── [domain1].md
    │   └── [domain2].md
    └── dashboards.json      # Optional: existing dashboards catalog

SKILL.md Template: See references/skill-template.md

SQL Dialect Section: See references/sql-dialects.md and include the appropriate dialect notes.

Reference File Template: See references/domain-template.md

Phase 4: Package and Deliver

1. Create all files in the skill directory
2. Package as a zip file
3. Present to user with summary of what was captured

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