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

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 × 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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Iteration Mode

Use when: User has an existing skill but needs to add more context.

Step 1: Load Existing Skill

Ask user to upload their existing skill (zip or folder), or locate it if already in the session.

Read the current SKILL.md and reference files to understand what's already documented.

Step 2: Identify the Gap

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