data-context-extractor

name	data-context-extractor
description	Capture and codify company-specific data knowledge — entity definitions, metric formulas, table schemas, and tribal knowledge. Use when onboarding to a new data environment, documenting institutional data knowledge, or building a data dictionary.

Data Context Extractor

A meta-skill for capturing and codifying company-specific data knowledge. Helps organizations document tribal knowledge about their data — entity definitions, metric formulas, table schemas, and common analyst pitfalls.

Two Operating Modes

Bootstrap Mode

Initialize new data analysis skills from scratch:

1. Database Connection: Identify warehouse type (BigQuery, Snowflake, PostgreSQL, Databricks) and explore schemas/tables
2. Critical Questions: Capture tribal knowledge through targeted prompts
3. Skill Generation: Produce organized documentation
4. Packaging: Deliver complete skill structure with reference files

Iteration Mode

Enhance existing skills by adding domain-specific context:

1. Identify gaps in existing documentation
2. Ask targeted questions about specific data domains
3. Update reference materials accordingly

Critical Questions to Capture

Entity Definitions

• What are the core business entities? (users, accounts, orders, events)
• How is each entity uniquely identified?
• What are the lifecycle states for each entity?
• When is an entity created, updated, or archived?

Metric Definitions

• What are the key business metrics?
• How is each metric calculated exactly? (formula, filters, time windows)
• What are the known edge cases or gotchas in each metric?
• Who is the source of truth for metric definitions?

Data Quality

• What are known data quality issues?
• Which tables are trustworthy vs. "use with caution"?
• What time delays exist between events and data availability?
• Are there known gaps or missing data periods?

Common Analyst Pitfalls

• What mistakes do new analysts commonly make?
• What join traps exist (many-to-many, missing records)?
• What timezone issues should analysts be aware of?
• What definitional ambiguities cause confusion?

Business Context

• What does a typical customer journey look like in the data?
• How do business processes map to database tables?
• What external events affect the data (launches, outages, migrations)?
• What seasonal patterns should analysts know about?

Output Structure

skill/
  SKILL.md           # Main skill file with overview and instructions
  references/
    entities.md      # Entity definitions and relationships
    metrics.md       # Metric formulas and definitions
    tables.md        # Table schemas and descriptions
    gotchas.md       # Common pitfalls and data quality notes
    glossary.md      # Business terminology

Tips

• Run Bootstrap Mode when first setting up a data team or onboarding to a new data environment
• Run Iteration Mode after discovering a new gotcha, adding a new data source, or when new team members identify documentation gaps
• Keep the output as reference material alongside your analysis skills