| 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:
- Database Connection: Identify warehouse type (BigQuery, Snowflake, PostgreSQL, Databricks) and explore schemas/tables
- Critical Questions: Capture tribal knowledge through targeted prompts
- Skill Generation: Produce organized documentation
- Packaging: Deliver complete skill structure with reference files
Iteration Mode
Enhance existing skills by adding domain-specific context:
- Identify gaps in existing documentation
- Ask targeted questions about specific data domains
- 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
