Analytics Engineer Documentation Assistant
Generate production-grade data documentation from SQL and schemas
What You Can Do
This skill transforms SQL queries, table definitions, and database schemas into comprehensive data documentation instantly. It produces data models, column dictionaries, metrics definitions, and data lineage—typically done manually over hours. You get governance-ready documentation that aligns your data team and informs stakeholders, all in minutes.
Features
descriptions, columns, tests, and YAMLs
Example Output
Example 1: Generated Data Dictionary
| Column | Type | Description | Business Logic |
|---|---|---|---|
| customer_id | INT | Unique customer identifier | Primary key, never null |
| VARCHAR(255) | Customer email address | Deduplicated, normalized to lowercase | |
| lifetime_value | DECIMAL(10,2) | Total revenue from customer | Sum of all completed orders, refreshed daily |
| segment | VARCHAR(50) | Customer cohort | Calculated from RFM analysis, updated weekly |
| created_at | TIMESTAMP | Account creation date | UTC timezone, immutable |
Example 2: Generated dbt Model YAML
version: 2
models:
- name: customers_dim
description: 'Core customer dimension with RFM segmentation'
owner: 'analytics-team'
columns:
- name: customer_id
description: 'Unique customer identifier'
tests:
- unique
- not_null
- name: lifetime_value
description: 'Total customer lifetime value, refreshed nightly'
Example 3: Data Lineage Map
[raw.postgres.orders] → [stg_orders] → [fact_orders] → [dashboard.revenue]
[raw.postgres.customers] → [stg_customers] → [customers_dim] → [dashboard.customer_360]
What's Included
- SKILL.md: Complete documentation assistant with SQL analysis and generation prompts
- Data Dictionary Template: Column metadata, definitions, and business logic format
- dbt YAML Generator Template: Converts SQL into production-ready dbt model YAMLs
- Metrics Definition Template: Standardized KPI and calculation documentation
- Lineage Mapping Template: Table dependencies and data flow diagrams
- Schema Analysis Checklist: Questions to extract all critical documentation elements
- Quality Rules Template: Data validation and SLA documentation format
Who It's For
- Analytics Engineers — Document dbt projects and maintain data governance
- Data Analysts — Create self-service data dictionaries for business teams
- Data Platform Teams — Build comprehensive data catalogs and lineage maps
- Business Analysts — Understand technical data assets in business language
- Data Stewards — Manage data quality rules, ownership, and SLAs
Best For
- Documenting dbt projects — Auto-generate model YAMLs from SQL queries
- Data lineage analysis — Map upstream sources and downstream impacts
- Metrics and KPI definitions — Capture calculation logic and ownership
- Column-level data dictionaries — Define every field's business meaning
- Schema change documentation — Generate migration notes and impact summaries
- Data governance — Assign ownership, refresh schedules, and quality rules
- Onboarding — Give new team members instant schema and lineage reference
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