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
You might also like
You can build production-grade vector search systems that retrieve semantically similar documents in sub-millisecond time using AgentDB's optimized HNSW indexing. The skill enables you to implement RAG pipelines, semantic search engines, and intelligent knowledge bases with configurable embedding dimensions, distance metrics (cosine, Euclidean, dot product), and similarity thresholds—all with built-in quantization and caching for massive performance gains.
by moonwell-fi
You'll systematically diagnose data quality problems by developing structured root cause analysis frameworks, calculating the true business impact, and creating reproducible validation tests. This skill walks you through hypothesis-driven investigation, data lineage analysis, and remediation planning — turning data issues into documented fixes and preventive measures.
by moonwell-fi
You can systematically evaluate and improve your RAG pipelines using Claude as a design partner. You'll analyze retrieval quality, identify bottlenecks in your embedding and chunking strategies, and receive actionable recommendations to reduce hallucinations and improve context relevance. By the end, you'll have a data-driven optimization plan tailored to your specific use case and performance metrics.
by moonwell-fi
You'll build comprehensive data quality test suites that validate transformations, detect anomalies, and document standards across your dbt and SQL pipelines. This skill generates production-ready test configurations, anomaly detection protocols, and validation rules that catch data issues before they impact analytics.
by moonwell-fi
You can convert complex datasets and business metrics into polished, executive-ready reports that stakeholders trust and act on. Claude generates data-driven narratives, executive summaries, actionable insights, and visualization recommendations tailored to your audience's priorities. Your reports will tell a cohesive story that connects metrics to business outcomes, eliminating confusion and accelerating decision-making.
by moonwell-fi
You can rapidly compile, organize, and format disparate health economic evidence—from clinical trials to cost-effectiveness analyses—into structured, regulatory-compliant dossiers. This skill maps your evidence to specific payer and HTA requirements, automatically generates evidence hierarchies, and produces submission-ready dossier outlines with formatting that meets regulatory standards for NICE, EUnetHTA, and other major bodies.
by moonwell-fi
You can structure comprehensive health technology assessments (HTAs) that organize clinical evidence, economic analyses, and regulatory considerations into evidence-based coverage recommendations. This skill helps you synthesize clinical trial data, health economic models, and real-world evidence into clear, defensible payer coverage determinations. You'll generate professional HTA reports that align with major frameworks like ICER, CADTH, and NICE standards.
by moonwell-fi
This skill automatically documents your entire analytics infrastructure by analyzing data sources, transformations, and outputs. You'll generate production-ready data dictionaries with field definitions, lineage maps showing data flow across systems, and transformation documentation that explains logic and dependencies. Save weeks of manual documentation work while keeping your analytics stack discoverable as it evolves.
by moonwell-fi