Analytics Documentation Generator
Auto-generate data dictionaries, lineage maps, and transformation docs from your analytics stack
What You Can Do
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.
Features
automatically catalog all tables, fields, data types, and business definitions from your warehouse
trace data flow from source systems through transformations to final analytics outputs
auto-generate docs explaining ETL logic, filters, aggregations, and dependencies
capture field descriptions, constraints, quality rules, and ownership
identify downstream dependencies when source schemas or transformations change
generate version-control-friendly documentation that lives alongside your code
pre-built connectors for Snowflake, BigQuery, Redshift, dbt, and Fivetran
automatically organize and cross-reference business term definitions across your stack
Example Output
Example Data Dictionary Entry
Table: customers_fact
Owner: Analytics Team
Updated: 2026-07-30
| Column | Type | Description | Business Definition |
|---|---|---|---|
| customer_id | INT | Unique customer identifier (PK) | Stable identifier for each customer |
| VARCHAR | Customer email address | Primary communication channel | |
| lifetime_value | DECIMAL(12,2) | Total customer spend (USD) | Sum of all successful transactions |
Example Lineage Map
salesforce_accounts → Fivetran → raw_salesforce → dbt models →
dim_accounts → Revenue Dashboard
↓
fact_opportunities → Sales Pipeline Report
Example Transformation Doc
Transformation: stg_orders__deduplicated
- Source:
raw_orderstable - Logic: Deduplicates orders by order_id, keeping latest updated_at timestamp
- Dependencies: Runs after
raw_ordersis refreshed, upstream offct_orders - Last updated: 2026-07-15
- Owner: analytics@company.com
What's Included
- SKILL.md: Complete analytics documentation generation workflow with decision trees
- Data Dictionary Template: Structured markdown template for table and column documentation
- Lineage Map Template: Mermaid-formatted data flow diagrams
- Transformation Documentation Template: ETL logic and dependency tracking
- Column Metadata Extraction Checklist: Step-by-step field cataloging guide
- Platform Integration Guide: Connection setup for Snowflake, BigQuery, Redshift, dbt Cloud, and Fivetran
- Schema Change Impact Analysis Workflow: Identify affected downstream transforms
- Business Glossary Builder: Automated term extraction and cross-reference guide
- Documentation Audit Checklist: Quality assurance for generated docs
Who It's For
- Data Engineers building and maintaining ETL pipelines across multiple source systems
- Analytics Engineers documenting dbt transformations and maintaining semantic layers
- Data Analysts onboarding to new analytics environments or inheriting legacy systems
- Data Governance Managers building metadata catalogs and compliance frameworks
- Business Intelligence Developers creating self-service analytics platforms and BI portals
Best For
- Documenting legacy analytics systems with missing or outdated metadata
- Creating data dictionaries for compliance (HIPAA, GDPR, SOX audits)
- Onboarding new team members to your analytics stack quickly
- Building data governance and cataloging frameworks from scratch
- Tracking schema changes and impact analysis across your entire data platform
You might also like
This skill helps you systematically diagnose failures in distributed ML training and serving infrastructure. You provide system logs, metrics, and error traces, and Claude performs structured root-cause analysis to identify the underlying issue—whether it's resource exhaustion, distributed system deadlock, data pipeline corruption, or model serving misconfiguration. You get a detailed diagnosis with remediation steps ranked by likelihood and implementation effort.
by moonwell-fi
You can systematically diagnose why your RAG system isn't returning relevant results and identify specific misalignments between queries and context. This skill walks you through analyzing retrieval quality, scoring context relevance, and optimizing prompt-retrieval alignment to improve your RAG accuracy. You'll get concrete, actionable recommendations for fixing bottlenecks in your pipeline.
by moonwell-fi
You'll handle difficult guest conversations with structured communication templates that de-escalate tension while protecting your property. Generate professional damage assessments, complaint responses, and resolution protocols that document interactions clearly and mitigate liability. Create personalized communication strategies tailored to each guest situation, from minor maintenance requests to serious damage claims.
by moonwell-fi
You can create well-structured semantic layer specifications that define business metrics, dimensions, and relationships from your raw data schemas. This skill validates your semantic layer design for consistency, identifies naming issues, detects redundancy, and generates comprehensive documentation and data dictionaries that keep your entire team aligned.
by moonwell-fi
Systematically evaluate your Claude prompts using quantitative scoring frameworks to identify weaknesses in clarity, task alignment, and output quality. You'll receive detailed optimization recommendations with A/B testing guidance to improve prompt effectiveness and consistency across different use cases and input variations.
by moonwell-fi
You can prepare your domain-specific datasets, train fine-tuned Claude models optimized for your use case, benchmark performance against base models, and deploy production-ready custom models. This skill automates the entire fine-tuning pipeline from raw data to live inference, complete with cost-benefit analysis and version control.
by moonwell-fi
You get structured guidance for designing controlled experiments, selecting appropriate metrics, and analyzing results with statistical rigor. Claude helps you identify confounding variables, calculate required sample sizes, interpret statistical significance, and document methodology for reproducible benchmarking that stands up to scrutiny.
by moonwell-fi
This skill transforms high-level feature requests into actionable implementation plans by conducting deep requirement interviews, analyzing existing codebases (or planning greenfield projects), and producing comprehensive handoff documents. It distinguishes between brownfield development (with existing code to integrate) and greenfield projects (starting from scratch), ensuring your technical strategy accounts for your actual constraints and patterns.
by moonwell-fi