Analytics Documentation Generator
Generate production-grade analytics documentation in minutes
What You Can Do
You can create comprehensive documentation for your data platforms, metrics, and analytics infrastructure that stakeholders actually understand. The skill generates clear data dictionaries, metric definitions, pipeline diagrams, and runbooks that bridge the gap between technical teams and business users. Your documentation stays consistent with your actual infrastructure while being instantly accessible to analysts, managers, and engineers.
Features
Automatically catalog all tables, columns, data types, freshness guarantees, and ownership
Define business metrics with calculation logic, expected values, SLAs, and common use cases
Generate architecture diagrams, data lineage, and ETL/ELT workflow explanations
Write plain-English summaries that translate technical concepts for business audiences
Auto-generate reusable SQL and dbt snippets with realistic examples for each table
Create troubleshooting guides with diagnostic queries and incident recovery procedures
Document data sources, transformations, downstream consumers, and dependencies
Establish PII classifications, access policies, and data quality requirements
Example Output
Metric Definition Card
Metric: Customer Lifetime Value (CLV)
Business Definition: Total expected revenue from a customer across their entire relationship
Owner: Revenue Analytics team (analytics@company.com)
SLA: Updated daily, 99.5% accuracy
Calculation: SUM(order_total) WHERE customer_id = X AND status = 'completed'
Common Uses: Segmentation, churn prediction, acquisition ROI
Limitation: Excludes refunds and chargebacks
Data Dictionary Excerpt
Table: events_raw
Purpose: Raw clickstream events from web and mobile applications
Freshness: Updated every 5 minutes
Ownership: Data Platform team
Columns:
- event_id (UUID): Unique identifier for each event
- user_id (VARCHAR): Customer identifier, NULL for anonymous users
- event_timestamp (TIMESTAMP): UTC time event occurred
- event_type (VARCHAR): category, purchase, login, etc.
Runbook Snippet
### Pipeline Failed: customer_daily_aggregation
**Error:** "Connection timeout after 900s"
**Root Cause:** Source database maintenance window not communicated
**Recovery:**
1. Check source system status: SELECT * FROM status_dashboard
2. If available, restart pipeline via dbt run -s customer_daily_aggregation
3. Verify completeness: SELECT COUNT(*) FROM customer_daily
What's Included
- SKILL.md: Full analytics documentation generator prompt with examples
- Templates: Data dictionary, metric card, runbook, and lineage diagram templates
- Checklists: Schema change reviews, quarterly documentation audits, access policy updates
- Workflows: Collaboration workflows with data teams, documentation PR review process, escalation procedures
- Examples: Sample documentation for real-world tables, metrics, and pipelines
- Guidelines: Best practices for metric definitions, naming conventions, and governance structures
Who It's For
- Data Engineers — Building and maintaining data platforms and pipelines
- Analytics Engineers — Documenting dbt models, metrics, and transformation logic
- Data Analysts — Explaining datasets and metric calculations to business stakeholders
- Data Governance Teams — Establishing PII policies, access controls, and data quality standards
- Platform Product Managers — Onboarding teams and documenting infrastructure changes
Best For
- Documenting new data sources, tables, and major schema changes
- Creating metric definitions with business context and calculation logic
- Building runbooks for common data pipeline failures and troubleshooting
- Establishing data governance frameworks and access policies
- Onboarding new team members to your analytics infrastructure
You might also like
This skill guides you through rigorous experiment design from hypothesis to analysis plan. You'll compute the statistical power needed for your tests, identify confounding variables to control for, design optimal randomization strategies, and generate pre-registered analysis plans that prevent p-hacking and publication bias. Whether you're running a product A/B test or investigating causal effects in observational data, you get structured, validated designs that account for real-world constraints.
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
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
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 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 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 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
You can confidently navigate complex statistical analyses by working through structured workflows that match your research question and data type to the right test. The skill guides you through assumption validation, helps you interpret results accurately without common pitfalls, and walks you through regression diagnostics with full transparency about what your findings actually mean.
by moonwell-fi