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home / skills / jeremylongshore / claude-code-plugins-plus-skills / window-function-generator

window-function-generator skill

/skills/12-data-analytics/window-function-generator

This skill helps you automate and validate window function generator tasks with production-ready code and best-practice guidance.

npx playbooks add skill jeremylongshore/claude-code-plugins-plus-skills --skill window-function-generator

Review the files below or copy the command above to add this skill to your agents.

Files (1)
SKILL.md
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---
name: "window-function-generator"
description: |
  Generate window function generator operations. Auto-activating skill for Data Analytics.
  Triggers on: window function generator, window function generator
  Part of the Data Analytics skill category. Use when working with window function generator functionality. Trigger with phrases like "window function generator", "window generator", "window".
allowed-tools: "Read, Write, Edit, Bash(cmd:*), Grep"
version: 1.0.0
license: MIT
author: "Jeremy Longshore <[email protected]>"
---

# Window Function Generator

## Overview

This skill provides automated assistance for window function generator tasks within the Data Analytics domain.

## When to Use

This skill activates automatically when you:
- Mention "window function generator" in your request
- Ask about window function generator patterns or best practices
- Need help with data analytics skills covering sql queries, data visualization, statistical analysis, and business intelligence.

## Instructions

1. Provides step-by-step guidance for window function generator
2. Follows industry best practices and patterns
3. Generates production-ready code and configurations
4. Validates outputs against common standards

## Examples

**Example: Basic Usage**
Request: "Help me with window function generator"
Result: Provides step-by-step guidance and generates appropriate configurations


## Prerequisites

- Relevant development environment configured
- Access to necessary tools and services
- Basic understanding of data analytics concepts


## Output

- Generated configurations and code
- Best practice recommendations
- Validation results


## Error Handling

| Error | Cause | Solution |
|-------|-------|----------|
| Configuration invalid | Missing required fields | Check documentation for required parameters |
| Tool not found | Dependency not installed | Install required tools per prerequisites |
| Permission denied | Insufficient access | Verify credentials and permissions |


## Resources

- Official documentation for related tools
- Best practices guides
- Community examples and tutorials

## Related Skills

Part of the **Data Analytics** skill category.
Tags: sql, analytics, visualization, statistics, bi

Overview

This skill automates generation of window function patterns for data analytics workflows. It produces production-ready SQL snippets, configuration guidance, and validation tips to speed up analytics tasks. Use it to standardize ranking, aggregation, and time-series window logic across projects.

How this skill works

The skill inspects your request for window-function intent and generates SQL or code templates for common window operations (ROW_NUMBER, RANK, SUM OVER, moving averages, lead/lag). It applies industry best practices, adds partitioning and ordering recommendations, and runs basic validations against common error patterns. Outputs include ready-to-use query fragments, configuration notes, and suggestions for integrating results into dashboards or ETL pipelines.

When to use it

  • You need canonical SQL for ranking, cumulative sums, or moving averages
  • You want to convert business rules into partition/order window logic
  • Validating or standardizing window queries across teams
  • Generating templates for ETL, BI dashboards, or analytics notebooks
  • Learning or teaching window function patterns and pitfalls

Best practices

  • Always include PARTITION BY and ORDER BY tailored to the metric and time granularity
  • Use ROWS or RANGE framing explicitly for deterministic moving-window calculations
  • Prefer explicit NULL handling and default values for lead/lag to avoid downstream surprises
  • Document assumptions about data ordering and time zones when generating time-series windows
  • Test window queries on sample partitions to confirm performance and correctness before wide deployment

Example use cases

  • Create a query that ranks users by monthly spend with ties handled consistently
  • Generate a 7-day rolling average for a web metric with proper framing and performance hints
  • Produce lead/lag expressions to compute retention or churn indicators in ETL jobs
  • Convert a business KPI definition into partitioned window SQL ready for a BI tool
  • Suggest Terraform or pipeline config notes for deploying window-based aggregations in production

FAQ

What inputs do you need to generate a window query?

Provide the table schema, the metric or column to compute, the partition and sort keys, and the desired frame (e.g., 7-day, cumulative).

Can the skill validate performance implications?

It flags common performance issues (missing indexes, large partitions, improper framing) and recommends optimizations, but full performance testing requires running queries on your environment.