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ProfFroggo
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data-analysis

Automate end-to-end data-analysis workflows from user inquiry to final report.

1|Updated Mar 8, 2026
Search Tags:#workflow#reporting#sql#data-analysis#data-quality#query-planning
One-click install
npx skills add https://github.com/ProfFroggo/froggo-mission-control --skill data-analysis-proffroggo
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-analysis
Source: https://github.com/ProfFroggo/froggo-mission-control/tree/main/.claude/skills/data-analysis
Command: npx skills add https://github.com/ProfFroggo/froggo-mission-control --skill data-analysis-proffroggo

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Data analysts and teams spend significant time scoping, planning, and documenting analyses. This Skill provides a repeatable, auditable framework to move from a user inquiry to a delivered analytical product.

Core Features & Use Cases

  • Structured analysis workflow from question to insights and recommendations.
  • Step-by-step guidance: brief write-up, data-source identification, data quality checks, query planning, and report formatting.
  • Use cases include performance analytics, product metrics deep-dives, and periodic reporting across teams.

Quick Start

Define the question, identify data sources, perform quality checks, run analyses, and deliver a formatted report.

Frequently Asked Questions about data-analysis

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I structure an end-to-end data analysis workflow from a business question to a final report?▼

An end-to-end data analysis workflow structures tasks from brief definition and data source planning to quality checks, query execution, and formatted reporting, ensuring repeatable and auditable analytical outcomes.

What is the best way to plan SQL queries and data quality checks for ad-hoc performance analytics?▼

The best way to plan SQL queries for performance analytics is enforcing sequential steps: define the brief, identify sources, execute data quality checks, formulate queries, and document results for auditability.

Can I use this approach to automate periodic reporting and product metrics deep-dives across diverse data sources?▼

Yes, you can automate periodic reporting and product metrics deep-dives across diverse data sources by applying a structured workflow that enforces brief definition, quality checks, query planning, and final report formatting.

How do I create a repeatable and auditable framework for ad-hoc data analysis requests?▼

You create a repeatable, auditable framework for ad-hoc data analysis by enforcing step-by-step documentation covering brief write-ups, data source identification, quality validation, query planning, and final report delivery.

What are the limitations of using a structured workflow for data analysis instead of ad-hoc querying?▼

A structured data analysis workflow limits rapid, exploratory ad-hoc querying by enforcing mandatory steps like brief definition, data source planning, and quality checks before executing queries and generating reports.

Does data analysis workflow automation require predefined SQL query templates to generate dashboards?▼

Data analysis workflow automation does not require predefined SQL query templates; it uses query planning steps to dynamically generate queries based on the defined brief, identified data sources, and quality checks.

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