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ahmedkhaledmohamed
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data-analyst

Writes and executes SQL queries to analyze data and explain findings for product managers.

5|2|Updated Jan 12, 2026
Search Tags:#metrics#insights#data analysis#sql#bigquery#product analytics
One-click install
npx skills add https://github.com/ahmedkhaledmohamed/PM-AI-Partner-Framework --skill data-analyst-ahmedkhaledmohamed
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-analyst
Source: https://github.com/ahmedkhaledmohamed/PM-AI-Partner-Framework/tree/main/framework/templates/claude-code-skills/data-analyst
Command: npx skills add https://github.com/ahmedkhaledmohamed/PM-AI-Partner-Framework --skill data-analyst-ahmedkhaledmohamed

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps Product Managers explore data, write queries, and extract actionable insights to inform product decisions and understand user behavior.

Core Features & Use Cases

  • Data Querying: Write and execute SQL queries against various data sources (e.g., BigQuery).
  • Metric Analysis: Analyze trends, segment data, and identify patterns in quantitative information.
  • Insight Generation: Translate data findings into clear interpretations and recommendations.
  • Use Case: A PM needs to understand why a new feature's adoption rate is lower than expected. The Data Analyst Skill can query user engagement data, segment by platform and user cohort, and identify specific drop-off points or user segments with low engagement.

Quick Start

Analyze our push notification delivery rate trends over the last 30 days, segmented by platform.

Frequently Asked Questions about data-analyst

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

FAQPage Schema
How do I write SQL queries to analyze product metrics and user behavior?▼

To analyze product metrics, you can write and execute SQL queries against data sources like BigQuery to explore user behavior. This process segments data by cohorts and platforms to identify specific patterns and trends.

What's the best way to investigate why a new feature's adoption rate is lower than expected?▼

Investigating low feature adoption requires querying user engagement data and segmenting it by platform and user cohort. This multi-dimensional data breakdown identifies specific drop-off points and user segments exhibiting low engagement.

Can I use BigQuery for multi-dimensional data breakdowns in product analytics?▼

Yes, you can use BigQuery for product analytics by writing SQL queries to perform multi-dimensional data breakdowns. This allows you to segment quantitative information, analyze trends, and identify patterns across various user dimensions.

How do I translate raw SQL data findings into actionable product insights?▼

Translating raw SQL data findings into actionable product insights involves explaining query results and identifying data limitations. This helps Product Managers understand user behavior and make informed product decisions.

What are the limitations of using SQL for feature impact analysis?▼

Limitations of using SQL for feature impact analysis include identifying constraints within the queried data itself. Recognizing these limitations ensures Product Managers avoid misinterpreting findings when evaluating quantitative engagement trends.

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