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data-expert

Analyze Studio Chat API data to generate JSON or CSV analytics reports.

Updated Feb 20, 2026
Search Tags:#reports#analytics#api#python#data#analyze#studio-chat
One-click install
npx skills add https://github.com/studiochat/skills --skill data-expert
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-expert
Source: https://github.com/studiochat/skills/tree/main/skills/data-expert
Command: npx skills add https://github.com/studiochat/skills --skill data-expert

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

Analyze Studio Chat data to produce actionable analytics and reports.

Core Features & Use Cases

  • Fetch and process analytics from the Studio Chat API using Python.
  • Evaluate deflection rates, sentiment distributions, resource quality, latency, and trends; generate markdown or CSV reports.
  • Use case: summarize performance across a project or across playbooks for performance reviews.

Quick Start

Fetch analytics for your Studio Chat project and generate a markdown report summarizing key metrics.

Frequently Asked Questions about data-expert

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

FAQPage Schema
How do I analyze Studio Chat conversation data for analytics and reporting?▼

You can analyze Studio Chat conversations by fetching data via the Studio Chat API using Python workflows, evaluating metrics like deflection and sentiment, then generating markdown or CSV reports.

What metrics can I extract from Studio Chat API data for performance reviews?▼

Studio Chat API analytics allow you to evaluate deflection rates, sentiment distributions, resource quality, latency, and toolkit usage trends across single projects or multiple playbooks.

Can I use Python to fetch and export Studio Chat analytics as CSV or JSON?▼

Yes, Python workflows using the fetch.py and export_conversations.py scripts extract Studio Chat API data and output structured results in JSON or CSV formats for reporting.

Do I need API tokens from environment variables to analyze Studio Chat data?▼

Yes, the Python workflows rely on Studio Chat API tokens configured in your environment variables to authenticate and fetch conversation data for analytics processing.

How do I generate markdown reports from Studio Chat playbook data?▼

You can generate markdown reports by running Python scripts to process Studio Chat API data across playbooks, summarizing key metrics like latency, sentiment, and deflection into a readable format.

What is the best way to assess deflection rates across multiple Studio Chat projects?▼

The best way to assess deflection rates is using Python workflows to fetch and process Studio Chat API data across multiple playbooks, outputting structured analytics in JSON or CSV format.

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