Act as a data analysis expert. You are skilled at examining YouTube channels, website databases, and user profiles to gather insights based on specific parameters provided by the user.

Jan 15, 2026

Data

PromptBeginner5 minmarkdownQuality: 24

1. Accept parameters such as ${platform:YouTube/Database/Profile}

${metrics:engagement/views/likes}

Jan 15, 2026

Data

PromptBeginner5 minmarkdownQuality: 20

- Use visualizations where applicable (e.g.

tables or charts) to present data.

Jan 15, 2026

General

PromptBeginner5 minmarkdownQuality: 20

- Review user profiles

extracting relevant information based on the specified criteria.

Jan 15, 2026

Find agent skills by outcome

- **Expert**: Multiple examples

Based on the recent and forecasted precipitation

- Include **emerging tools/standards** (e.g.

# - Claude (by Anthropic): Strong in highly structured

# - DeepSeek R1: Affordable and powerful for logical reasoning and math-based predictions (e.g.

First

# Audience: Homeowners in snowy regions

Then

# - GPT-4o (by OpenAI): Highly versatile with strong creative problem-solving

# - Perplexity AI: Combines conversational AI with instant web searches for up-to-date weather data from sources like NOAA; great for concise

# - Copilot (by Microsoft): Integrates Bing weather data for reliable

# - Gemini 2.5 (by Google): Outstanding for real-time weather integration via Google Search and Maps

# Goal: To provide data-driven advice on the optimal timing and methods for clearing snow from a driveway

# Modified Date: December 27

- Forecasted snowfall

**Risks**: What could break

**How**: Key implementation decisions (patterns used

When to clear the snow (generic)

- Evaluate the structure and data of website databases

**Smells**: Tech debt

Detailed Analysis of YouTube Channels, Databases, and Profiles

1. Accept parameters such as ${platform:YouTube/Database/Profile}

- Use visualizations where applicable (e.g.

- Review user profiles

- Expert: Multiple examples

- Include emerging tools/standards (e.g.

Risks: What could break

How: Key implementation decisions (patterns used

Smells: Tech debt