Yt Competitive Analysis

CommunityPopular

ericosiu

yt-competitive-analysis

Analyze YouTube channels for outlier videos and packaging patterns. Identifies what's working (2x+ average views) across any set of channels. Use when asked for YouTube competitive analysis, viral video patterns, or packaging/title inspiration.

Overview

Publisher	ericosiu
Repository	`ai-marketing-skills`
Skill name	`yt-competitive-analysis`
Stars	3.5K
Forks	685
Bundled files	2
License	MIT
Links	Skill source Repository Homepage

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
2 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by ericosiu on GitHub. Read the source before you install it.

Installation

Install the Yt Competitive Analysis AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
Enable the skill in any chat where you want it available.

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/ericosiu/ai-marketing-skills.git /tmp/ai-marketing-skills
mkdir -p .claude/skills
cp -r /tmp/ai-marketing-skills/yt-competitive-analysis .claude/skills/yt-competitive-analysis

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Yt Competitive Analysis in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Yt Competitive Analysis on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Yt Competitive Analysis is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

YouTube Competitive Analysis

Outlier detection and packaging pattern extraction for YouTube channels.

When to Use

User asks for YouTube competitive analysis
User wants to find viral video patterns
User wants packaging/title inspiration from specific creators
User wants to track competitor YouTube performance

Prerequisites

YouTube Data API v3 key set as $YOUTUBE_API_KEY

Usage

bash
# Analyze specific channels
python3 analyze.py "$YOUTUBE_API_KEY" --channels "@handle1,@handle2" --days 30

# Use predefined sets
python3 analyze.py "$YOUTUBE_API_KEY" --set ai
python3 analyze.py "$YOUTUBE_API_KEY" --set business
python3 analyze.py "$YOUTUBE_API_KEY" --set both

# Export formats
python3 analyze.py "$YOUTUBE_API_KEY" --set both --output json
python3 analyze.py "$YOUTUBE_API_KEY" --set both --output console

Predefined Channel Sets

AI Creators: Jeff Su, Alex Finn, Riley Brown, Dan Martell, Matt Wolfe, Nate Herk, Grace Leung, Matt Berman

Business Creators: Alex Hormozi, Gary Vaynerchuk, Patrick Bet-David, Codie Sanchez, Leila Hormozi, Iman Gadzhi, My First Million

Output Interpretation

Multiplier: Times above channel average (2.0x = double normal)
Outlier threshold: 2x average. Study anything above this.
Title patterns: Common words in outlier titles indicate proven formats
Cadence: Videos per week. Higher cadence creators may have lower per-video averages.

Channel Analytics Feedback Loop

Competitive analysis is only half the loop. When you have access to the channel's own analytics, compare candidate packaging against actual performance after publish.

Before recommending a package:

Pull channel baseline by topic, title pattern, thumbnail pattern, length, publish day/time, and format.
Check impressions, CTR, average view duration, retention curve, watch time, subscribers gained, comments, and traffic source.
Compare the proposed title/thumbnail/hook against similar historical videos and competitor outliers.

After publishing:

Log video ID, title, thumbnail concept, hook, topic bucket, retention devices, and publish date.
Pull analytics after the chosen readback window.
Compare baseline vs candidate.
Patch title formulas, thumbnail rules, hook patterns, retention beats, chapter structure, Shorts selection, or repurposing guidance only if the candidate wins.

Readback windows:

24-48 hours for CTR and early retention
7 days for average view duration and watch time
28 days for topic durability and subscriber gain

Do not call packaging validated until analytics are checked. Otherwise it is a screenplay wearing a lab coat.

Packaging Skeletons (Proven Formats)

Long-form:

"X, Clearly Explained"
"X hours of Y in Z minutes"
"The Laziest Way to X"
"Give me X minutes and I'll Y"
"X INSANE Use Cases for Y"

Shorts:

"2024 vs 2025 X" (year comparison)
"Bad Good Great X" (tier ranking)
"Stop doing X, do Y instead" (contrarian)

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

analyze.py10 KB
requirements.txt1 KB

Frequently asked questions

What does the Yt Competitive Analysis AI skill do?

Why use Yt Competitive Analysis on TypingMind?

Because you install it once and use it with any model. Yt Competitive Analysis is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Yt Competitive Analysis in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ericosiu/ai-marketing-skills/tree/main/yt-competitive-analysis. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Yt Competitive Analysis?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Yt Competitive Analysis?

As many as you like. As long as a model supports skills, you can use Yt Competitive Analysis with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Yt Competitive Analysis AI skill free?

Yes. It is published on GitHub by ericosiu under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

YouTube Competitive Analysis

When to Use

Prerequisites

Usage

Predefined Channel Sets

Output Interpretation

Channel Analytics Feedback Loop

Packaging Skeletons (Proven Formats)

Bundled files

Frequently asked questions

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