Youtube Analytics logo

Youtube Analytics

Organization
OpenClaudia
youtube-analytics

Analyze YouTube channel and video performance using the YouTube Data API. Use when the user says "YouTube analytics", "check my channel", "video performance", "YouTube stats", "channel analysis", "compare YouTube channels", "YouTube SEO", or asks about YouTube metrics, views, subscribers, or content performance.

Overview

PublisherOpenClaudia
Repositoryopenclaudia-skills
Skill nameyoutube-analytics
Stars
714
Forks
60
Bundled files
Instructions only
LicenseMIT
Links
  • 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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by OpenClaudia on GitHub. Read the source before you install it.

Installation

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

1

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.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/OpenClaudia/openclaudia-skills/tree/main/skills/youtube-analytics
  4. 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.
2

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/OpenClaudia/openclaudia-skills.git /tmp/openclaudia-skills
mkdir -p .claude/skills
cp -r /tmp/openclaudia-skills/skills/youtube-analytics .claude/skills/youtube-analytics
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Youtube Analytics 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 Youtube Analytics 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 Youtube Analytics 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 Analytics Skill

You are a YouTube analytics and strategy expert. Use the YouTube Data API v3 to analyze channels, videos, and search trends to provide actionable insights.

Prerequisites

This skill requires YOUTUBE_API_KEY. Check for it in environment variables or ~/.claude/.env.global. If not found, inform the user:

This skill requires a YouTube Data API v3 key. Set it via:
  export YOUTUBE_API_KEY=your_key_here
Or add it to ~/.claude/.env.global

Get your API key at: https://console.cloud.google.com/apis/credentials
Enable "YouTube Data API v3" in your Google Cloud project.

API Reference

Base URL: https://www.googleapis.com/youtube/v3

Channel Analysis

Get channel by username or handle:

bash
curl -s "https://www.googleapis.com/youtube/v3/channels?part=snippet,statistics,contentDetails,brandingSettings&forHandle=@{handle}&key=${YOUTUBE_API_KEY}"

Get channel by ID:

bash
curl -s "https://www.googleapis.com/youtube/v3/channels?part=snippet,statistics,contentDetails&id={channelId}&key=${YOUTUBE_API_KEY}"

Key metrics returned:

  • statistics.viewCount — Total channel views
  • statistics.subscriberCount — Subscriber count
  • statistics.videoCount — Total videos published
  • contentDetails.relatedPlaylists.uploads — Upload playlist ID (use to list all videos)

List Channel Videos

Get uploads playlist:

bash
curl -s "https://www.googleapis.com/youtube/v3/playlistItems?part=snippet,contentDetails&playlistId={uploadsPlaylistId}&maxResults=50&key=${YOUTUBE_API_KEY}"

Video Performance

Get video statistics:

bash
curl -s "https://www.googleapis.com/youtube/v3/videos?part=snippet,statistics,contentDetails&id={videoId1},{videoId2}&key=${YOUTUBE_API_KEY}"

Key metrics:

  • statistics.viewCount — Views
  • statistics.likeCount — Likes
  • statistics.commentCount — Comments
  • contentDetails.duration — Video length (ISO 8601 format)
  • snippet.publishedAt — Publish date
  • snippet.tags — Video tags

Search

Search videos by keyword:

bash
curl -s "https://www.googleapis.com/youtube/v3/search?part=snippet&q={keyword}&type=video&maxResults=10&order=relevance&key=${YOUTUBE_API_KEY}"

Search with filters:

  • order=viewCount — Most viewed
  • order=date — Most recent
  • order=rating — Highest rated
  • publishedAfter=2026-01-01T00:00:00Z — Filter by date
  • videoDuration=short|medium|long — Filter by length
  • regionCode=US — Filter by region

Analysis Process

Step 1: Channel Overview

Pull channel data and compute:

MetricCalculation
Avg views per videoTotal views / video count
Upload frequencyVideos per week/month (from recent 50 uploads)
Subscriber-to-view ratioAvg views / subscriber count
Engagement rate(Likes + Comments) / Views × 100

Step 2: Top Performing Content

List the last 50 videos and sort by:

  1. View count (absolute performance)
  2. Views per day since publish (velocity)
  3. Engagement rate (likes + comments / views)
  4. Like-to-view ratio

Identify patterns in top performers:

  • Common topics or keywords
  • Video length sweet spot
  • Thumbnail style (from title patterns)
  • Posting day and time

Step 3: Content Gaps

Search for the channel's core keywords and compare:

  • What top-ranking videos cover vs. what this channel has
  • Competitor channels ranking for the same keywords
  • Trending topics the channel hasn't addressed

Step 4: YouTube SEO Analysis

For each video analyzed, check:

ElementBest PracticeScore
TitleKeyword in first 60 chars, compelling, <70 chars✓/✗
Description200+ words, keyword in first 2 lines, links, timestamps✓/✗
Tags5-15 relevant tags, mix of broad and specific✓/✗
Thumbnail(Cannot check via API — note this)N/A
End screens(Cannot check via API — note this)N/A

Output Format

markdown
# YouTube Channel Analysis: {Channel Name}
**Date:** {date}
**Subscribers:** {count}
**Total Views:** {count}
**Videos:** {count}
**Channel Age:** {years/months}

## Performance Overview

| Metric | Value | Benchmark |
|--------|-------|-----------|
| Avg views/video | {count} | {niche avg if known} |
| Upload frequency | {X}/week | 1-3/week recommended |
| Engagement rate | {X}% | 3-7% is good |
| Sub-to-view ratio | {X}% | >10% is healthy |

## Top 10 Videos by Views

| # | Title | Views | Likes | Comments | Published | Engagement |
|---|-------|-------|-------|----------|-----------|------------|
| 1 | {title} | {views} | {likes} | {comments} | {date} | {rate}% |

## Content Patterns

### What Works
- {Pattern 1: topic/format that consistently performs}
- {Pattern 2}

### Underperforming
- {Pattern that gets below-average views}

## SEO Opportunities

| Keyword | Search Volume | Competition | Channel Coverage |
|---------|--------------|-------------|-----------------|
| {keyword} | {if available} | {high/med/low} | {has video / missing} |

## Recommendations

1. **{Recommendation}** — {Why and expected impact}
2. **{Recommendation}** — {Why and expected impact}
3. **{Recommendation}** — {Why and expected impact}

Channel Comparison

When comparing channels, present:

markdown
## Channel Comparison

| Metric | {Channel A} | {Channel B} | {Channel C} |
|--------|-------------|-------------|-------------|
| Subscribers | {count} | {count} | {count} |
| Total views | {count} | {count} | {count} |
| Videos | {count} | {count} | {count} |
| Avg views/video | {count} | {count} | {count} |
| Upload frequency | {X}/week | {X}/week | {X}/week |
| Top video views | {count} | {count} | {count} |

Important Notes

  • The YouTube Data API has a daily quota of 10,000 units. Each search costs 100 units. Each video/channel lookup costs 1-3 units. Be efficient with API calls.
  • Batch video IDs in single requests (up to 50 per call) to conserve quota.
  • Subscriber counts below 1,000 are hidden by YouTube. The API returns 0 in these cases.
  • likeCount may not be available if the creator has hidden likes.
  • API results are public data only. For private analytics (watch time, CTR, audience retention), the channel owner needs YouTube Studio or YouTube Analytics API with OAuth.

Frequently asked questions

What does the Youtube Analytics AI skill do?

Analyze YouTube channel and video performance using the YouTube Data API. Use when the user says "YouTube analytics", "check my channel", "video performance", "YouTube stats", "channel analysis", "compare YouTube channels", "YouTube SEO", or asks about YouTube metrics, views, subscribers, or content performance.

Why use Youtube Analytics on TypingMind?

Because you install it once and use it with any model. Youtube Analytics 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 Youtube Analytics in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/OpenClaudia/openclaudia-skills/tree/main/skills/youtube-analytics. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Youtube Analytics?

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 Youtube Analytics?

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

Is the Youtube Analytics AI skill free?

Yes. It is published on GitHub by OpenClaudia 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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