Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add nikhilbhansali/claude-youtube-skills --skill youtube-own-channel-analyzergit clone --depth 1 https://github.com/nikhilbhansali/claude-youtube-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/nikhilbhansali/claude-youtube-skills/youtube-own-channel-analyzer)<a href="https://agentmods.dev/skills/nikhilbhansali/claude-youtube-skills/youtube-own-channel-analyzer"><img src="https://agentmods.dev/badge/skills/nikhilbhansali/claude-youtube-skills/youtube-own-channel-analyzer/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/nikhilbhansali/claude-youtube-skills/youtube-own-channel-analyzer"><img src="https://agentmods.dev/badge/skills/nikhilbhansali/claude-youtube-skills/youtube-own-channel-analyzer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00118 | $0.00929 |
| Opus 5.5 | $0.00047 | $0.00372 |
| Sonnet 5.5 | $0.00024 | $0.00186 |
| Haiku 4.5 | $0.00012 | $0.00093 |
Grade A, and why
youtube-own-channel-analyzer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 101 lines — stays where its author put it; the contents beside it link to each section on GitHub.
YouTube Own Channel Analyzer
Analyze your YouTube channel's performance using the YouTube Data API v3.
Setup
- API Key: Get from Google Cloud Console - enable YouTube Data API v3
- Channel ID: Accept channel ID (UC...), @handle, or full URL
API Endpoints
BASE_URL = https://www.googleapis.com/youtube/v3
# Channel details
GET /channels?part=snippet,statistics,contentDetails,brandingSettings&id={channelId}&key={API_KEY}
# Channel videos (paginated)
GET /search?part=snippet&channelId={channelId}&order=date&type=video&maxResults=50&key={API_KEY}
# Video details (batch up to 50 IDs)
GET /videos?part=snippet,statistics,contentDetails&id={videoIds}&key={API_KEY}
Analysis Workflow
1. Resolve Channel ID
@handle → Search API with handle, get channelId from result
/channel/UC... → Extract directly
/c/name or /user/name → Use forUsername parameter
2. Fetch Data
- Channel: snippet, statistics, contentDetails, brandingSettings
- Videos: Paginate through search results, then batch video details
3. Analyze
Content Types - Categorize by title/description patterns:
| Type | Pattern |
|---|---|
| Tutorial | tutorial, how to, guide, learn |
| Review | review, unbox, first look, comparison |
| Vlog | vlog, day in, life, daily |
| Educational | explain, education, lesson |
| Gaming | gameplay, game, gaming, stream |
| Music | music, song, cover, lyrics |
Duration Buckets: Short (<5min), Medium (5-15min), Long (15-30min), Very Long (30+min)
Performance Metrics:
- View-to-sub ratio: views/subscribers (benchmark: 10-20%)
- Engagement rate: (likes+comments)/views (benchmark: 1-5%)
- Like rate: likes/views (benchmark: 3-7%)
- Comment rate: comments/views (benchmark: 0.5-2%)
- Viral threshold: views > 5x subscribers
- Underperforming: views < 10% subscribers
Upload Patterns: Track day-of-week, hour-of-day, consistency (stddev of days between uploads)
Title Analysis: Track numbers, emojis, questions, brackets, caps usage, common words
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2mo ago First seen · 101 lines · 118 tokens per session scan A ea3a21af3876
youtube-own-channel-analyzer is a skill published in the GitHub repository nikhilbhansali/claude-youtube-skills (5 stars, last pushed 9mo ago), licensed MIT. It adds 118 tokens to every session and 929 once invoked, about $0.0005 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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