youtube-own-channel-analyzer

youtube-own-channel-analyzer is a skill for Claude Code, Codex from nikhilbhansali/youtube-data-skills. It costs 118 tokens per session (1,333 once invoked), scanned A, original, MIT.

A skill that analyzes a YouTube channel using the YouTube Data API, a service for retrieving channel and video information. It examines uploads, viewing and engagement measures, content patterns, and growth trends.

In plain words
What is it for?
Use it to analyze your own channel by handle, URL, or channel ID, choose how many uploads to inspect, and produce JSON data and a written report.
Why use it?
It turns channel data into a structured analysis instead of requiring you to inspect each video and metric manually.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to analyze your own channel by handle, URL, or channel ID, choose how many uploads to inspect, and produce JSON data and a written report.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/nikhilbhansali/youtube-data-skills/youtube-own-channel-analyzer
Install

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.

Any agent
npx skills add nikhilbhansali/youtube-data-skills --skill youtube-own-channel-analyzer
Clone the repo
git clone --depth 1 https://github.com/nikhilbhansali/youtube-data-skills

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for youtube-own-channel-analyzer

README.md
[![agentmods](https://agentmods.dev/badge/skills/nikhilbhansali/youtube-data-skills/youtube-own-channel-analyzer/github.svg)](https://agentmods.dev/skills/nikhilbhansali/youtube-data-skills/youtube-own-channel-analyzer)
Your own site
<a href="https://agentmods.dev/skills/nikhilbhansali/youtube-data-skills/youtube-own-channel-analyzer"><img src="https://agentmods.dev/badge/skills/nikhilbhansali/youtube-data-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.

agentmods 80×15 button for youtube-own-channel-analyzer

Your own site · 80×15
<a href="https://agentmods.dev/skills/nikhilbhansali/youtube-data-skills/youtube-own-channel-analyzer"><img src="https://agentmods.dev/badge/skills/nikhilbhansali/youtube-data-skills/youtube-own-channel-analyzer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 118 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,333 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00118 $0.01333
Opus 5 $0.00059 $0.00666
Sonnet 5 $0.00024 $0.00267
Haiku 4.5 $0.00012 $0.00133

Measured 12d ago against content hash 6fbd4f85c4bd, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

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 12d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/analyze_channel.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

skills/youtube-own-channel-analyzer/SKILL.md · 157 lines

How it starts

The opening of the file, as written. The whole thing — 157 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.

Usage

/youtube-own-channel-analyzer @MyChannel
/youtube-own-channel-analyzer https://youtube.com/@MyChannel
/youtube-own-channel-analyzer UCxxxxxxxxxxxxxxxxxxxxxx --max-videos 300

Instructions

Step 1: Parse Arguments

  • Channel (required): @handle, channel URL, or channel ID (UC...)
  • --max-videos N (optional): how many recent uploads to analyze (default: 200, 0 = all)

Step 2: Get the API Key

Get a key from Google Cloud Console with YouTube Data API v3 enabled. Check Claude memory first; if not found, ask the user to paste it.

Step 3: Run the Bundled Script

Run scripts/analyze_channel.py — resolve the path relative to this skill's own directory:

YT_API_KEY=API_KEY python3 <skill-dir>/scripts/analyze_channel.py "@CHANNEL" [--max-videos N]

Dependency: pip3 install google-api-python-client.

Step 4: Read the Data

reports/data/channel-analysis-<channel-slug>-<YYYY-MM-DD>.json

Step 5: Write the Report

Write to the path the script printed: reports/channel-analysis-<channel-slug>-<YYYY-MM-DD>.md

What the Script Computes

Channel Resolution

@handlechannels.list(forHandle=...); /channel/UC... and bare UC...channels.list(id=...); legacy /user/nameforUsername. Never search.list.

Video Collection

contentDetails.relatedPlaylists.uploadsplaylistItems.list pagination → videos.list in batches of 50.

Content Types

Categorized by title/description/tag 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
Other no match

Duration Buckets

Short (<5 min), Medium (5-15 min), Long (15-30 min), Very Long (30+ min).

Read the full file on GitHub · 157 lines

Files

What ships with it

2 files 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.

Changes

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.

  1. 12d ago First seen · 157 lines · 118 tokens per session scan A 6fbd4f85c4bd

Subscribe to this mod's changes

youtube-own-channel-analyzer is a skill published in the GitHub repository nikhilbhansali/youtube-data-skills (2 stars, last pushed 25d ago), licensed MIT. It adds 118 tokens to every session and 1,333 once invoked, about $0.0006 per session on Opus 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.

Related

Other skills, from other repositories

systematic-debugging

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.

obra/superpowers · 21 tokens

local-ai-agents

Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…

microsoft/ai-agents-for-beginners · 200 tokens

next-cache-components-adoption

Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…

vercel/next.js · 95 tokens

chat-pet-sprite-creation

Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.

microsoft/vscode · 53 tokens

cpu-profile-analysis

Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…

microsoft/vscode · 71 tokens

insight-error-page

Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…

vercel/next.js · 83 tokens