YouTube outlier research on your own free API key. An MCP server and an n8n community node, sharing the Bangermap scoring engine.
Bangermap integrations
以下摘录自该仓库的 README(片段) · 在 GitHub 读完整 README →
YouTube outlier research as installable pieces, each running on your own free YouTube Data API key.
An outlier is a video that beat its own channel's normal performance. A 10x on a small channel is a stronger signal than a million views on a channel that always gets a million, because it is proof an idea worked without an audience behind it. Everything here measures that, and nothing here meters you.
Quick start
Get a free YouTube Data API key from the Google Cloud console, which is two screens and no billing details. Enable YouTube Data API v3, create an API key, and keep it to hand.
Claude Code, one command.
Claude Desktop, in claude_desktop_config.json.
Any other MCP client works the same way. The server speaks stdio, needs only YOUTUBE_API_KEY in its environment, and starts with no key at all if you just want to inspect its tools.
Then ask for what you want
The tools are written to be picked by an agent from a plain request, so these all work as typed.
- Which of MrBeast's recent uploads overperformed?
- Is this video an outlier? https://www.youtube.com/watch?v=...
- What is a normal view count for @veritasium?
- Compare @mkbhd and @mrwhosetheboss, who is having the better month?
- Find channels like @mkbhd and show me what is working across them right now.
The four tools
Every channel argument takes a channel URL, an @handle, a channel id, or the URL of any video on that channel.
| Tool | Arguments | What it returns | Quota |
| --- | --- | --- | --- |
| find_outliers | channel, depth (light 50, standard 100, deep 200), limit | One channel's recent uploads ranked by multiple against its own baseline | ~5 units |
| channel_baseline | channel | What normal looks like, baseline views, subscribers, uploads in the last 90 days, best recent multiple | ~5 units |
| compare_channels | channels (separated by spaces, commas or newlines), depth, limit | The strongest outliers across several channels, each scored against its own baseline | ~5 units per channel |
| sweep_niche | seed, max_channels, limit | Channels featured by the seed channel, then the strongest outliers across all of them | ~5 units per channel found |
A free key carries 10,000 units a day, so roughly 1,100 channel scans.
The skill
The server does the arithmetic. The skill teaches the method, what a baseline is, how to read a multiple, why age changes the answer, and when one outlier is noise. It follows the open Agent Skills format, so it works in any client that reads skills.
In Claude Code, this repo is a plugin marketplace.
Also here
| Package | What it is |
| --- | --- |
| bangermap-mcp | The MCP server above |
| n8n-nodes-bangermap | An n8n community node, the same four operations, one output item per outlier, in its own repo |
The server reads src/lib, the scoring engine and YouTube client it shares with the Bangermap app, its free browser tools and the n8n node. One definition of an outlier, four places it runs, so they cannot disagree.
Why bring your own key
Google gives every account a free YouTube Data API key with 10,000 units a day. Reading a channel costs one unit. Tools that resell those calls as credits are charging for something that is free on your own key, and metering it is the only way that business works.