youtube-outlier-research

youtube-outlier-research is a skill for Claude Code from AdesiaHQ/bangermap-integrations. It costs 86 tokens per session (1,187 once invoked), scanned A, original, MIT.

A research workflow that finds YouTube videos that performed unusually well compared with their own channel. It uses a channel's typical views as the baseline and compares Shorts separately from longer videos.

In plain words
What is it for?
Finding channel outliers, checking whether a video exceeded its normal performance, measuring typical view counts, studying what works in a niche, and suggesting content ideas based on those results.
Why use it?
Raw view counts can be misleading: a large number may be normal for one channel but exceptional for another. The workflow accounts for channel size, video format, and the time needed for views to settle.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the youtube-outlier-research plugin — 1 skill, 1 MCP server shipped together

Good fit Finding channel outliers, checking whether a video exceeded its normal performance, measuring typical view counts, studying what works in a niche, and suggesting content ideas based on those results.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/adesiahq/bangermap-integrations/youtube-outlier-research
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 AdesiaHQ/bangermap-integrations --skill youtube-outlier-research
Clone the repo
git clone --depth 1 https://github.com/AdesiaHQ/bangermap-integrations

Made for: Claude Code.

Or install youtube-outlier-research, the plugin that ships this one along with the rest of its 1 skill, 1 MCP server.

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-outlier-research

README.md
[![agentmods](https://agentmods.dev/badge/skills/adesiahq/bangermap-integrations/youtube-outlier-research/github.svg)](https://agentmods.dev/skills/adesiahq/bangermap-integrations/youtube-outlier-research)
Your own site
<a href="https://agentmods.dev/skills/adesiahq/bangermap-integrations/youtube-outlier-research"><img src="https://agentmods.dev/badge/skills/adesiahq/bangermap-integrations/youtube-outlier-research/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-outlier-research

Your own site · 80×15
<a href="https://agentmods.dev/skills/adesiahq/bangermap-integrations/youtube-outlier-research"><img src="https://agentmods.dev/badge/skills/adesiahq/bangermap-integrations/youtube-outlier-research.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 86 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,187 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 $0.00086 $0.01187
Opus 5 $0.00043 $0.00593
Sonnet 5 $0.00017 $0.00237
Haiku 4.5 $0.00009 $0.00119

Measured 10d ago against content hash b937d648eaab, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

youtube-outlier-research 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 10d 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.

skills/youtube-outlier-research/SKILL.md · 76 lines

How it starts

The opening of the file, as written. The whole thing — 76 lines — stays where its author put it; the contents beside it link to each section on GitHub.

YouTube outlier research

A video's view count says more about the channel it sits on than about the video. 500,000 views is unremarkable on a channel that always gets a million, and 60,000 views is a discovery on a channel that usually gets 4,000. The second is evidence that an idea worked without an audience carrying it, which is the only evidence worth copying.

So the unit of analysis is the multiple, meaning a video's views divided by its own channel's baseline. Report multiples. Never rank videos from different channels by raw views.

The measurement

Baseline. The median views of the channel's 30 most recent settled uploads of the same format. Median rather than mean, because one runaway hit drags a mean upward and makes every other upload look like a failure against it.

Settled means published at least 7 days ago. Younger uploads are still accumulating views, so including them drags the baseline down and understates every multiple.

Format splits at 180 seconds. Shorts and long-form get separate baselines and are never compared to each other, since a 40-second video judged against 20-minute numbers is flattered or punished by nothing but its length.

Typical range is the 25th to 75th percentile of the same pool. A video inside that range is the channel doing what it always does, whatever its raw views.

Not enough data. Fewer than 5 settled uploads in a format means no baseline for that format. Say so rather than computing one from three videos.

Reading a result

  • Below 1x is under the channel's own normal, whatever the view count looks like.
  • 3x is the common bar for calling something an outlier.
  • 10x and above reshapes a niche, and is rare enough to be worth checking for a cause, a collaboration, an external link, or a video that got picked up somewhere.

Two things change the reading and are the most common analysis errors.

Age. A 12x from two years ago has already been absorbed, every serious competitor made their version. A 12x from last month is still open. Always report how old an outlier is next to its multiple.

Read the full file on GitHub · 76 lines

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. 10d ago First seen · 76 lines · 86 tokens per session scan A b937d648eaab

Subscribe to this mod's changes

youtube-outlier-research is a skill published in the GitHub repository AdesiaHQ/bangermap-integrations (0 stars, last pushed 1mo ago), licensed MIT. It adds 86 tokens to every session and 1,187 once invoked, about $0.0004 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.

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