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Youtube Title Outlier Packaging

Community
timscheuerai
youtube-title-outlier-packaging

Find YouTube packaging outliers in the author's niche, draft 2 A/B titles from proven structures with every claim marked, and brief thumbnail concepts. Use when choosing or split-testing a YouTube title or packaging a video before recording.

Overview

Publishertimscheuerai
Repositorylinkedin-funnel
Skill nameyoutube-title-outlier-packaging
Stars
60
Forks
20
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 timscheuerai on GitHub. Read the source before you install it.

Installation

Install the Youtube Title Outlier Packaging 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/timscheuerai/linkedin-funnel/tree/main/skills/youtube-title-outlier-packaging
  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/timscheuerai/linkedin-funnel.git /tmp/linkedin-funnel
mkdir -p .claude/skills
cp -r /tmp/linkedin-funnel/skills/youtube-title-outlier-packaging .claude/skills/youtube-title-outlier-packaging
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Youtube Title Outlier Packaging 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 Title Outlier Packaging 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 Title Outlier Packaging 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.

Workspace: all context paths below (identity, audience, strategy, voice, brand, raw, output, index, log, workspace preferences and context scripts) resolve from context/ in the kit root. Read both the root AGENTS.md and context/AGENTS.md.

YouTube title and outlier packaging

Turn a video idea into packaging: measured outlier references, 2 title variants for a split test and concrete thumbnail concepts. The author owns the final pick. This skill prepares the brief; it does not upload or invent proof.

Read identity/proof.md before any public number, audience/ideal-follower.md for who the title must stop, and the brief from content-waterfall when there is one. Pair with youtube-thumbnail-outlier-research for thumbnails and youtube-thumbnail for the render.

Rules

  • Deliverable claims only. A number in a title is packaging until the author can show it in the video. Never invent metrics or present another creator's results as the author's.
  • No forced comparison. Do not package "A vs B vs C" unless the video is a comparison.
  • Packaging first. Draft titles before the outline; the outline must then deliver the promise.

Find outliers

Use what is available, in this order:

  1. The vidIQ plugin, if installed: its outlier and trend views across channels.
  2. The YouTube Data API or manual research: search the niche for the last 3 to 6 months.
  3. Channels the author's audience already watches.

An outlier is a video doing far better than its own channel's normal. The fair comparison is current views against the median of the same channel's videos of similar age and length (at least 5 of them). A ratio without peers is not evidence. These ratios compare current views; they are not views per hour, click-through rate or proof that the title caused the result.

Filter by hand: drop live streams, videos under 4 minutes and off-audience topics; check the description for sponsorship; open the real thumbnail and note the text, face or UI and the 1 dominant promise. Keep the URLs. Shortlist 5 to 10 references.

Cross-niche validation

Before a structure goes into an A/B pair, prove it outside the niche: youtube-video-research (gate 2) searches 4 to 6 donor niches and keeps a structure only with 3x outliers from 3 or more channels in 2 or more niches, and only if the author's niche has not worn it out.

Title structures

Borrow the shape, never the exact wording.

StructurePatternWhen it fits
Experiment"I Let X Run My Y" / "Can X Actually...?"A visible test with a verdict
Cheat code"... So Easy It Feels Illegal"A strong outcome the video really shows
Countable payload"10 Agents That..." / "(30+ Examples)"A roster on screen
Volume and timeVolume of work tied to a timeboxSpeed is the story
Metric arrow"Build → Sell?" / before and after1 visual consequence
Exact numberA specific figure beats a round oneOnly with on-video proof
Reversal"AI Can Find 10'000 Leads. Why Is Nobody Buying?"Problem framing for new viewers

Avoid "The X nobody talks about" and career titles when the buyer is a founder.

Draft the A/B pair

  1. Name the video's real outcome in 1 line.
  2. Pick 2 different structures, not 2 synonyms of the same hook.
  3. Put a risky or numeric claim in parentheses or the second clause so it can be swapped.
  4. Mark each claim: proven, on video only or packaging hypothesis.
  5. Deliver Variant A and Variant B, each with a 1-line reason.

Thumbnail concepts

From the references, note text placement, face or screen, numeral size, colour and what the eye hits first at 320 x 180. Write 4 to 6 concepts as: text / visual / colour / what not to do. 1 dominant promise per thumbnail, 2 to 5 words on it. Hand the chosen concepts to youtube-thumbnail.

Deliver

The shortlist of references with URLs, the A/B titles with claim status, the thumbnail concepts and anything the author must confirm before publishing.

Frequently asked questions

What does the Youtube Title Outlier Packaging AI skill do?

Find YouTube packaging outliers in the author's niche, draft 2 A/B titles from proven structures with every claim marked, and brief thumbnail concepts. Use when choosing or split-testing a YouTube title or packaging a video before recording.

Why use Youtube Title Outlier Packaging on TypingMind?

Because you install it once and use it with any model. Youtube Title Outlier Packaging 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 Title Outlier Packaging in TypingMind?

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

Which AI models can use Youtube Title Outlier Packaging?

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 Title Outlier Packaging?

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

Is the Youtube Title Outlier Packaging AI skill free?

Yes. It is published on GitHub by timscheuerai 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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