mrbeast-perspective

Role-plays MrBeast to advise on YouTube titles, thumbnails, hooks, and retention optimization.

50|3|Updated Jun 30, 2026
One-click install
npx skills add https://github.com/Dubaoxu/distillation-skills --skill mrbeast-perspective-dubaoxu
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: mrbeast-perspective
Source: https://github.com/Dubaoxu/distillation-skills/tree/main/nuwa-skill/examples/mrbeast-perspective
Command: npx skills add https://github.com/Dubaoxu/distillation-skills --skill mrbeast-perspective-dubaoxu

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires yt-dlp, Pillow, and includes scripts (resource) and references (resource) components.

What problem does it solve? Creators struggle to improve video click-through rates, retention curves, and title/thumbnail effectiveness without access to proven viral content methodology. This Skill channels MrBeast's content creation operating system—distilled from his leaked 36-page production handbook, six in-depth podcasts, and decision records—to give concrete, data-driven YouTube optimization advice in his first-person voice. ## Core Features & Use Cases - Immersive Persona Role-Play: Responds as MrBeast in first person, applying 6 mental models (CTR × AVD equation, stair-stepping, simple concept × extreme execution) and 8 decision heuristics to content questions. - Agentic Research Workflow: Classifies questions, then uses web search to pull real CTR benchmarks, competitor data, and trend information before answering—never guessing from stale training data. - Runnable Analysis Scripts: Includes four tools—YouTube subtitle fetching, title pattern analysis, retention curve checking against MrBeast's pacing rules, and thumbnail-title complementarity auditing. - Use Case: A creator asks why their video titled "10 AI Coding Tips" underperforms; the Skill diagnoses the title against MrBeast's formulas, rewrites it with a concrete hook, and prescribes a 0-30 second hook structure targeting >90% retention. ## Quick Start Ask the Skill to review your video title, thumbnail concept, or opening 30 seconds using MrBeast's perspective, for example: "Use the MrBeast perspective to critique my title 'My Morning Routine' and improve my retention curve."

Frequently Asked Questions about mrbeast-perspective

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I improve my YouTube click-through rate with better titles?

Apply MrBeast's title formulas: keep titles under 8 words, put numbers first, and use patterns like money anchoring, first-person challenges, or extreme contrasts. The included analyze_titles.py script classifies your titles into formula categories and reports length and number-usage statistics.

How to check video script retention before publishing?

Run retention_curve_checker.py on your script file. It scores five dimensions—opening hook, re-engagement moments every 3-5 minutes, ending CTA, boring low-action segments, and escalation structure—then outputs a graded Markdown report with specific fixes.

What makes a good YouTube thumbnail according to MrBeast?

Three elements: one expressive face, one focal object, and one implied question, with at most 3-5 words of text. The thumbnail_audit.py script checks title-thumbnail complementarity, text volume, emotional signal, and curiosity gap, and can analyze image brightness, contrast, and saturation if Pillow is installed.

Does this Skill work for platforms other than YouTube?

The methodology is optimized for YouTube's algorithm. Core principles like CTR × AVD and simple concepts transfer to Bilibili, Douyin, or other platforms, but the Skill's own honesty boundaries note that algorithm weights, title conventions, and cultural references differ and require adaptation.

Why does the Skill search the web before answering content questions?

Its agentic protocol requires real data before giving advice: for questions about specific niches, competitors, or trends, it must fetch current CTR benchmarks and competitor performance via web search rather than relying on potentially outdated training knowledge.