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Video Production

Community
melgarafael
video-production

Video content expertise — scripts (YouTube format), descriptions, thumbnail concepts (text specs for sharp/SVG), storyboarding, video SEO (tags, chapters), hook-first methodology, and retention patterns. Use when producing any video-related content or strategy.

Overview

Publishermelgarafael
RepositorygrowthOS
Skill namevideo-production
Stars
58
Forks
33
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 melgarafael on GitHub. Read the source before you install it.

Installation

Install the Video Production 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/melgarafael/growthOS/tree/main/skills/video-production
  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/melgarafael/growthOS.git /tmp/growthOS
mkdir -p .claude/skills
cp -r /tmp/growthOS/skills/video-production .claude/skills/video-production
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Video Production 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 Video Production 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 Video Production 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.

Video Production Skill

Text-based video production expertise for YouTube and social video platforms. All outputs are text — no actual video/image generation.

Core Methodology: Hook-First

Every video script follows the hook-first pattern:

  1. Hook (0-5s): Pattern interrupt that stops the scroll. Must create curiosity gap or emotional trigger.
  2. Setup (5-30s): Context + promise of value. State exactly what the viewer will learn/get.
  3. Body (30s-end): Deliver value in clear segments with retention triggers every 60-90 seconds.
  4. CTA (final 15s): Single clear action. Never stack multiple CTAs.

Retention Triggers

Insert one every 60-90 seconds to prevent drop-off:

  • Pattern interrupt: Change visual pace, topic shift, "but here's where it gets interesting"
  • Open loop: Tease upcoming content: "I'll show you the exact template in a moment"
  • Social proof: Quick data point or result
  • Direct address: "Now you might be thinking..." — acknowledge viewer's mental state
  • B-roll cue: [CUT TO: screen recording / example / diagram]

Script Writing Format

markdown
# [VIDEO TITLE — YouTube optimized, <60 chars]

**Target length:** [X] minutes
**Audience:** [specific viewer persona]
**Core promise:** [single sentence — what viewer gets]

---

## HOOK (0:00 - 0:05)

[TALKING HEAD]
"[Pattern interrupt — question, bold statement, or surprising fact]"

## SETUP (0:05 - 0:30)

[TALKING HEAD + B-ROLL]
"[Context. What this video covers. Why it matters NOW.]"

**Retention checkpoint:** [open loop for upcoming content]

## SECTION 1: [Topic] (0:30 - X:XX)

[SCREEN RECORDING / B-ROLL]
"[Teaching content with specific, actionable steps]"

**Key point:** [One-sentence takeaway]

[Repeat sections as needed]

## CTA (X:XX - end)

[TALKING HEAD]
"[Single action: subscribe, link in description, comment]"

---

### SEO Metadata
- **Title:** [<60 chars, front-load keyword]
- **Description:** [see description template below]
- **Tags:** [10-15 relevant tags]
- **Chapters:** [timestamp markers from sections]

YouTube Description Template

markdown
[Hook sentence — repeat the core promise from the video]

In this video, I [action verb — show/teach/reveal/break down] [specific topic].

🔗 Resources mentioned:
- [Resource 1]: [URL]
- [Resource 2]: [URL]

⏱️ Chapters:
0:00 — [Hook/Intro]
0:30 — [Section 1 title]
X:XX — [Section 2 title]
[...]

📌 Key takeaways:
• [Takeaway 1]
• [Takeaway 2]
• [Takeaway 3]

[CTA paragraph — what to do next, related videos]

#[tag1] #[tag2] #[tag3]

Thumbnail Concept Spec

Thumbnails are described as text specs for sharp/SVG generation. Never attempt to generate actual images.

yaml
thumbnail:
  layout: [split | centered | thirds | before-after]
  dimensions: "1280x720"
  background:
    type: [solid | gradient | image-blur]
    value: "[color hex or gradient spec]"
  text:
    headline: "[3-5 words MAX — large, readable at mobile size]"
    font_weight: bold
    font_size: "~120px equivalent"
    color: "[high contrast against background]"
    position: [left | center | right]
    stroke: "[optional outline color for readability]"
  face:
    expression: "[emotion: surprise, excitement, curiosity, shock]"
    position: [left | right]
    size: "[head + shoulders, ~40% of frame]"
  accent_elements:
    - type: [arrow | circle | emoji | icon]
      position: "[x, y quadrant]"
      color: "[brand accent color]"
  contrast_score: "[high | medium — must be high for mobile feeds]"
  text_readability: "[mobile-first — readable at 100px thumbnail width]"

Thumbnail Best Practices

  • 3-word rule: Headline text should be 3-5 words maximum
  • Face + emotion: Human faces with exaggerated expressions increase CTR 30-40%
  • High contrast: Must be readable as a 100px-wide thumbnail on mobile
  • No clickbait: Thumbnail must honestly represent video content
  • Brand consistency: Use recurring color palette across video series
  • A/B variants: Always produce 2 thumbnail concepts for testing

Video SEO

Tags Strategy

yaml
tags:
  primary: "[exact target keyword]"
  secondary:
    - "[keyword variation 1]"
    - "[keyword variation 2]"
    - "[related topic 1]"
  long_tail:
    - "[question-format keyword]"
    - "[how-to format keyword]"
  brand:
    - "[channel name]"
    - "[series name if applicable]"
  total: 10-15  # sweet spot for YouTube

Chapter Markers

  • First chapter MUST start at 0:00
  • Minimum 3 chapters for YouTube to display them
  • Each chapter title: concise, keyword-rich, <40 chars
  • Chapters appear in Google search results — optimize for search intent

YouTube Algorithm Signals

SignalWeightHow to Optimize
Click-through rate (CTR)Very HighThumbnail + title testing
Watch timeVery HighHook-first + retention triggers
Session timeHighEnd screens + playlists
Engagement (likes/comments)MediumAsk specific questions in CTA
Upload consistencyMediumRegular schedule
Subscriber conversionMediumValue-first CTA placement

Storyboarding

yaml
storyboard:
  scene_number: [N]
  timestamp: "[start - end]"
  visual:
    type: [talking-head | screen-recording | b-roll | animation | text-overlay]
    description: "[what viewer sees]"
  audio:
    narration: "[script text for this segment]"
    music: "[mood: upbeat, chill, dramatic, none]"
    sfx: "[sound effect if any]"
  text_overlay: "[on-screen text, lower-thirds, callouts]"
  transition: "[cut | fade | zoom | swipe]"
  retention_device: "[hook | open-loop | proof | direct-address | none]"
  notes: "[production notes, emphasis points]"

Output Types

OutputFormatUse Case
Video scriptMarkdownFull production script
YouTube descriptionMarkdownVideo description + SEO
Thumbnail specYAMLSpec for sharp/SVG rendering
StoryboardYAMLScene-by-scene production plan
Tags listYAMLYouTube tag optimization
Chapter markersMarkdownYouTube chapters

Anti-Patterns

  • Starting with "Hey guys, welcome back" — hook first, intro later
  • Stacking multiple CTAs — one CTA per video
  • Ignoring mobile thumbnail readability
  • Generic tags like "tutorial" or "how to"
  • Description with only links, no searchable text
  • Chapters without 0:00 start marker
  • Scripts without retention triggers in long-form content

Integration with Remotion Rendering

This skill produces text-based storyboards and scripts. To convert a storyboard into an actual MP4 video, hand off to the remotion-video skill and the video-producer agent, which handle:

  1. Storyboard → Composition mapping (remotion-video skill)
  2. Remotion environment discovery (auto-detects CLI, entry point, and deps on any machine)
  3. Render to MP4 (video-producer agent Phase 3)

Handoff flow:

video-production skill (this skill) → generates storyboard YAML
  ↓
remotion-video skill → maps to composition JSON (frame calculations, animations)
  ↓
video-producer agent → discovers Remotion CLI, renders MP4, opens video

The storyboard YAML format from this skill is compatible with the remotion-video composition mapping when these fields are present per scene: type, duration (seconds), text, animation, transition. The remotion-video skill calculates startFrame and durationFrames automatically.

Frequently asked questions

What does the Video Production AI skill do?

Video content expertise — scripts (YouTube format), descriptions, thumbnail concepts (text specs for sharp/SVG), storyboarding, video SEO (tags, chapters), hook-first methodology, and retention patterns. Use when producing any video-related content or strategy.

Why use Video Production on TypingMind?

Because you install it once and use it with any model. Video Production 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 Video Production in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/melgarafael/growthOS/tree/main/skills/video-production. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Video Production?

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 Video Production?

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

Is the Video Production AI skill free?

Yes. It is published on GitHub by melgarafael 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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