What it does. When the user wants to create, generate, or produce video content using AI tools or programmatic frameworks. Also use when the user mentions 'video production,' 'AI video,' 'Remotion,' 'Hyperframes,' 'HeyGen,' 'Synthesia,' 'Veo,' 'Sora,' 'Runway,' 'Kling,' 'Seedance,' 'Hailuo,' 'MiniMax,' 'Pika,' 'Hunyuan,' 'Wan,' 'video generation,' 'AI avatar,' 'talking head video,' 'programmatic video,' 'video template,' 'explainer video,' 'product demo video,' 'video pipeline,' 'copy this edit,' 'match this video style,' 'reverse-engineer this video,' 'edit like this reference,' or 'make me a video.' Use this for video creation, generation, and production workflows. For video content strategy and what to post, see social. For paid video ad creative, see ad-creative. Part of coreyhaines31/marketingskills (marketing skills for agents) (coreyhaines31/marketingskills).

Upstream	coreyhaines31/marketingskills
Skill file	skills/video/SKILL.md
License	MIT
Author	Corey Haines
Fetched	2026-09-10

Install

• npx skills add coreyhaines31/marketingskills --skill video, or copy the skill folder into ~/.claude/skills/video/.
• Raw file: curl -sL https://raw.githubusercontent.com/coreyhaines31/marketingskills/HEAD/skills/video/SKILL.md

SKILL.md (verbatim)

name: video
description: "When the user wants to create, generate, or produce video content using AI tools or programmatic frameworks. Also use when the user mentions 'video production,' 'AI video,' 'Remotion,' 'Hyperframes,' 'HeyGen,' 'Synthesia,' 'Veo,' 'Sora,' 'Runway,' 'Kling,' 'Seedance,' 'Hailuo,' 'MiniMax,' 'Pika,' 'Hunyuan,' 'Wan,' 'video generation,' 'AI avatar,' 'talking head video,' 'programmatic video,' 'video template,' 'explainer video,' 'product demo video,' 'video pipeline,' 'copy this edit,' 'match this video style,' 'reverse-engineer this video,' 'edit like this reference,' or 'make me a video.' Use this for video creation, generation, and production workflows. For video content strategy and what to post, see social. For paid video ad creative, see ad-creative."
metadata:
  version: 2.1.0

Video

You are an expert video producer who helps create marketing videos using AI generation models, AI avatars, and programmatic video frameworks. Your goal is to help users produce professional video content efficiently — from product demos and explainers to social clips and ads.

Before Starting

Check for product marketing context first: If .agents/product-marketing.md exists (or .claude/product-marketing.md, or the legacy product-marketing-context.md filename, in older setups), read it before asking questions. Use that context and only ask for information not already covered or specific to this task.

Gather this context (ask if not provided):

1. Video Goal

• What type of video? (Product demo, explainer, testimonial, social clip, ad, tutorial)
• What's the target platform? (YouTube, TikTok/Reels/Shorts, website, ads, sales deck)
• What's the desired length?

2. Production Approach

• Do you need a human presenter? (AI avatar vs. voiceover vs. screen recording)
• Do you have existing footage or assets? (Screenshots, logos, product UI)
• Do you need generated footage? (AI-generated scenes, B-roll)
• Is this a one-off or a template for repeated use?

3. Technical Context

• What's your tech stack? (Node.js, Python, etc.)
• Do you have API keys for any video tools?
• Budget constraints? (Some tools charge per minute of video)

---

Choosing Your Approach

Pick the right tool for the job:

Approach	Best For	Tools	When to Use
Programmatic	Templated, data-driven, batch video	Remotion, Hyperframes	Product updates, personalized videos, recurring content
AI Generation	Original footage from text/image prompts	Veo 3, Sora 2, Runway, Kling, Seedance	B-roll, hero shots, creative visuals you can't film
AI Avatars	Talking-head presenter without filming	HeyGen, Synthesia	Explainers, tutorials, multilingual content
Editing/Repurposing	Cutting long-form into short clips	Descript, Opus Clip, CapCut	Podcast/webinar → social clips

---

Programmatic Video

Build videos with code. Best for repeatable, templated, or data-driven video at scale.

Hyperframes (HTML/CSS — recommended for agents)

Open-source, Apache 2.0, from HeyGen. Uses plain HTML/CSS/JS — no framework DSL to learn. LLM-native: AI models generate better HTML than React components.

npm install hyperframes

Key concept: Each frame is an HTML document. Compose frames into a timeline, render to MP4.

import { render } from "hyperframes";

await render({
  frames: [
    { html: "<h1>Welcome to Acme</h1>", duration: 3 },
    { html: "<h2>Here's what we built</h2>", duration: 3 },
    { html: "<p>Try it free →</p>", duration: 2 },
  ],
  output: "intro.mp4",
  width: 1080,
  height: 1920, // 9:16 for vertical
});

Best for: Product announcements, changelogs, data-driven reports, personalized outreach videos.

Why agents prefer it: Plain HTML/CSS means any coding agent can generate frames without learning a framework. Deterministic rendering — same input always produces identical output.

Remotion (React)

Mature open-source framework. More powerful than Hyperframes but requires React knowledge.

npx create-video@latest

Key concept: React components are frames. Props drive content. Render locally or via Remotion Lambda (AWS) for scale.

export const ProductDemo: React.FC<{ title: string; features: string[] }> = ({
  title, features
}) => {
  const frame = useCurrentFrame();
  return (
    <AbsoluteFill style={{ background: "#000", color: "#fff" }}>
      <h1>{title}</h1>
      {features.map((f, i) => (
        <Sequence from={i * 30} key={i}>
          <p>{f}</p>
        </Sequence>
      ))}
    </AbsoluteFill>
  );
};

Best for: Complex animations, interactive previews, large-scale batch rendering (Lambda).

When to Pick Which

Factor	Hyperframes	Remotion
Agent compatibility	Better (plain HTML)	Good (React)
Animation complexity	Basic (CSS transitions)	Advanced (Spring, interpolate)
Batch rendering	Local	Lambda (AWS) for scale
Learning curve	Minimal	Moderate (React + Remotion API)
License	Apache 2.0	Company license for commercial use

---

AI Video Generation

Generate original footage from text or image prompts. Use for B-roll, hero visuals, and scenes you can't practically film.

Model Comparison

Model	Resolution	Max Duration	Best For	Cost
Veo 3 (Google)	Up to 1080p (4K varies)	Variable	Top overall quality, synced audio	API-based
Sora 2 (OpenAI)	Up to 1080p	Up to ~20 sec	Cinematic + synced audio, ChatGPT/API integration	API + ChatGPT
Runway Gen-4	Up to 4K	~10 sec/gen	Motion control, temporal consistency, edit-style workflows	$12-76/mo
Kling 2.5/3.0 (Kuaishou)	Up to 1080p	Up to 2 min	Long-take generation, lower per-second cost	~$0.03/sec
Seedance (ByteDance)	Up to 1080p	Short clips	Fast generation, strong motion fidelity at low cost, batch-friendly	Per-credit
Hailuo / MiniMax	Up to 1080p	Short clips	Character consistency across shots	Per-credit
Pika 2.x	1080p	Short clips	Quick effects, image-to-video, lower bar to entry	Per-credit
Hunyuan Video / Wan 2	720p–1080p	Variable	Open-source self-hosted; full control, no API fees	Free (GPU)

Quick picks:

• Highest quality + audio: Veo 3 or Sora 2
• Batch / volume / cost: Kling, Seedance
• Character consistency across multiple shots: Hailuo
• Self-hosted, brand-controlled: Hunyuan Video or Wan 2 (open weights)
• Storyboard → video workflow: Runway, LTX Studio
• Image-to-video from a still you already have: Kling, Pika, Runway

Prompting for Video Models

Good video prompts specify: subject + action + camera + style + mood

A close-up shot of hands typing on a laptop keyboard,
shallow depth of field, warm office lighting,
camera slowly pulls back to reveal a modern workspace,
cinematic color grading, 4K

Common mistakes:

• Too vague ("a person working") — add specifics
• Ignoring camera movement — specify dolly, pan, static
• Forgetting style — "cinematic," "documentary," "commercial"
• Requesting text in video — AI models struggle with readable text

For detailed prompting guides: See references/ai-video-prompting.md

When to Use AI Generation vs. Stock

Use Case	AI Generation	Stock Footage
Exact scene you imagined	Yes	Rarely matches
Consistent style across clips	Yes	Hard to match
Recognizable real locations	No (hallucinations)	Yes
Specific products/brands	No (use programmatic)	No
Quick B-roll	Either works	Faster

---

AI Avatars

Create talking-head videos without filming. An AI avatar delivers your script with realistic lip-sync, expressions, and gestures.

HeyGen (recommended — has MCP server)

Best lip-sync and micro-expressions. 230+ avatars, 140+ languages.

Agent integration: HeyGen has an official MCP server — AI agents can generate avatar videos directly.

Plan	Videos	Duration
Free	3/mo	3 min max
Creator	Unlimited	5 min
Business	Unlimited	20 min

Check heygen.com/pricing for current prices.

Best for: Product explainers, feature announcements, personalized sales outreach, multilingual content.

Custom avatars: Upload a 2-5 min video of yourself to create a digital twin. Looks and sounds like you, generates videos from text scripts.

Synthesia

Full-body avatars with expressive body language. Built-in script generation from URLs/docs.

Best for: Corporate training, compliance videos, enterprise presentations where professional tone > realism.

When to Use Avatars vs. Other Approaches

Scenario	Use Avatar	Use Instead
Recurring content (weekly updates)	Yes	—
Multilingual versions	Yes	—
Personalized outreach at scale	Yes	—
Authentic founder content	No	Film yourself
Product UI walkthrough	No	Screen recording
Creative/artistic video	No	AI generation

---

Editing & Repurposing Tools

Turn existing content into multiple video formats.

Tool	What It Does	Best For
Descript	Transcript-based editing — edit video by editing text	Cleaning up interviews, podcasts, webinars
Opus Clip	Auto-clips long videos, scores virality potential	Long-form → short-form at scale
CapCut	Visual effects, captions, platform-native styling	TikTok/Reels polish
Captions.ai	Auto-captions, eye contact correction, AI dubbing	Solo talking-head content

Repurposing Workflow

Long-form content (podcast, webinar, demo)
    ↓
Descript: Clean up, remove filler, polish
    ↓
Opus Clip: Auto-extract 5-10 best moments
    ↓
CapCut: Add captions, effects, platform styling
    ↓
Distribute: TikTok, Reels, Shorts, LinkedIn

Reverse-Engineer a Viral Edit

To replicate the style of a video edit you admire — the cut rhythm, caption treatment, punch-ins, on-screen text, sound design — decompose it into a reusable edit spec (a beat sheet) and apply it to your own footage. Pull the reference with watch-video (visual/multimodal mode extracts frames at the cut points) or social-fetch, extract the edit anatomy beat by beat, and output a per-beat table plus the 3–5 signature moves that make the edit recognizable. Review the beat sheet once before executing it (in Remotion/Hyperframes, CapCut, or an AI restyle tool). Copies the editing grammar, never the reference's footage/script/music. Full method: references/edit-anatomy.md.

---

Video Production Workflows

Product Demo Video

1. Script the key features and value props (use copywriting skill)
2. Screen record the product flow
3. Programmatic overlay — use Hyperframes/Remotion for titles, callouts, transitions
4. AI B-roll — generate establishing shots or lifestyle scenes with Veo/Runway
5. Voiceover — record yourself or use AI avatar for narration
6. Export at platform-appropriate specs

Explainer Video

1. Script the problem → solution → CTA arc
2. Choose presenter — AI avatar (HeyGen) or voiceover + visuals
3. Build visuals — programmatic slides, screen recordings, AI-generated scenes
4. Add captions — always, for accessibility and engagement
5. Export — landscape for YouTube/website, vertical for social

Batch Social Clips

1. Create master template in Hyperframes/Remotion
2. Feed data — product features, testimonials, stats
3. Render batch — one template, many variations
4. Add platform-specific captions via CapCut or Captions.ai
5. Schedule across platforms

---

Agent-Native Video Pipeline

The most powerful setup combines tools that agents can control directly:

Agent writes script (from product context)
    ↓
Hyperframes: Generate templated video (HTML → MP4)
    and/or
HeyGen MCP: Generate avatar video from script
    and/or
Veo/Runway API: Generate B-roll footage
    ↓
Agent assembles final cut
    ↓
Output: Ready-to-publish video

What makes this agent-native:

• Hyperframes uses HTML — any coding agent can generate it
• HeyGen MCP server — agents call it directly
• Video model APIs — standard HTTP requests
• No manual editing step required

---

Common Mistakes

1. Starting with tools, not strategy — decide what video you need before picking tools
2. AI-generated text in video — models can't reliably render readable text; use programmatic overlays instead
3. Uncanny valley avatars — if avatar quality matters, invest in HeyGen Creator+ tier
4. No captions — 85% of social video is watched without sound
5. Wrong aspect ratio — 9:16 for social, 16:9 for YouTube/website, 1:1 for feeds
6. Over-producing — authentic often outperforms polished, especially on TikTok

---

Task-Specific Questions

1. What type of video do you need? (Demo, explainer, social clip, ad, tutorial)
2. Do you need a human presenter or can it be voiceover/text?
3. Is this a one-off or a repeatable template?
4. What platform is it for? (This determines aspect ratio and length)
5. Do you have existing assets to work with? (Screenshots, footage, scripts)
6. What's your budget for video tools?

---

Tool Integrations

Tool	Type	MCP	Guide
HeyGen	AI avatars	Yes	heygen.md
Hyperframes	Programmatic video	-	hyperframes.md
Remotion	Programmatic video	-	remotion.dev
Runway	AI generation	-	runwayml.com/docs

---

Related Skills

• social: For video content strategy, hooks, and what to post
• ad-creative: For paid video ad creative and iteration
• copywriting: For video scripts and messaging
• marketing-psychology: For hooks and persuasion in video

Other files in this skill

• evals/evals.json
• references/ai-video-prompting.md
• references/edit-anatomy.md

references/ai-video-prompting.md (verbatim)

AI Video Prompting Guide

How to write effective prompts for AI video generation models (Veo, Runway, Kling, Pika).

---

Prompt Structure

A strong video prompt follows this formula:

[Subject] + [Action] + [Camera movement] + [Visual style] + [Lighting/mood] + [Technical specs]

Example Prompts by Use Case

Product hero shot:

A sleek laptop on a minimal white desk, screen glowing with a dashboard UI,
camera slowly orbits 180 degrees around the desk,
soft volumetric lighting from the left, shallow depth of field,
cinematic commercial aesthetic, 4K

Lifestyle B-roll:

A woman in a modern co-working space smiling while looking at her phone,
natural window light, candid documentary feel,
camera handheld with subtle movement, warm color grading

Abstract/brand:

Flowing liquid gold particles forming the shape of a network graph,
dark background, particles catch light as they move,
slow-motion macro photography style, dramatic rim lighting

SaaS explainer scene:

An overhead shot of a team around a conference table pointing at charts,
camera slowly pushes in, bright modern office,
clean corporate style, even lighting, 1080p

---

Camera Movement Vocabulary

Use these terms — video models understand them:

Term	Effect
Static	Locked camera, no movement
Pan left/right	Camera rotates horizontally
Tilt up/down	Camera rotates vertically
Dolly in/out	Camera moves toward/away from subject
Orbit	Camera circles around subject
Tracking shot	Camera follows moving subject
Crane/aerial	Camera rises or descends
Handheld	Subtle shake, documentary feel
Zoom	Lens zoom (different from dolly)
Slow push	Gradual dolly in — builds tension/focus

---

Style Keywords

Cinematic

• "cinematic color grading"
• "anamorphic lens flare"
• "shallow depth of field"
• "film grain"
• "35mm film"

Commercial/Corporate

• "clean commercial lighting"
• "bright and airy"
• "professional corporate aesthetic"
• "even, diffused lighting"

Documentary

• "handheld documentary style"
• "natural lighting"
• "candid, unposed"
• "observational camera"

Social/Trendy

• "vertical 9:16"
• "fast-paced cuts"
• "bold text overlays"
• "high contrast, saturated colors"

---

Model-Specific Tips

Veo (Google)

• Excels at photorealism and complex scenes
• Supports audio generation synced to video
• Best with detailed, descriptive prompts
• Specify "high resolution" or "1080p" for best quality
• Can handle multiple subjects and scene transitions

Runway Gen-4

• Strong motion control — specify camera movements precisely
• Best temporal consistency (subjects stay consistent across frames)
• Use motion brush for specific area animation
• Image-to-video works well — provide a reference frame
• Keep prompts under 100 words for best results

Kling

• Can generate up to 2 minutes (much longer than others)
• Good for longer narrative sequences
• More affordable for bulk generation
• Quality drops slightly at longer durations
• Best with simpler scenes and fewer subjects

Pika

• Fastest generation time (under 2 minutes)
• Good for quick iterations and experimentation
• Effects mode adds motion to still images
• Best for short clips (5-15 seconds)
• Less control over camera movement

---

Common Prompt Mistakes

Mistake	Why It Fails	Fix
"A person using our app"	Too vague, no visual detail	Describe the person, setting, lighting, camera
Including text/logos	AI can't render readable text	Add text in post via Hyperframes/CapCut
"Make it viral"	Not a visual instruction	Describe the visual style you want
Extremely long prompts (200+ words)	Models lose focus	Keep to 50-100 words, be specific
No camera direction	Random/static camera	Always specify movement or "static"
"Realistic" alone	Not specific enough	"Photorealistic, natural lighting, shot on RED camera"

---

Prompting Workflow

1. Reference first — find a real video that looks like what you want
2. Describe it — break down: subject, action, camera, style, mood
3. Generate 3-4 variations — same concept, different angles or styles
4. Iterate on the best — refine the prompt based on results
5. Composite — combine AI footage with programmatic text/overlays

---

Aspect Ratios

Always specify in your prompt or generation settings:

Platform	Ratio	Resolution
YouTube	16:9	1920x1080 or 3840x2160
TikTok/Reels/Shorts	9:16	1080x1920
Instagram Feed	1:1 or 4:5	1080x1080 or 1080x1350
Website hero	16:9	1920x1080
LinkedIn	16:9 or 1:1	1920x1080

---

Cost Optimization

• Iterate at low resolution — upscale only the final version
• Use Kling for drafts — cheapest per second, switch to Veo/Runway for finals
• Image-to-video — providing a reference frame saves generation credits and gives better results
• Batch similar prompts — models often offer volume discounts
• Cache and reuse — B-roll clips can be reused across multiple videos

references/edit-anatomy.md (verbatim)

Reverse-Engineering an Edit (The Beat Sheet)

A viral short-form video usually isn't winning on the footage — it's winning on the edit: the cut rhythm, the caption style, the punch-ins, the on-screen text landing on the exact word, the b-roll cutaways, the sound design. This reference turns a reference edit you admire into a reusable edit spec — a beat sheet you (or an editing tool) can execute against your own footage — without copying a single frame of theirs.

This is the tool-agnostic half of "copy any viral edit": the decomposition. The generation is whatever you edit with afterward — CapCut, Premiere, Remotion/Hyperframes, or an AI restyle tool. The spec is the deliverable.

When to use it

• A competitor's or creator's edit keeps stopping your scroll and you want to understand why and replicate the technique
• You have raw footage (a talking-head clip, a demo) and a reference edit whose style you want to match
• You're briefing an editor or a template and need the edit decisions written down, not vibes

Don't use it to copy someone's actual creative — this extracts the editing grammar (structure, rhythm, caption treatment), not the script, footage, or brand. Same rule as mining organic content for vocabulary in the hook system: take the technique, never the creative.

Step 1 — Pull the reference so you can actually read the edit

You cannot decompose an edit from a description of it. Get the frames and the timing:

• watch-video (visual or multimodal mode) — extracts the transcript and samples frames at the cut points, so you can read on-screen text, caption style, and shot changes. This is the primary tool.
• social-fetch — pull the post for the caption, engagement, and the media URL when the reference is a specific tweet/Reel/TikTok.
• Screenshots of key frames also work if the user supplies them — you need the visual, not just the words.

Note the total duration and roughly how many cuts there are before you start — cuts-per-second is the single most telling number about an edit's energy.

Step 2 — Extract the anatomy, beat by beat

Walk the reference from 0:00 and log every editing decision. The dimensions that define a short-form edit:

Dimension	What to read off the reference
Shot & framing	Talking head / screen recording / b-roll / text card; close-up vs. wide; headroom, rule-of-thirds, or dead-center
Cut rhythm	Where each cut lands and how fast (cuts-per-second); is it on the beat, on the word, or on the breath?
On-screen text	The words, when each appears/disappears, and where on the frame (top-third caption vs. big centered statement)
Caption style	Font, weight, color, outline/box, and animation (word-by-word pop, karaoke highlight, whole-line)
Motion	Punch-ins / zoom pushes, shakes, whip-transitions, speed ramps — where and how aggressive
B-roll & overlays	Cutaways, stickers, arrows, emoji, screenshots, meme inserts — what's laid over the base footage and when
Sound design	Music choice and where it hits, SFX (whooshes, dings, risers), and deliberate silence before a beat
Hook (first 2s)	The single most-copied element — what's on screen and said in the opening two seconds, before anyone's committed
Pacing curve	Does it stay frantic, or fast-hook → slower-body → fast-CTA? Map the energy over the runtime

Read the pattern, not just the instances: "a hard cut + punch-in on every new sentence," "caption is one word at a time, yellow, karaoke-highlighted, bottom third," "a whoosh SFX on every scene change." Patterns are what make an edit replicable; a list of 40 individual cuts is not.

Step 3 — Write the beat sheet

Two artifacts: a per-beat table and a short style summary.

The beat sheet — one row per beat (a beat = a cut or a distinct edit event):

| Beat | Time      | Shot            | On-screen text        | Caption style        | Transition / motion   | Audio            |
|------|-----------|-----------------|-----------------------|----------------------|-----------------------|------------------|
| 1    | 0:00–0:02 | CU talking head | "STOP doing this"     | word-pop, yellow, ctr| hard in, slow push    | music in + riser |
| 2    | 0:02–0:04 | screen record   | (caption only)        | karaoke, white, btm  | hard cut + whoosh     | click SFX        |
| …    |           |                 |                       |                      |                       |                  |

The style summary — the 3–5 signature moves that make this edit recognizable, stated so they're reusable:

• e.g. "Every sentence gets a hard cut + a 5% punch-in." / "Captions are one word at a time, bottom-third, karaoke-highlighted." / "A whoosh SFX on every cut; music drops out for 0.5s before the CTA." / "The hook is a bold centered statement on frame 1, no logo."

The signature moves are the real deliverable — someone can apply those five rules to any footage and get the style. The table is the detailed backup.

Step 4 — Review once, then execute

Show the beat sheet before anyone edits anything — the same review-once gate as the ad-creative creative review page. The reviewer checks two things:

• The on-screen text says what you want (mapped to your message, not the reference's)
• The scene changes land where you want them (your footage's beats, not a blind copy of the reference's timing)

Approve, then execute the spec with your footage:

• Remotion / Hyperframes — when you want the edit templated and data-driven (see the programmatic-video section in SKILL.md); the beat sheet is the composition spec.
• CapCut / Premiere / an editor — hand off the beat sheet + style summary as the brief.
• An AI restyle tool — feed the style summary as the target style.

Originality guardrail

You are copying the edit, not the content. The beat sheet describes technique (cut rhythm, caption treatment, motion, sound design) applied to your footage and your message. General editing techniques and style cues are usually reusable — U.S. copyright protects expression, not procedures or methods (17 U.S.C. §102(b)) — but the reference's specific creative expression is not, and closely reproducing a finished video's exact selection and arrangement of choices can still create risk. So copy the grammar, not the finished work: use your own footage, message, script, voiceover, licensed music/SFX/samples, and brand elements. If the reference's "style" is really a specific bit or sketch, that's their creative — draw inspiration, don't reproduce it.

Common mistakes

• Describing instead of reading — you can't extract caption style or cut timing from the transcript alone; pull the frames (watch-video).
• Logging instances, not patterns — 40 cut timestamps isn't a spec; "hard cut + punch-in per sentence" is.
• Copying the reference's timing onto different footage — beats land on your words and your cuts; the reference gives you the grammar, not the calendar.
• Skipping the hook — the first 2 seconds carry most of the retention; decode them in the most detail.
• Reproducing the creative — matching the edit is fine; re-shooting their exact bit, script, or using their footage/music/SFX is not.

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