Video Toolkit

Create professional explainer videos from a text brief. The toolkit uses open-source AI models on cloud GPUs (Modal or RunPod) for voiceover, image generation, music, and talking head animation. Remotion (React) handles composition and rendering.

CRITICAL: Toolkit Path

The toolkit lives at a fixed path. ALWAYS
cd
here before running any tool command.

bash

TOOLKIT=~/.openclaw/workspace/claude-code-video-toolkit
cd $TOOLKIT

NEVER run tool commands from inside a project directory. Tools resolve paths relative to the toolkit root.

Setup

Step 1: Check Current State

bash

cd ~/.openclaw/workspace/claude-code-video-toolkit
python3 tools/verify_setup.py

If everything shows

[x]

, skip to "Quick Test" below. Otherwise continue setup.

Step 2: Install Python Dependencies

bash

cd ~/.openclaw/workspace/claude-code-video-toolkit
pip3 install --break-system-packages -r tools/requirements.txt

Note:

--break-system-packages

is needed on Debian/Ubuntu with managed Python (PEP 668). Safe inside containers.

Step 3: Configure Cloud GPU Endpoints

The toolkit needs cloud GPU endpoint URLs in

.env

. Check if

.env

exists and has Modal endpoints:

bash

cat ~/.openclaw/workspace/claude-code-video-toolkit/.env | grep MODAL

If Modal endpoints are configured, you're ready. If not, ask the user to provide Modal endpoint URLs or set up Modal:

bash

pip3 install --break-system-packages modal
python3 -m modal setup   # Opens browser for authentication

# Deploy each tool — capture the endpoint URL from output
cd ~/.openclaw/workspace/claude-code-video-toolkit
modal deploy docker/modal-qwen3-tts/app.py
modal deploy docker/modal-flux2/app.py
modal deploy docker/modal-music-gen/app.py
modal deploy docker/modal-sadtalker/app.py
modal deploy docker/modal-image-edit/app.py
modal deploy docker/modal-upscale/app.py
modal deploy docker/modal-propainter/app.py
modal deploy docker/modal-ltx2/app.py      # Requires: modal secret create huggingface-token HF_TOKEN=hf_...

LTX-2 prerequisite: Before deploying LTX-2, create a HuggingFace secret and accept the Gemma 3 license:

bash

modal secret create huggingface-token HF_TOKEN=hf_your_read_access_token

Add each URL to

.env

MODAL_QWEN3_TTS_ENDPOINT_URL=https://...modal.run
MODAL_FLUX2_ENDPOINT_URL=https://...modal.run
MODAL_MUSIC_GEN_ENDPOINT_URL=https://...modal.run
MODAL_SADTALKER_ENDPOINT_URL=https://...modal.run
MODAL_IMAGE_EDIT_ENDPOINT_URL=https://...modal.run
MODAL_UPSCALE_ENDPOINT_URL=https://...modal.run
MODAL_DEWATERMARK_ENDPOINT_URL=https://...modal.run
MODAL_LTX2_ENDPOINT_URL=https://...modal.run

Optional but recommended — Cloudflare R2 for reliable file transfer:

R2_ACCOUNT_ID=...
R2_ACCESS_KEY_ID=...
R2_SECRET_ACCESS_KEY=...
R2_BUCKET_NAME=video-toolkit

Step 4: Verify and Quick Test

bash

cd ~/.openclaw/workspace/claude-code-video-toolkit
python3 tools/verify_setup.py

All tools should show

[x]

. Then run a quick test to confirm the GPU pipeline works:

bash

cd ~/.openclaw/workspace/claude-code-video-toolkit
python3 tools/qwen3_tts.py --text "Hello, this is a test." --speaker Ryan --tone warm --output /tmp/video-toolkit-test.mp3 --cloud modal

If you get a valid .mp3 file, setup is complete. If it fails, check:

```
.env
```
has the correct
```
MODAL_QWEN3_TTS_ENDPOINT_URL
```

Run

python3 tools/verify_setup.py --json

and check

modal_tools

for which endpoints are missing

Cost: Modal includes $30/month free compute. A typical 60s video costs $1-3.

Creating a Video

Step 1: Create Project

bash

cd ~/.openclaw/workspace/claude-code-video-toolkit
cp -r templates/product-demo projects/PROJECT_NAME
cd projects/PROJECT_NAME
npm install

Templates:

product-demo

(marketing/explainer),

sprint-review

sprint-review-v2

(composable scenes).

Step 2: Write Config

Edit

projects/PROJECT_NAME/src/config/demo-config.ts

typescript

export const demoConfig: ProductDemoConfig = {
  product: {
    name: 'My Product',
    tagline: 'What it does in one line',
    website: 'example.com',
  },
  scenes: [
    { type: 'title', durationSeconds: 9, content: { headline: '...', subheadline: '...' } },
    { type: 'problem', durationSeconds: 14, content: { headline: '...', problems: ['...', '...'] } },
    { type: 'solution', durationSeconds: 13, content: { headline: '...', highlights: ['...', '...'] } },
    { type: 'stats', durationSeconds: 12, content: { stats: [{value: '99%', label: '...'}, ...] } },
    { type: 'cta', durationSeconds: 10, content: { headline: '...', links: ['...'] } },
  ],
  audio: {
    backgroundMusicFile: 'audio/bg-music.mp3',
    backgroundMusicVolume: 0.12,
  },
};

Scene types:

title

problem

solution

demo

feature

stats

cta

Duration rule: Estimate

durationSeconds

ceil(word_count / 2.5) + 2

. You will adjust this after generating audio in Step 4.

Step 3: Write Voiceover Script

Create

projects/PROJECT_NAME/VOICEOVER-SCRIPT.md

markdown

## Scene 1: Title (9s, ~17 words)
Build videos with AI. The product name toolkit makes it easy.

## Scene 2: Problem (14s, ~30 words)
The problem statement goes here. Keep it punchy and relatable.

Word budget per scene:

(durationSeconds - 2) * 2.5

words. The -2 accounts for 1s audio delay + 1s padding.

Step 4: Generate Assets

CRITICAL: All commands below MUST be run from the toolkit root, not the project directory.

bash

cd ~/.openclaw/workspace/claude-code-video-toolkit

4a. Background Music

bash

cd ~/.openclaw/workspace/claude-code-video-toolkit
python3 tools/music_gen.py \
  --preset corporate-bg \
  --duration 90 \
  --output projects/PROJECT_NAME/public/audio/bg-music.mp3 \
  --cloud modal

Presets:

corporate-bg

upbeat-tech

ambient

dramatic

tension

hopeful

cta

lofi

4b. Voiceover (per-scene)

Generate ONE .mp3 file PER SCENE. Do NOT generate a single voiceover file.

bash

cd ~/.openclaw/workspace/claude-code-video-toolkit

# Scene 01
python3 tools/qwen3_tts.py \
  --text "The voiceover text for scene one." \
  --speaker Ryan --tone warm \
  --output projects/PROJECT_NAME/public/audio/scenes/01.mp3 \
  --cloud modal

# Scene 02
python3 tools/qwen3_tts.py \
  --text "The voiceover text for scene two." \
  --speaker Ryan --tone warm \
  --output projects/PROJECT_NAME/public/audio/scenes/02.mp3 \
  --cloud modal

# ... repeat for each scene

Speakers:

Ryan

Aiden

Vivian

Serena

Uncle_Fu

Dylan

Eric

Ono_Anna

Sohee

Tones:

neutral

warm

professional

excited

calm

serious

storyteller

tutorial

For voice cloning (needs a reference recording):

bash

cd ~/.openclaw/workspace/claude-code-video-toolkit
python3 tools/qwen3_tts.py \
  --text "Text to speak" \
  --ref-audio assets/voices/reference.m4a \
  --ref-text "Exact transcript of the reference audio" \
  --output projects/PROJECT_NAME/public/audio/scenes/01.mp3 \
  --cloud modal

4c. Scene Images

bash

cd ~/.openclaw/workspace/claude-code-video-toolkit
python3 tools/flux2.py \
  --prompt "Dark tech background with blue geometric grid, cinematic lighting" \
  --width 1920 --height 1080 \
  --output projects/PROJECT_NAME/public/images/title-bg.png \
  --cloud modal

Image presets (use

--preset

instead of

--prompt --width --height

title-bg

problem

solution

demo-bg

stats-bg

cta

thumbnail

portrait-bg

bash

cd ~/.openclaw/workspace/claude-code-video-toolkit
python3 tools/flux2.py \
  --preset title-bg \
  --output projects/PROJECT_NAME/public/images/title-bg.png \
  --cloud modal

4d. Video Clips — B-Roll & Animated Backgrounds (optional)

Generate AI video clips for b-roll cutaways, animated slide backgrounds, or intro/outro sequences:

bash

cd ~/.openclaw/workspace/claude-code-video-toolkit

# B-roll clip from text
python3 tools/ltx2.py \
  --prompt "Aerial drone shot over a European city at golden hour, cinematic wide angle" \
  --output projects/PROJECT_NAME/public/videos/broll-europe.mp4 \
  --cloud modal

# Animate a slide/screenshot (image-to-video)
python3 tools/ltx2.py \
  --prompt "Gentle particle effects, soft ambient light shifts, very slight camera drift" \
  --input projects/PROJECT_NAME/public/images/title-bg.png \
  --output projects/PROJECT_NAME/public/videos/animated-title.mp4 \
  --cloud modal

# Abstract intro/outro background
python3 tools/ltx2.py \
  --prompt "Dark moody abstract background with flowing blue light streaks, bokeh particles, cinematic" \
  --output projects/PROJECT_NAME/public/videos/intro-bg.mp4 \
  --cloud modal

Use in Remotion compositions with

<OffthreadVideo>

tsx

<OffthreadVideo src={staticFile('videos/broll-europe.mp4')} />

LTX-2 rules:

Max ~8 seconds per clip (193 frames at 24fps). Default is ~5s (121 frames).
Width/height must be divisible by 64. Default: 768x512.
~$0.20-0.25 per clip, ~2.5 min generation time.
Cold start ~60-90s. Subsequent clips on warm GPU are faster.
Generated audio is ambient only — use voiceover/music tools for speech and music.
~30% of generations may have training data artifacts (logos/text). Re-run with
```
--seed
```
to vary.

4e. Talking Head Narrator (optional)

Generate a presenter portrait, then animate per-scene clips:

bash

cd ~/.openclaw/workspace/claude-code-video-toolkit

# 1. Generate portrait
python3 tools/flux2.py \
  --prompt "Professional presenter portrait, clean style, dark background, facing camera, upper body" \
  --width 1024 --height 576 \
  --output projects/PROJECT_NAME/public/images/presenter.png \
  --cloud modal

# 2. Generate per-scene narrator clips (one per scene, NOT one long video)
python3 tools/sadtalker.py \
  --image projects/PROJECT_NAME/public/images/presenter.png \
  --audio projects/PROJECT_NAME/public/audio/scenes/01.mp3 \
  --preprocess full --still --expression-scale 0.8 \
  --output projects/PROJECT_NAME/public/narrator-01.mp4 \
  --cloud modal

# Repeat for each scene that needs a narrator

SadTalker rules — follow these exactly:

ALWAYS use
```
--preprocess full
```
(default
```
crop
```
outputs a square, wrong aspect ratio)
ALWAYS use
```
--still
```
(reduces head movement, looks professional)
ALWAYS generate per-scene clips (6-15s each), NEVER one long video
Processing: ~3-4 min per 10s of audio on Modal A10G
```
--expression-scale 0.8
```
keeps expressions subtle (range 0.0-1.5)

4e. Image Editing (optional)

Create scene variants from existing images:

bash

cd ~/.openclaw/workspace/claude-code-video-toolkit
python3 tools/image_edit.py \
  --input projects/PROJECT_NAME/public/images/title-bg.png \
  --prompt "Make it darker with red tones, more ominous" \
  --output projects/PROJECT_NAME/public/images/problem-bg.png \
  --cloud modal

4f. Upscaling (optional)

bash

cd ~/.openclaw/workspace/claude-code-video-toolkit
python3 tools/upscale.py \
  --input projects/PROJECT_NAME/public/images/some-image.png \
  --output projects/PROJECT_NAME/public/images/some-image-4x.png \
  --scale 4 --cloud modal

Step 5: Sync Timing

ALWAYS do this after generating voiceover. Audio duration differs from estimates.

bash

cd ~/.openclaw/workspace/claude-code-video-toolkit
for f in projects/PROJECT_NAME/public/audio/scenes/*.mp3; do
  echo "$(basename $f): $(ffprobe -v error -show_entries format=duration -of csv=p=0 "$f")s"
done

Update each scene's

durationSeconds

demo-config.ts

to:

ceil(actual_audio_duration + 2)

Example: if

01.mp3

is 6.8s, set scene 1

durationSeconds

(ceil(6.8 + 2) = 9).

Step 6: Review Still Frames

bash

cd ~/.openclaw/workspace/claude-code-video-toolkit/projects/PROJECT_NAME
npx remotion still src/index.ts ProductDemo --frame=100 --output=/tmp/review-scene1.png
npx remotion still src/index.ts ProductDemo --frame=400 --output=/tmp/review-scene2.png

Check: text truncation, animation timing, narrator PiP positioning, background contrast.

Step 7: Render

bash

cd ~/.openclaw/workspace/claude-code-video-toolkit/projects/PROJECT_NAME
npm run render

Output:

out/ProductDemo.mp4

Composition Patterns

Per-Scene Audio

Use per-scene audio with a 1-second delay (

from={30}

= 30 frames = 1s at 30fps):

tsx

<Sequence from={30}>
  <Audio src={staticFile('audio/scenes/01.mp3')} volume={1} />
</Sequence>

Per-Scene Narrator PiP

tsx

<Sequence from={30}>
  <OffthreadVideo
    src={staticFile('narrator-01.mp4')}
    style={{ width: 320, height: 180, objectFit: 'cover' }}
    muted
  />
</Sequence>

ALWAYS use
<OffthreadVideo>
, NEVER
<video>
. Remotion requires its own component for frame-accurate rendering.

Transitions

tsx

import { TransitionSeries, linearTiming } from '@remotion/transitions';
import { fade } from '@remotion/transitions/fade';
import { glitch } from '../../../lib/transitions/presentations/glitch';
import { lightLeak } from '../../../lib/transitions/presentations/light-leak';

NEVER import from
lib/transitions
barrel — import custom transitions from

lib/transitions/presentations/

directly.

Error Recovery

Problem	Solution
Tool command fails with "No module named..."	Run `pip3 install --break-system-packages -r tools/requirements.txt` from toolkit root
"MODAL_*_ENDPOINT_URL not configured"	Check `.env` has the endpoint URL. Run `python3 tools/verify_setup.py`
SadTalker output is square/cropped	You forgot `--preprocess full` . Re-run with that flag
Audio too short/long for scene	Re-run Step 5 (sync timing) and update config
`npm run render` fails	Make sure you're in the project dir, not toolkit root. Run `npm install` first
"Cannot find module" in Remotion	Check import paths. Custom components use `../../../lib/` relative paths
Cold start timeout on Modal	First call after idle takes 30-120s. Retry once — second call uses warm GPU
SadTalker client timeout (long audio)	The client HTTP request can time out before Modal finishes. Modal still uploads the result to R2. Check `sadtalker/results/` in the `video-toolkit` R2 bucket for the output. Use `python3 -c "import boto3; ..."` with the R2 creds from `.env` to list and generate a presigned URL

Cost Estimates (Modal)

Tool	Typical Cost	Notes
Qwen3-TTS	~$0.01/scene	~20s per scene on warm GPU
FLUX.2	~$0.01/image	~3s warm, ~30s cold
ACE-Step	~$0.02-0.05	Depends on duration
SadTalker	~$0.05-0.20/scene	~3-4 min per 10s audio
Qwen-Edit	~$0.03-0.15	~8 min cold start (25GB model)
RealESRGAN	~$0.005/image	Very fast
LTX-2.3	~$0.20-0.25/clip	~2.5 min per 5s clip, A100-80GB

Total for a 60s video: ~$1-3 depending on scenes and narrator clips.

Modal Starter plan: $30/month free compute. Apps scale to zero when idle.

video_toolkit

NPX Install

Tags

SKILL.md Content

Video Toolkit

CRITICAL: Toolkit Path

Setup

Step 1: Check Current State

Step 2: Install Python Dependencies

Step 3: Configure Cloud GPU Endpoints

Step 4: Verify and Quick Test

Creating a Video

Step 1: Create Project

Step 2: Write Config

Step 3: Write Voiceover Script

Step 4: Generate Assets

4a. Background Music

4b. Voiceover (per-scene)

4c. Scene Images

4d. Video Clips — B-Roll & Animated Backgrounds (optional)

4e. Talking Head Narrator (optional)

4e. Image Editing (optional)

4f. Upscaling (optional)

Step 5: Sync Timing

Step 6: Review Still Frames

Step 7: Render

Composition Patterns

Per-Scene Audio

Per-Scene Narrator PiP

Transitions

Error Recovery

Cost Estimates (Modal)