/ltx2
AI video generation with LTX-2.3 22B — text-to-video, image-to-video clips for video production. Use when generating video clips, animating images, creating b-roll, animated backgrounds, or motion content. Triggers include video generation, animate image, b-roll, motion, video
$ npx -y skills add digitalsamba/claude-code-video-toolkit --skill ltx2 --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
- Slash command
/ltx2
Context preview
The summary Claude sees to decide when to auto-load this skill.
AI video generation with LTX-2.3 22B — text-to-video, image-to-video clips for video production. Use when generating video clips, animating images, creating b-roll, animated backgrounds, or motion content. Triggers include video generation, animate image, b-roll, motion, video
SKILL.md
ltx2.SKILL.mdname: ltx2
description: AI video generation with LTX-2.3 22B — text-to-video, image-to-video clips for video production. Use when generating video clips, animating images, creating b-roll, animated backgrounds, or motion content. Triggers include video generation, animate image, b-roll, motion, video clip, text-to-video, image-to-video.
LTX-2.3 Video Generation
Generate ~5 second video clips from text prompts or images using the LTX-2.3 22B DiT model. Runs on Modal (A100-80GB). Requires `MODAL_LTX2_ENDPOINT_URL` in `.env`.
Quick Reference
# Text-to-video
python3 tools/ltx2.py --prompt "A sunset over the ocean, golden light on waves, cinematic" --output sunset.mp4
# Image-to-video (animate a still image)
python3 tools/ltx2.py --prompt "Gentle camera drift, soft ambient motion" --input photo.jpg --output animated.mp4
# Custom resolution and duration
python3 tools/ltx2.py --prompt "..." --width 1024 --height 576 --num-frames 161 --output wide.mp4
# Fast mode (fewer steps, quicker)
python3 tools/ltx2.py --prompt "..." --quality fast --output quick.mp4
# Reproducible output
python3 tools/ltx2.py --prompt "..." --seed 42 --output reproducible.mp4
Parameters
| Parameter | Default | Description | |-----------|---------|-------------| | `--prompt` | (required) | Text description of the video | | `--input` | - | Input image for image-to-video | | `--width` | 768 | Video width (divisible by 64) | | `--height` | 512 | Video height (divisible by 64) | | `--num-frames` | 121 | Frame count, must satisfy `(n-1) % 8 == 0` | | `--fps` | 24 | Frames per second | | `--quality` | standard | `standard` (30 steps) or `fast` (15 steps) | | `--steps` | 30 | Override inference steps directly | | `--seed` | random | Seed for reproducibility | | `--output` | auto | Output file path | | `--negative-prompt` | sensible default | What to avoid | | `--lora` | none | Style LoRA preset. Currently: `crt-terminal`. |
Style LoRAs
Style LoRAs bias the output toward a specific visual aesthetic. They're baked into the Modal image and selected per-request; switching LoRAs forces a pipeline rebuild (~60s one-time cost per container lifetime per switch).
`crt-terminal` — CRT / pixel-art terminals
Base: LTX-2.3 22B, trained by [@lovis93](https://huggingface.co/lovis93/crt-animation-terminal-ltx-2.3-lora) (Apache 2.0).
# Trigger word is auto-prepended — write the prompt normally
python3 tools/ltx2.py --lora crt-terminal \
--prompt "a terminal typing out \"\\$ claude --continue\" character by character in glowing green pixel font, scanlines, phosphor glow, low choppy frame rate, hacker mood" \
--output crt_claude.mp4
**What the preset changes:**
- Prepends `crtanim,` to the prompt (the LoRA's trigger word)
- Defaults to 1024×1024, 121 frames (the ratio it was trained on)
- Relaxes the default negative prompt so on-screen text isn't filtered out
**Prompt pattern:** `<CRT aesthetic> → <color palette> → <animation style> → <subject> → <literal text in quotes> → <mood>`. Keep on-screen text to 1–3 words — the model can't render long strings reliably. The LoRA prefers static framing; ask for camera moves explicitly if you want them.
Valid Frame Counts
`(n - 1) % 8 == 0`: 25 (~1s), 49 (~2s), 73 (~3s), 97 (~4s), **121 (~5s default)**, 161 (~6.7s), 193 (~8s max practical).
Common Resolutions
| Resolution | Ratio | Notes | |------------|-------|-------| | 768x512 | 3:2 | Default, good balance | | 512x512 | 1:1 | Square, fastest | | 1024x576 | 16:9 | Widescreen | | 576x1024 | 9:16 | Portrait/vertical |
Prompting Guide
LTX-2 responds well to cinematographic descriptions. Layer these dimensions:
- **Camera:** "Slow dolly forward", "Aerial drone shot", "Tracking shot", "Static wide angle"
- **Lighting:** "Golden hour", "Cinematic lighting", "Neon-lit", "Soft diffused light"
- **Motion:** "Timelapse of...", "Slow motion", "Gentle camera drift", "Gradually transitions"
- **Style:** "Shot on 35mm film", "Documentary style", "Clean minimal aesthetic"
- **Negative:** Always implicitly avoids "worst quality, blurry, jittery, watermark, text, logo"
Keep prompts under 200 words. Be specific about the scene.
Good Prompts
# Atmospheric b-roll
"Aerial drone shot slowly flying over turquoise ocean waves breaking on white sand, golden hour sunlight, cinematic"
# Product/tech scene
"Close-up of hands typing on a mechanical keyboard, shallow depth of field, soft desk lamp lighting, cozy atmosphere"
# Abstract background
"Dark moody abstract background with flowing blue light streaks, subtle geometric grid, bokeh particles floating, cinematic tech atmosphere"
# Animate a portrait
"Professional headshot, subtle natural head movement, confident warm expression, studio lighting, shallow depth of field"
# Animate a slide/screenshot
"Gentle subtle particle effects floating across a presentation slide, soft ambient light shifts, very slight camera drift"
Bad Prompts
# Too vague
"A cool video"
# Too many competing ideas
"A cat riding a skateboard while juggling fire on the moon during a thunderstorm"
# Describing text/UI (model can't render text reliably)
"A website showing the text 'Welcome to our platform'"
Video Production Use Cases
B-Roll Clips
Generate atmospheric 5s shots for cutaways between narrated scenes:
python3 tools/ltx2.py --prompt "Futuristic holographic interface, glowing data visualizations, clean workspace, cinematic" --output broll_tech.mp4
python3 tools/ltx2.py --prompt "Aerial view of European city at golden hour, modern architecture" --output broll_europe.mp4
Animated Slide Backgrounds
Feed a slide screenshot and add subtle motion:
python3 tools/ltx2.py --prompt "Gentle particle effects, soft ambient light shifts, very slight camera drift" --input slide.png --output animated_slide.mp4
Animated Portraits
Bring still headshots to life:
python3 tools/ltx2.py --prompt "Subtle natural
Read more
name: ltx2 description: AI video generation with LTX-2.3 22B — text-to-video, image-to-video clips for video production. Use when generating video clips, animating images, creating b-roll, animated backgrounds, or motion content. Triggers include video generation, animate image, b-roll, motion, video clip, text-to-video, image-to-video.
LTX-2.3 Video Generation
Generate ~5 second video clips from text prompts or images using the LTX-2.3 22B DiT model. Runs on Modal (A100-80GB). Requires `MODAL_LTX2_ENDPOINT_URL` in `.env`.
Quick Reference
# Text-to-video python3 tools/ltx2.py --prompt "A sunset over the ocean, golden light on waves, cinematic" --output sunset.mp4 # Image-to-video (animate a still image) python3 tools/ltx2.py --prompt "Gentle camera drift, soft ambient motion" --input photo.jpg --output animated.mp4 # Custom resolution and duration python3 tools/ltx2.py --prompt "..." --width 1024 --height 576 --num-frames 161 --output wide.mp4 # Fast mode (fewer steps, quicker) python3 tools/ltx2.py --prompt "..." --quality fast --output quick.mp4 # Reproducible output python3 tools/ltx2.py --prompt "..." --seed 42 --output reproducible.mp4
Parameters
| Parameter | Default | Description | |-----------|---------|-------------| | `--prompt` | (required) | Text description of the video | | `--input` | - | Input image for image-to-video | | `--width` | 768 | Video width (divisible by 64) | | `--height` | 512 | Video height (divisible by 64) | | `--num-frames` | 121 | Frame count, must satisfy `(n-1) % 8 == 0` | | `--fps` | 24 | Frames per second | | `--quality` | standard | `standard` (30 steps) or `fast` (15 steps) | | `--steps` | 30 | Override inference steps directly | | `--seed` | random | Seed for reproducibility | | `--output` | auto | Output file path | | `--negative-prompt` | sensible default | What to avoid | | `--lora` | none | Style LoRA preset. Currently: `crt-terminal`. |
Style LoRAs
Style LoRAs bias the output toward a specific visual aesthetic. They're baked into the Modal image and selected per-request; switching LoRAs forces a pipeline rebuild (~60s one-time cost per container lifetime per switch).
`crt-terminal` — CRT / pixel-art terminals
Base: LTX-2.3 22B, trained by [@lovis93](https://huggingface.co/lovis93/crt-animation-terminal-ltx-2.3-lora) (Apache 2.0).
# Trigger word is auto-prepended — write the prompt normally python3 tools/ltx2.py --lora crt-terminal \ --prompt "a terminal typing out \"\\$ claude --continue\" character by character in glowing green pixel font, scanlines, phosphor glow, low choppy frame rate, hacker mood" \ --output crt_claude.mp4
**What the preset changes:**
- Prepends `crtanim,` to the prompt (the LoRA's trigger word)
- Defaults to 1024×1024, 121 frames (the ratio it was trained on)
- Relaxes the default negative prompt so on-screen text isn't filtered out
**Prompt pattern:** `<CRT aesthetic> → <color palette> → <animation style> → <subject> → <literal text in quotes> → <mood>`. Keep on-screen text to 1–3 words — the model can't render long strings reliably. The LoRA prefers static framing; ask for camera moves explicitly if you want them.
Valid Frame Counts
`(n - 1) % 8 == 0`: 25 (~1s), 49 (~2s), 73 (~3s), 97 (~4s), **121 (~5s default)**, 161 (~6.7s), 193 (~8s max practical).
Common Resolutions
| Resolution | Ratio | Notes | |------------|-------|-------| | 768x512 | 3:2 | Default, good balance | | 512x512 | 1:1 | Square, fastest | | 1024x576 | 16:9 | Widescreen | | 576x1024 | 9:16 | Portrait/vertical |
Prompting Guide
LTX-2 responds well to cinematographic descriptions. Layer these dimensions:
- **Camera:** "Slow dolly forward", "Aerial drone shot", "Tracking shot", "Static wide angle"
- **Lighting:** "Golden hour", "Cinematic lighting", "Neon-lit", "Soft diffused light"
- **Motion:** "Timelapse of...", "Slow motion", "Gentle camera drift", "Gradually transitions"
- **Style:** "Shot on 35mm film", "Documentary style", "Clean minimal aesthetic"
- **Negative:** Always implicitly avoids "worst quality, blurry, jittery, watermark, text, logo"
Keep prompts under 200 words. Be specific about the scene.
Good Prompts
# Atmospheric b-roll "Aerial drone shot slowly flying over turquoise ocean waves breaking on white sand, golden hour sunlight, cinematic" # Product/tech scene "Close-up of hands typing on a mechanical keyboard, shallow depth of field, soft desk lamp lighting, cozy atmosphere" # Abstract background "Dark moody abstract background with flowing blue light streaks, subtle geometric grid, bokeh particles floating, cinematic tech atmosphere" # Animate a portrait "Professional headshot, subtle natural head movement, confident warm expression, studio lighting, shallow depth of field" # Animate a slide/screenshot "Gentle subtle particle effects floating across a presentation slide, soft ambient light shifts, very slight camera drift"
Bad Prompts
# Too vague "A cool video" # Too many competing ideas "A cat riding a skateboard while juggling fire on the moon during a thunderstorm" # Describing text/UI (model can't render text reliably) "A website showing the text 'Welcome to our platform'"
Video Production Use Cases
B-Roll Clips
Generate atmospheric 5s shots for cutaways between narrated scenes:
python3 tools/ltx2.py --prompt "Futuristic holographic interface, glowing data visualizations, clean workspace, cinematic" --output broll_tech.mp4 python3 tools/ltx2.py --prompt "Aerial view of European city at golden hour, modern architecture" --output broll_europe.mp4
Animated Slide Backgrounds
Feed a slide screenshot and add subtle motion:
python3 tools/ltx2.py --prompt "Gentle particle effects, soft ambient light shifts, very slight camera drift" --input slide.png --output animated_slide.mp4
Animated Portraits
Bring still headshots to life:
python3 tools/ltx2.py --prompt "Subtle natural
Tell Claude Code what video you want — it writes the script, generates the voiceover, music, and visuals, and renders the MP4.
Repo: digitalsamba/claude-code-video-toolkit
Other skills on claude-code-video-toolkit.
- /acestep
AI music generation with ACE-Step 1.5 — background music, vocal tracks, covers, stem extraction, audio repainting, and continuation for video production. Use when generating music, soundtracks, jingles, or working with audio stems. Triggers include background music, soundtrack,
Open skill - /elevenlabs
Generate AI voiceovers, sound effects, and music using ElevenLabs APIs. Use when creating audio content for videos, podcasts, or games. Triggers include generating voiceovers, narration, dialogue, sound effects from descriptions, background music, soundtrack generation, voice
Open skill - /ffmpeg
Video and audio processing with FFmpeg. Use for format conversion, resizing, compression, audio extraction, and preparing assets for Remotion. Triggers include converting GIF to MP4, resizing video, extracting audio, compressing files, or any media transformation task.
Open skill - /frontend-design
Create distinctive, production-grade frontend interfaces with high design quality. Use this skill when the user asks to build web components, pages, or applications. Generates creative, polished code that avoids generic AI aesthetics.
Open skill - /ideogram4
Prompting patterns for Ideogram 4 text-to-image — best-in-class in-image text rendering and exact color/layout control via structured JSON captions. Use when generating images that need legible on-image text (title cards, thumbnails, logos, signage, CTAs), precise brand colors,
Open skill - /moviepy
Python video composition with moviepy 2.x — overlaying deterministic text on AI-generated video (LTX-2, SadTalker), compositing clips, single-file build.py video projects. Use when adding labels/captions/lower-thirds to LTX-2 or SadTalker outputs, building short ad-style spots
Open skill

