/openclaw-video-toolkit

name: video_toolkit
description: Create professional videos autonomously using claude-code-video-toolkit — AI voiceovers, image generation, music, talking heads, and Remotion rendering.
metadata:
  openclaw:
    emoji: "🎬"
    skillKey: "video-toolkit"
    os: ["darwin", "linux"]
    requires:
      bins: ["node", "python3", "ffmpeg", "npm"]

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.**

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.

CRITICAL: Progress Reporting

**ALWAYS add `--progress json` to every cloud GPU tool command.** This gives you structured JSON Lines on stderr so you can monitor job status, detect stuck jobs, and report progress to the user in real-time.

# CORRECT — always include --progress json
python3 tools/music_gen.py --preset corporate-bg --duration 60 --output bg.mp3 --progress json

# WRONG — no visibility into job status
python3 tools/music_gen.py --preset corporate-bg --duration 60 --output bg.mp3

Tools that support `--progress json`: `music_gen.py`, `qwen3_tts.py`, `flux2.py`, `upscale.py`, `sadtalker.py`, `image_edit.py`, `dewatermark.py`, `ltx2.py`, `chain_video.py`.

See the **Progress Reporting** section below for output format and stage definitions.

CRITICAL: Long-Running Tasks — Use yieldMs, Not background:true

**Any tool command that takes more than 30 seconds MUST use `exec` with `yieldMs` so you can report progress to the user live.** This includes: batch FLUX generation, chain_video, SadTalker, music generation, and any multi-scene pipeline.

exec command:"cd ~/.openclaw/workspace/claude-code-video-toolkit && python3 tools/chain_video.py --output-dir /path/ --progress json ..." yieldMs:10000

**The polling loop:** 1. `exec` with `yieldMs:10000` starts the command and returns control to you every 10 seconds 2. Read the `--progress json` output — look for `"stage":"item"` (scene complete) or `"stage":"complete"` (all done) 3. Report progress to the user ("Scene 05/30 complete, 17%") 4. Poll again: `process action:poll sessionId:<id>` 5. Repeat until `"stage":"complete"`

**Why:** Your agent run ends when you finish responding. If you use `bash background:true`, you lose the ability to report progress — the user sees silence until they nudge you. With `yieldMs`, you stay in the loop.

**NEVER do this:**

• `bash background:true command:"long running thing"` then promise to "monitor" — you can't, your run ends
• Break a batch into individual tool calls across separate messages — your run ends between each one
• Promise to "continue autonomously" — you literally cannot without an external trigger

Setup

Step 1: Check Current State

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

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:

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:

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](https://huggingface.co/google/gemma-3-12b-it-qat-q4_0-unquantized):

modal secret create huggingface-token HF_TOKEN=hf_your_read_access_token

Add each URL to `.env`:

ACEMUSIC_API_KEY=...                          # Free key from acemusic.ai/api-key (best music quality)
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

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:

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 comp