mv-pipeline

What This Skill Does

The mv-pipeline skill is a robust, end-to-end automation engine designed to turn creative concepts into professional-grade music videos. It orchestrates a complex workflow that spans songwriting, AI-based audio generation, cinematic visual synthesis, technical lyrics alignment, and high-fidelity video editing. By integrating browser automation for platforms like Suno with specialized tools like Veo 3.1 and Remotion, the skill drastically reduces the overhead of producing high-quality multimedia content. It streamlines the lifecycle of an MV into six distinct stages: conceptualization, composition, visual creation, timing synchronization, assembly, and publishing, allowing users to handle the process either as a single cohesive flow or as modular, independent tasks.

Installation

You can install the skill by running the following command in your terminal: clawhub install openclaw/skills/skills/koatora20/mv-pipeline Ensure you have the required Python virtual environment and dependencies (such as Remotion and stable-ts) configured in your workspace as specified in the repository documentation.

Use Cases

• Independent Artists: Rapidly generate concept music videos for new tracks without needing a dedicated video production team.
• Content Creators: Automate the production of lyrical videos or visualizers for social media platforms to maintain a consistent output schedule.
• Experimental Filmmakers: Utilize the automated scoring system to handle batch generation of background clips for complex, high-energy editing styles like hyper-pop or trap music videos.
• Enterprise Marketing: Create branded audio-visual assets by utilizing custom prompts for visual consistency alongside generated music.

Example Prompts

1. "Initialize a new project with the mv-pipeline for a Cyberpunk Trap song about a dystopian city; start by generating the songwriting concept."
2. "I have the audio in project_dir/audio/. Can you run the lyrics alignment using stable-ts and prepare the timeline for Remotion?"
3. "Run the mass video generation pipeline using the current scene list in scene_list.yaml, then perform quality scoring and remove all clips with a flicker score over 0.5."

Tips & Limitations

• DOM Dependency: Because this skill relies on browser automation for Suno, layout changes on the target website may require you to refresh your snapshot-based references. Always perform a fresh snapshot if automation fails.
• Quality Control: When generating 30+ scenes, leverage the automated quality scoring system. Setting your overall threshold to 0.15 is a great baseline to filter out blur, motion artifacts, or morphing errors typical in generative video.
• Asset Management: Keep your project directory organized by following the strict structure (e.g., /audio, /lyrics, /video/scenes) to ensure the scripts can locate assets for the compilation phase.
• Cost Awareness: Be mindful that Veo 3.1 via Vertex AI involves API costs. For cost-effective development, prioritize the Google Flow option unless specific high-end rendering features are needed.