ai-content-pipeline

What This Skill Does

The AI Content Pipeline skill provides a robust framework for orchestrating multi-modal generative AI workflows. By leveraging the inference.sh CLI, this skill enables users to chain complex operations—such as generating high-quality imagery with FLUX, creating realistic animations via Wan 2.5, synthesizing human-like speech with Kokoro TTS, and producing talking-head videos with OmniHuman. It functions as a powerful glue layer for your content factory, allowing you to build modular pipelines where the output of one model serves as the input for the next, facilitating automated, large-scale media production.

Installation

To integrate this skill into your OpenClaw environment, execute the following command in your terminal:

clawhub install openclaw/skills/skills/okaris/ai-content-pipeline

Ensure you have the inference.sh CLI installed and authenticated via infsh login to allow the agent to interface with the underlying model endpoints securely.

Use Cases

• Automated Social Media: Generate daily, trending topic-based short-form videos with automated scripts, voiceovers, and dynamic backgrounds.
• Marketing Materials: Create consistent, branded video assets or explainer videos by chaining LLM-written copy with specialized TTS and avatar synthesis.
• Content Scalability: Streamline production for YouTube or TikTok channels by defining repeatable logic for image-to-video transitions and media merging.
• Research to Media: Automatically summarize complex topics from search results and convert them into accessible, visual video explainers.

Example Prompts

1. "Build a 30-second YouTube Short about the history of space exploration, including a script, a realistic voiceover, and a generated cinematic background video."
2. "Create a talking head video using a professional portrait of a tech expert explaining quantum computing. Start by writing the script and then generate the audio and avatar video."
3. "Run the image to video workflow: generate a futuristic cyberpunk city skyline with FLUX, then animate it using the Wan 2.5 video model."

Tips & Limitations

• Chain Efficiency: Always verify individual step outputs (saved as .json files) before proceeding to expensive rendering steps.
• Resource Usage: Media generation is computationally intensive. Be mindful of your inference credits when chaining long-form animations.
• Quality Control: While the pipeline is automated, manual review of LLM-generated scripts is recommended before passing them to the TTS engine to ensure tone consistency.
• Integration: The media-merger tool is essential for final assembly; ensure your audio_url and video_url paths are correctly validated within your bash scripts.