Building an automated video editing pipeline with OpenClaw MCP tools

Automated video editing pipeline implementation

A developer created an OpenClaw skill that connects to a video editor to automate processing of recorded content like streams, talking head videos, and tutorials. The skill handles converting long recordings into shorts and clips for social media, addressing a problem where manual editing previously took 3-4 hours per recording.

Technical approaches for long-running tasks

The developer implemented three patterns to handle video processing in an MCP context where operations can't complete within typical timeout limits:

• WebSocket polling with HTTP fallback: The skill opens a socket connection for real-time progress events and falls back to HTTP polling if the socket fails
• Webhook support: For fire-and-forget workflows, users can pass a callback URL, and the server sends a signed project.completed event when done
• Watch mode with state: The skill stores a watchers.json file locally that tracks which channel URLs to monitor and which video IDs have already been processed

Key implementation insights

Spend control: When agents can spend money on your behalf, guardrails are essential. The developer built a three-tier spend policy with per-action limits and caps.

Presets for configuration: Instead of exposing many configuration fields, the skill defines 8 named presets. Agents can simply say "use the podcast preset" to apply complex configurations.

Next_steps in tool responses: After operations like downloads complete, responses include hints like "generate thumbnails" that agents naturally pick up and suggest without prompting.

Watch mode pattern for monitoring workflows

The watch mode pattern follows this structure:

• User registers a source like a YouTube channel URL
• Skill stores it locally with configuration (like daily caps)
• On each "check," the skill lists videos from the source and processes new ones

This pattern works for any "monitor a source and process items" workflow, including RSS feeds or Dropbox folders.

Performance metrics

• Processed about 15 recordings
• Average turnaround: 4 minutes for a 20-minute video
• Each processed video returns with a jump-cut edit, subtitles, and 20-30 shorts

The skill is available as @web2labs/studio on ClawHub with public source code on GitHub, using Web2Labs Studio as the backend.