embedded-captions

Add visually consistent captions to talking-head videos using Python, WhisperX, and ffmpeg.

Updated Feb 27, 2026

Search Tags:#visual identity #video editing #captioning #cinematic effects #talking-head videos

Ready to run

npx skills add https://github.com/zeitlinb/contentmonster --skill embedded-captions-zeitlinb

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: embedded-captions
Source: https://github.com/zeitlinb/contentmonster/tree/main/.agents/skills/embedded-captions
Command: npx skills add https://github.com/zeitlinb/contentmonster --skill embedded-captions-zeitlinb

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires whisper, hyperframes, sharp, puppeteer, ffmpeg, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill allows you to add engaging, visually consistent captions to talking-head videos, enhancing their narrative and visual appeal.

Core Features & Use Cases

Custom Visual Identities: Offers a variety of visual styles for captions, including cinematic effects, themed identities, and traditional rail-style subtitles.
Cinematic Mode: Embeds captions into the scene behind the subject, creating a dynamic and immersive viewing experience.
Theme Mode: Provides a themed constitution for each identity, offering unique visual styles tailored for different content types.
Automated Workflow: Simplifies the process of adding captions by automating steps like video transcription, background removal, and rendering.

Quick Start

Use the embedded-captions skill to add a cinematic-style caption to the video 'interview.mp4' using the 'keynote' identity.

Frequently Asked Questions about embedded-captions

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I add cinematic captions to talking-head videos automatically?▼

The cinematic mode embeds captions into the scene behind the subject by performing background removal, compositing video layers with ffmpeg, and transcribing audio with WhisperX to create a dynamic viewing experience.

What is the best way to style subtitles for vlogging and content creation?▼

Styling subtitles is achieved by applying custom visual identities that provide themed visual styles, cinematic effects, or traditional rail-style subtitles tailored for different content types.

Does this automated captioning workflow require Python and ffmpeg?▼

Yes, the automated captioning workflow requires Python scripts for background removal and image processing, alongside ffmpeg for video compositing to render the final output.

Can I use WhisperX for transcription in an automated video editing pipeline?▼

Yes, WhisperX is used for transcription within the video editing pipeline to automatically generate text from talking-head audio, which is then styled and composited into the video.

How does background removal work for embedding text in video production?▼

Background removal isolates the talking-head subject so captions can be layered behind them, utilizing hyperframes and sharp for image manipulation before ffmpeg composites the final video.

What are the limitations of using themed visual identities for captioning?▼

Themed visual identities are tailored for talking-head videos, meaning heavily dynamic scenes may not composite correctly, and the workflow depends on successful background removal and WhisperX transcription accuracy.