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Subtitle Burner

OrganizationPopular
TokenRhythm
subtitle-burner

Burn an SRT subtitle file into an MP4 via ffmpeg's subtitles filter (libass). Single-pass re-encode of video; audio copied as-is. Uses a verified managed Noto Sans CJK font when available. Used by meta-short-drama as the final subtitling step after merge.

Overview

PublisherTokenRhythm
Repositoryopensquilla
Skill namesubtitle-burner
Stars
7K
Forks
566
Bundled files
1
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by TokenRhythm on GitHub. Read the source before you install it.

Installation

Install the Subtitle Burner AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/TokenRhythm/opensquilla.git /tmp/opensquilla
mkdir -p .claude/skills
cp -r /tmp/opensquilla/src/opensquilla/skills/bundled/subtitle-burner .claude/skills/subtitle-burner
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Subtitle Burner in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Subtitle Burner on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Subtitle Burner is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

subtitle-burner

Burns an SRT subtitle stream into an MP4. The video is re-encoded (H.264 + faststart), the audio is copied untouched. libass renders the text per ASS-style override flags, so Chinese / Japanese / Korean characters render through the managed Noto Sans CJK font. The font directory is supplied through OPENSQUILLA_MEDIA_FONTS_DIR by the managed toolchain. An empty/whitespace-only SRT is a valid no-subtitle request: the input video is probed, copied to the requested output, and reported as SUBTITLES_SKIPPED: empty without invoking libass.

Inputs (with:)

keyrequireddefaultnotes
inputyesSource MP4 path.
subtitlesyes.srt path (UTF-8).
outputyesOutput MP4 path. Parent dir created if missing.
fontnoNoto Sans CJK SCOne libass FontName; comma-separated names are not a fallback chain.
fonts_dirnomanaged environmentDirectory containing subtitle fonts, normally supplied by OpenSquilla.
font_sizeno42Font size. When play_res=auto this is in source-video pixels.
margin_vno80Bottom margin in source-video pixels (because play_res=auto sets PlayRes to the input W×H).
play_resnoautoauto probes the input MP4 for resolution; or pass WxH like 720x1280. Setting this makes FontSize/MarginV act in source pixels rather than libass's 384×288 default.
crfno20x264 CRF (0-51, lower = better quality).
presetnomediumx264 preset.

Output

Prints the absolute path of the subtitled MP4 on stdout. Empty SRT input first prints SUBTITLES_SKIPPED: empty. Both paths stage the result in the output directory, require ffprobe to confirm a decodable positive-duration video stream, and atomically replace the destination only after validation. Non-zero exit on any encoding, copy, probe, or output-installation failure; stderr tails the last 2.5 KB of the encoder log for diagnosis.

Dependencies

  • ffmpeg ≥ 5.0 with libass, libx264, AAC, xfade, and zoompan support.
  • ffprobe from the matching ffmpeg distribution.
  • Noto Sans CJK Regular (managed by OpenSquilla; OFL-1.1).
  • Python 3.8+.

The script auto-locates ffmpeg via PATH; on Windows it falls back to the winget Gyan.FFmpeg / scoop / chocolatey install paths if PATH inheritance failed (matches the resolution logic in video-merger and video-still-animator).

Path-escaping notes

ffmpeg's subtitles= filter is picky on Windows:

  • Drive-letter colons (C:/…) must be backslash-escaped (C\:/…).
  • The path uses forward slashes regardless of host OS.
  • Single quotes inside the path get backslash-escaped.

The script applies these rules so callers don't have to.

Style chain

The force_style defaults render white text with a 2-px black outline on a transparent background (BorderStyle=3), bottom-centred, 80 px above the frame edge. Override any of the --* flags via with.* if you want a different look.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Subtitle Burner AI skill do?

Burn an SRT subtitle file into an MP4 via ffmpeg's subtitles filter (libass). Single-pass re-encode of video; audio copied as-is. Uses a verified managed Noto Sans CJK font when available. Used by meta-short-drama as the final subtitling step after merge.

Why use Subtitle Burner on TypingMind?

Because you install it once and use it with any model. Subtitle Burner is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Subtitle Burner in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/TokenRhythm/opensquilla/tree/main/src/opensquilla/skills/bundled/subtitle-burner. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Subtitle Burner?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Subtitle Burner?

As many as you like. As long as a model supports skills, you can use Subtitle Burner with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Subtitle Burner AI skill free?

Yes. It is published on GitHub by TokenRhythm under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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