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Wjs Translating Subtitles

Community
jianshuo
wjs-translating-subtitles

Use when the user has an SRT (or transcript text) in one language and wants it translated to another, with punctuation-bounded re-segmentation so cues end at real sentence breaks. Simplified Chinese (zh-CN) and English (en) are first-class targets; other targets follow the same rules. Outputs a target-language SRT or bilingual SRT — no audio, no burn-in. Triggers — "翻译字幕", "翻成中文", "translate this SRT", "中英双语字幕", "把这个 SRT 翻译成 X", "bilingual subtitles".

Overview

Publisherjianshuo
Repositoryclaude-skills
Skill namewjs-translating-subtitles
Stars
137
Forks
22
Bundled files
Instructions only
LicenseMIT
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Wjs Translating Subtitles 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.
    https://github.com/jianshuo/claude-skills/tree/main/wjs-translating-subtitles
  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/jianshuo/claude-skills.git /tmp/claude-skills
mkdir -p .claude/skills
cp -r /tmp/claude-skills/wjs-translating-subtitles .claude/skills/wjs-translating-subtitles
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Wjs Translating Subtitles 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 Wjs Translating Subtitles 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 Wjs Translating Subtitles 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.

wjs-translating-subtitles

Source-language SRT in → target-language (or bilingual) SRT out. This skill is text-only. Burn-in lives in /wjs-burning-subtitles; voice dub in /wjs-dubbing-video.

When to use

  • User has an SRT in language A and wants it in language B.
  • User pasted a transcript (with or without timestamps) and wants a translation that becomes an SRT.
  • User has an SRT but cues end mid-sentence — this skill's re-segmentation step fixes that.

When NOT to use

  • No source-language SRT yet → run /wjs-transcribing-audio first.
  • User wants burned-in subtitles → finish translation here, then /wjs-burning-subtitles.
  • User wants voice dub → finish translation here, then /wjs-dubbing-video.

Pick the target

Resolve target from the user's phrasing once, don't re-ask:

  • "翻成中文 / 中文字幕 / 中文配音" → zh-CN.
  • "translate to English / English subs / English dub" → en.
  • "bilingual" / "双语" → produce both .<source>.srt and .<target>.srt (and optionally a combined .<source>-<target>.srt).
  • Ambiguous → default to whichever the user has historically chosen in the project.

Simplified Chinese and English are fully validated. Other targets (Japanese, Korean, French, etc.) work via the same rules; the bottleneck is TTS-voice availability if dubbing follows — see /wjs-dubbing-video before promising.

Shared translation principles

  • Prioritize meaning over literal wording.
  • Use concise subtitle-style language — viewers read at ~3 wps for Chinese, ~3–4 wps for English; lines that exceed that go off-screen before they can be read.
  • Preserve the tone of the speaker. Casual source → casual target; formal source → formal target.
  • Do not over-translate names, brands, cultural references, or technical terms.
  • Keep numbers, dates, names, and places accurate.
  • If a phrase has no exact equivalent, translate the meaning naturally. No literal/word-for-word constructions.
  • Avoid stiff, machine-translated output.

Translating into Simplified Chinese (zh-CN)

  • Use natural spoken Mandarin for casual speech, formal Mandarin for formal speech.
  • Use Simplified characters only (do NOT use Traditional Hanzi unless the user explicitly asks).
  • Subtitle lines should be roughly 15 Chinese characters or fewer per line, max 2 lines per cue (3 only when unavoidable for very long cues).
  • Use Chinese punctuation: 「,」「。」「;」「:」「、」「——」. Never mix English commas/periods into Chinese subtitles.
  • Minimize filler demonstratives 「这」「那」「这个」「那个」「那份」「那种」「那里」「那样」. Spanish-to-Chinese (and English-to-Chinese) MT routinely inserts these because the source has overt demonstratives that Chinese usually drops. Examples:
    • "这把我们带入二元世界的载体" → "把我们带入二元的载体"
    • "运用那份能量" → "运用这股能量" if needed, or just "运用能量"
    • "正是在这合一里" → "正是在合一中"
    • "像罪人那样翻滚" → "像罪人翻滚" / "像罪人般翻滚"
    • "那份精微的觉知" → "精微的觉知" Keep them only when they carry real meaning (deixis, contrast, or fixed phrase like spiritual "我就是那" / "tat tvam asi"). Default is to delete; add back only if the sentence becomes ambiguous.

Examples (Spanish → Chinese):

text
Spanish: No pasa nada.            → Chinese: 没关系。
Spanish: Vamos a ver qué pasa.    → Chinese: 我们看看会发生什么。
Spanish: Me parece una locura.    → Chinese: 我觉得这太疯狂了。
Spanish: ¿Qué quieres decir?      → Chinese: 你是什么意思?
Spanish: La verdad es que no lo esperaba.
                                  → Chinese: 说实话,我没想到会这样。

Translating into English (en)

  • Use natural conversational English. Avoid translationese ("It is precisely through entering the body…" → "It's by entering the body…").
  • Lines should be roughly 40–42 characters or fewer (about 7–9 words), max 2 lines per cue. Hard cap 50 chars per line.
  • Use ASCII punctuation: , . ; : — (em-dash). Avoid Unicode curly quotes — keeps .srt portable.
  • For contemplative/spiritual content, prefer plain words over Latinate jargon: "presence" over "manifestation," "wholeness" over "totality," "wake up" over "awaken to consciousness."

Examples (Spanish → English):

text
Spanish: No pasa nada.            → English: It's nothing.
Spanish: Vamos a ver qué pasa.    → English: Let's see what happens.
Spanish: Me parece una locura.    → English: This feels crazy to me.
Spanish: ¿Qué quieres decir?      → English: What do you mean?
Spanish: La verdad es que no lo esperaba.
                                  → English: Honestly, I wasn't expecting this.

Re-segment at punctuation boundaries (mandatory)

Whisper segments by silence/breath, not grammar. The result almost always has cues that end mid-sentence (e.g., "...es una forma de aterrizar," next cue starts "el espíritu en el cuerpo..."). Any TTS that processes one cue at a time will then insert an unnatural pause exactly where the original speaker did not. The fix is mandatory before dubbing — and improves on-screen reading too.

Punctuation set differs:

  • Chinese cues must end at , 。 ; : —— or 、.
  • English cues must end at , . ; : — (em-dash) or, in practice for subtitles, occasionally a single dash. Never end an English cue on a comma-less clause break, and never split inside a phrase like "kind of" or "in order to".

Rules:

  • Every cue must end at a real punctuation mark. Never let a cue end on a noun, verb, conjunction, or article that flows into the next cue.
  • It is fine (and often necessary) to split a single source cue into 2–4 shorter cues, with timestamps interpolated by character position within the original cue's duration.
  • It is fine to merge the tail of one source cue with the head of the next when they form one clause — the merged cue inherits the start of the first and the end of the second.
  • Target 3–8 seconds per cue. Cues shorter than ~1.5s feel choppy on screen; cues longer than ~10s usually contain a missed punctuation break.

A typical 2–3 minute talk yields roughly 25–40 punct-bounded cues from 12–18 raw source cues. Don't try to keep the original cue count.

When TTS dubbing follows: the punctuation-bounded structure means each TTS clip is a complete utterance with proper end-intonation, and concatenating clips sounds natural because every join is at a real pause point.

SRT output rules

text
1
00:00:01,200 --> 00:00:04,800
中文字幕内容

2
00:00:04,800 --> 00:00:08,500
中文字幕内容
  • Number subtitles sequentially starting from 1.
  • Timestamp format: HH:MM:SS,mmm. Comma milliseconds, never period milliseconds.
  • Do not overlap timestamps.
  • Preserve the original timing unless adjustment is necessary.
  • Each subtitle should usually be 1–2 lines.
  • If one subtitle is too long, split it into shorter subtitles when timing allows.
  • Do not add commentary inside the subtitle file.

Bilingual output

When the user asks for bilingual: source on first line, target on second:

text
1
00:00:01,200 --> 00:00:04,800
No pasa nada.
没关系。

Rules:

  • Keep source first, target second.
  • Preserve timing.
  • Avoid adding extra explanations unless requested.
  • Keep both lines short enough to read.

Output formats

Depending on the user request, provide one or more:

  1. Target-only .srt
  2. Bilingual .srt (source line + target line)
  3. Target transcript without timestamps
  4. Side-by-side source/target table

Default output for "translate this SRT" with no other modifiers: target-only .srt + a short uncertainty note if needed.

File naming

text
input.srt                          # source (e.g., from /wjs-transcribing-audio)

translated outputs:
  input.zh-CN.srt                  # Simplified Chinese only
  input.en.srt                     # English only
  input.es-zh.srt                  # Spanish + Chinese bilingual
  input.es-en.srt                  # Spanish + English bilingual
  input.es-zh-en.srt               # three-language

BCP-47-style suffixes make the target language obvious at a glance and keep multiple target-language outputs side-by-side.

Handling unclear audio markers

If the source SRT contains [inaudible] or [unclear]:

  • Translate the surrounding context naturally.
  • Keep the bracketed marker in the target SRT (don't invent content).
  • If a [unclear] chunk makes a cue ungrammatical in the target language, leave it bracketed and add a note in the response (not in the SRT file).

Quality gate before handoff

  • Subtitle numbers are sequential
  • Timestamps are valid (HH:MM:SS,mmm, no overlap)
  • Milliseconds use commas
  • Translation is natural; speaker tone preserved
  • Line length within platform/cue caps
  • Proper nouns accurate
  • No cue ends mid-clause / mid-phrase
  • No invented content

Downstream

  • /wjs-burning-subtitles — burn this SRT onto the video, or soft-mux as a togglable track.
  • /wjs-dubbing-video — generate a TTS voice dub from this SRT, time-aligned to the original timing.
  • For bilingual playback: most platforms can soft-mux multiple subtitle tracks, but if you need bilingual visible at once, burn the *.source-target.srt directly via /wjs-burning-subtitles.

Common pitfalls

  • Letting the cue end mid-sentence after translation. The source's silence-aligned cues are unsafe boundaries; re-segment at punctuation, always.
  • Filler demonstratives in Chinese output. MT inserts 「这」/「那」 because the source had eso/that. Delete them aggressively.
  • Period milliseconds. Whisper local writes .mmm; SRT spec is ,mmm. Always normalize.
  • Translating proper nouns. Brand names, place names, technical terms — leave as-is or use the conventional target-language version (e.g., "OpenAI" stays, "New York" → "纽约").
  • Over-shortening for cue caps. If a line is genuinely longer than the cap, split into two cues with interpolated timestamps; don't drop meaning to fit the cap.
  • Forgetting to do re-segmentation when no dub is requested. The punct-bounded SRT is also better for reading — line endings at natural pauses match how viewers scan. Re-segment even when burn-only.

Frequently asked questions

What does the Wjs Translating Subtitles AI skill do?

Use when the user has an SRT (or transcript text) in one language and wants it translated to another, with punctuation-bounded re-segmentation so cues end at real sentence breaks. Simplified Chinese (zh-CN) and English (en) are first-class targets; other targets follow the same rules. Outputs a target-language SRT or bilingual SRT — no audio, no burn-in. Triggers — "翻译字幕", "翻成中文", "translate this SRT", "中英双语字幕", "把这个 SRT 翻译成 X", "bilingual subtitles".

Why use Wjs Translating Subtitles on TypingMind?

Because you install it once and use it with any model. Wjs Translating Subtitles 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 Wjs Translating Subtitles in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/jianshuo/claude-skills/tree/main/wjs-translating-subtitles. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Wjs Translating Subtitles?

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 Wjs Translating Subtitles?

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

Is the Wjs Translating Subtitles AI skill free?

Yes. It is published on GitHub by jianshuo under the MIT 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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