Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add renky1025/agent-skills --skill video-dubbinggit clone --depth 1 https://github.com/renky1025/agent-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/renky1025/agent-skills/video-dubbing)<a href="https://agentmods.dev/skills/renky1025/agent-skills/video-dubbing"><img src="https://agentmods.dev/badge/skills/renky1025/agent-skills/video-dubbing/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/renky1025/agent-skills/video-dubbing"><img src="https://agentmods.dev/badge/skills/renky1025/agent-skills/video-dubbing.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 142 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
- medium Excessive Agency · line 140 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00066 | $0.01477 |
| Opus 5 | $0.00033 | $0.00739 |
| Sonnet 5 | $0.00013 | $0.00295 |
| Haiku 4.5 | $0.00007 | $0.00148 |
Grade A, and why
video-dubbing scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 143 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Video Dubbing & Subtitle Sync
Complete workflow: ASR → translate → TTS → merge audio → burn subtitles
Pipeline
视频 → 抽音轨(16kHz) → ASR(whisper) → AI翻译 → TTS(dub_segments) → 合并音轨 → 烧录硬字幕
Workflow
0. Prerequisite Check
Ensure whisper + moviepy + mlx-audio are installed:
pip install openai-whisper moviepy 2>/dev/null
# mlx_audio for TTS:
pip install mlx-audio 2>/dev/null # Apple Silicon
1. Extract Audio & Transcribe (ASR)
Extract 16kHz mono audio, then run whisper for accurate per-segment timestamps:
ffmpeg -y -i input.mp4 -ar 16000 -ac 1 -c:a pcm_s16le audio16k.wav
whisper audio16k.wav \
--model large-v3-turbo \
--language <source_lang> \
--output_format srt \
--output_dir .
Output: audio16k.srt with native timestamps matching the video.
Use
large-v3-turbofor best accuracy. If you want even better Chinese recognition, install Qwen3-ASR (pip install qwen-asr) and usescripts/qwen3_asr.pyinstead.
2. Translate Subtitles (AI does this)
Read the SRT, translate each segment's text into the target language. Output one line per SRT segment, preserving order exactly.
- Count SRT segments first (e.g. 27 segments → 27 translated lines)
- Save as
translated.txtin the working directory - Verify line count matches SRT segment count before proceeding
3. Generate Dubbed Audio
python3 scripts/dub_segments.py audio16k.srt translated.txt dubbing.wav subtitle_synced.srt --lang <target_lang>
What it does:
- Generates TTS per segment with voice consistency (first segment = voice reference)
- Preserves original timestamps — subtitles stay synced with video
- Smart speed adjustment: only adjusts segments that overflow their slot (atempo 0.88–1.20)
- Bridges adjacent gaps <1s for natural flow
4. Merge Dubbed Audio into Video
ffmpeg -y \
-i input.mp4 \
-i dubbing.wav \
-map 0:v:0 -map 1:a:0 \
-c:v copy -c:a aac -b:a 192k \
-shortest \
output_temp.mp4
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 143 lines · 66 tokens per session scan A e1b7ab942dd8
video-dubbing is a skill published in the GitHub repository renky1025/agent-skills (11 stars, last pushed yesterday), licensed MIT. It adds 66 tokens to every session and 1,477 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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