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Install in OpenClaw
/install video-japanese-subtitle
Description
日语视频自动翻译烧录技能。将日语视频转换为中文硬字幕，完整流程：ffmpeg 提取音频 → Whisper 日语转录 → LLM 翻译日→中 → SRT 转 ASS → ffmpeg NVENC 烧录硬字幕 → 验证。触发条件：用户提到视频字幕、硬字幕、字幕烧录、日语视频翻译、whisper 字幕、ass 字幕、...
Usage Guidance
Do not run this script uninspected. Specific things to check or change before use:
- Remove or rotate the hardcoded QCLAW_TOKEN; ideally provide the gateway URL and token via environment variables (and the skill metadata should declare them).
- Verify QCLAW_GATEWAY is indeed a trusted local service (default is http://127.0.0.1:28789). If you point it to a remote HTTP endpoint, your subtitle text and token will be sent in cleartext.
- Update the hardcoded filesystem paths (VIDEO_DIR, OUTPUT_DIR, FFMPEG_DIR) to match your environment or allow configuration via env vars/CLI arguments.
- Confirm the ffmpeg binary path is valid; the script forces a process PATH change (only for the process) which is generally OK but indicates author-specific setup.
- The script imports 'whisper' (openai-whisper) but comments mention faster-whisper — confirm which dependency you want and install accordingly.
- Prefer the skill to declare required credentials (QCLAW_TOKEN) in metadata instead of embedding secrets in code. If you cannot validate the gateway and token, treat the token as compromised and avoid using it.
If you want, I can produce a safer version of the script that reads gateway and token from environment variables, removes author-specific paths, and documents required env vars for the registry.
Capability Analysis
Type: OpenClaw Skill
Name: video-japanese-subtitle
Version: 1.0.0
The skill bundle contains several high-risk coding practices and hardcoded sensitive information in `scripts/subtitle_translate.py`. Specifically, it includes a hardcoded API token (`QCLAW_TOKEN`) and multiple absolute file paths (e.g., `D:\Users\liket\Desktop\000`) that point to a specific user's environment, making the script non-portable and potentially exposing local directory structures. While the logic appears aligned with the stated purpose of video translation, the inclusion of hardcoded credentials and the reliance on specific local paths are significant security vulnerabilities.
Capability Assessment
Purpose & Capability
Name/description match the implementation: the code extracts audio, runs Whisper transcription, sends batches to an LLM gateway for translation, converts SRT→ASS and uses ffmpeg to burn subtitles. Using a local LLM gateway, Whisper, and ffmpeg is coherent with the stated purpose.
Instruction Scope
SKILL.md and the script instruct the agent to run a local Python script that reads videos from a specified directory and POSTs subtitle text to an LLM gateway. The script modifies PATH and uses hardcoded absolute paths (e.g., D:\Users\liket..., E:\ffmpeg\...), and contains an embedded QClaw token. The runtime behavior is narrowly scoped to video files, but the embedded token and hardcoded paths grant more implicit access than the SKILL metadata declares (no required credentials listed).
Install Mechanism
No install spec — instruction-only with a Python script. No remote downloads or archive extraction. This is lower install risk, but the shipped script will execute on the host if run.
Credentials
Registry metadata lists no required env vars or credentials, yet the script includes a hardcoded QCLAW_TOKEN and a gateway URL constant. Embedding a bearer token in published code is disproportionate and risky. The script also hardcodes user-specific filesystem paths and a specific ffmpeg path, which is unusual for a generic skill and may leak user-specific configuration if reused verbatim.
Persistence & Privilege
The skill is not always-on and does not request special platform privileges. It only modifies its process PATH variable at runtime and writes output files to an output directory. It does not modify other skills or system-wide configs.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install video-japanese-subtitle - After installation, invoke the skill by name or use
/video-japanese-subtitle - Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
- Initial release: Automated Japanese video translation and hard-subtitle workflow.
- Supports full pipeline: ffmpeg audio extraction, Whisper transcription, LLM-based Japanese-to-Chinese translation, SRT-to-ASS conversion, ffmpeg NVENC hard-subtitle rendering, and validation.
- Resilient to interruptions: Implements step-wise checkpointing and file existence checks for resumability.
- Automatic fallback mechanisms: GPU/CPU encoding switch, LLM/MyMemory translation backup.
- Includes detailed troubleshooting guide and performance benchmarks.
Metadata
Frequently Asked Questions
What is 视频日语字幕?
日语视频自动翻译烧录技能。将日语视频转换为中文硬字幕，完整流程：ffmpeg 提取音频 → Whisper 日语转录 → LLM 翻译日→中 → SRT 转 ASS → ffmpeg NVENC 烧录硬字幕 → 验证。触发条件：用户提到视频字幕、硬字幕、字幕烧录、日语视频翻译、whisper 字幕、ass 字幕、... It is an AI Agent Skill for Claude Code / OpenClaw, with 66 downloads so far.
How do I install 视频日语字幕?
Run "/install video-japanese-subtitle" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is 视频日语字幕 free?
Yes, 视频日语字幕 is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does 视频日语字幕 support?
视频日语字幕 is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created 视频日语字幕?
It is built and maintained by xunnv (@xunnv); the current version is v1.0.0.
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