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在 OpenClaw 中安装
/install subtitle-extractor
功能描述
Subtitle extractor for Bilibili, YouTube, Xiaohongshu, Douyin, and local files. Extracts native subtitles or Whisper transcription in original format. Agent...
安全使用建议
This skill appears to implement subtitle extraction and optional Whisper transcription, but review these issues before installing:
- Cookie handling: the SKILL.md claim that cookies are "never transmitted externally" is incorrect — cookies passed to yt-dlp are used for network requests and will reach the video service. Do not place sensitive cookies in the skill directory unless you understand the exposure.
- Mandatory pre-run: the skill requires Steps 1–4 to run before responding. That can trigger network access and large model downloads even if the user's request doesn't need them. If you want to avoid unexpected network activity, run in an isolated environment or require explicit user approval before extraction/transcription.
- Missing/unclear requirements: ffmpeg and the faster-whisper Python package (and optionally modelscope) are required for transcription flows but are not listed in the registry metadata. Ensure you install/verify these tools first.
- Runtime downloads: transcription may auto-download Whisper models (~150MB for base); this consumes bandwidth and disk. Consider setting VIDEO_SUMMARY_WHISPER_MODEL to a vetted local model directory to avoid automatic downloads.
- File discovery: the script auto-discovers any *.txt with 'bilibili' in the skill directory. Keep that directory free of unrelated cookie files.
If these behaviors are acceptable, proceed; otherwise request the author clarify the manifest (declare ffmpeg/faster-whisper env vars) and remove the misleading security statement about cookie transmission.
功能分析
Type: OpenClaw Skill
Name: subtitle-extractor
Version: 1.0.0
The skill is a legitimate subtitle extraction and transcription tool that utilizes well-known utilities like yt-dlp and faster-whisper. The Python script (subtitle-extractor.py) implements secure subprocess handling using lists to prevent shell injection and limits cookie access to the local skill directory. The instructions in SKILL.md are strictly aligned with the stated functionality, providing clear error handling and dependency management without any evidence of data exfiltration, persistence, or malicious prompt injection.
能力评估
Purpose & Capability
The name/description match the code: it uses yt-dlp for native subtitles and faster-whisper/ffmpeg for transcription. However the registry metadata only lists yt-dlp as a required binary while the SKILL.md and script clearly require ffmpeg (for transcription) and the Python faster-whisper package. That mismatch between declared requirements and actual runtime needs is inconsistent.
Instruction Scope
SKILL.md forcefully requires running Steps 1–4 before answering user requests (non-negotiable), which can trigger network access, model downloads, and reading local cookie files even when not necessary. The SKILL.md claims cookie files are "read locally only, never transmitted externally," but the script passes cookies to yt-dlp for network requests (so cookie data will be sent to remote services). The script also auto-discovers any *.txt with 'bilibili' in its filename in the skill directory, which could pick up unintended cookie files.
Install Mechanism
There is no install spec (instruction-only), but the included Python script performs runtime downloads (Whisper models via faster-whisper/HuggingFace or ModelScope through modelscope), which will fetch ~100s of MBs of model data and write them to disk. Runtime downloads are not flagged in a manifest and may require network access and storage.
Credentials
The registry declares no required env vars, but SKILL.md and the script read/use VIDEO_SUMMARY_WHISPER_MODEL and reference VIDEO_SUMMARY_COOKIES. These env vars can change behavior (point to local models or cookie paths). The skill reads files from its directory and may write model files and outputs under the skill folder; the use of env vars and file discovery is not declared in the metadata.
Persistence & Privilege
The skill is not marked always:true and does not request platformwide privileges. It writes models and outputs to its own skill directory and does not modify other skills or system-wide agent settings. Autonomous invocation is allowed (platform default) but not combined with 'always'.
如何使用
- 确保已安装 OpenClaw（本地或 Docker 部署）
- 在对话框中输入安装命令：
/install subtitle-extractor - 安装完成后，直接呼叫该 Skill 的名称或使用
/subtitle-extractor触发 - 根据 Skill 的参数说明提供必要输入，即可获得结构化输出
版本历史
v1.0.0
Subtitle Extractor 1.0.0 – Initial Release
- Extracts subtitles from Bilibili, YouTube, Xiaohongshu, Douyin, and local files.
- Supports both native subtitle extraction and Whisper transcription fallback.
- Handles dependency checks (yt-dlp, ffmpeg, faster-whisper) and provides user guidance for missing components.
- Manages Bilibili cookies securely via auto-discovery in the skill directory.
- Outputs subtitle file path, video title, and author; agent saves and processes per user request.
元数据
常见问题
subtitle-extractor 是什么？
Subtitle extractor for Bilibili, YouTube, Xiaohongshu, Douyin, and local files. Extracts native subtitles or Whisper transcription in original format. Agent... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件，目前累计下载 101 次。
如何安装 subtitle-extractor？
在 OpenClaw 或 Claude Code 对话框中运行命令「/install subtitle-extractor」即可一键安装，无需额外配置。
subtitle-extractor 是免费的吗？
是的，subtitle-extractor 完全免费，采用 MIT-0 许可证，可自由下载、安装和使用。
subtitle-extractor 支持哪些平台？
subtitle-extractor 跨平台运行，可在任意部署了 OpenClaw / Claude Code 的环境中使用（cross-platform）。
谁开发了 subtitle-extractor？
由 poltawa（@poltawa）开发并维护，当前版本 v1.0.0。
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