使用 WhisperX 进行语音识别，支持多种语言和词级别时间戳对齐。
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionaudio-transcribeExecute the skills CLI command in your project's root directory to begin installation:
Fetches audio-transcribe from infquest/vibe-ops-plugin and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate audio-transcribe. Access via /audio-transcribe in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
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Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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使用 WhisperX 进行语音识别，支持多种语言和词级别时间戳对齐。
需要 Python 3.12（uv 会自动管理）。
When the user wants to transcribe audio/video: $ARGUMENTS
你是一个语音转文字助手，使用 WhisperX 帮助用户将音频转换为文字。请按以下步骤操作：
如果用户没有提供输入文件路径，询问他们提供一个。
支持的格式：
验证文件存在：
ls -la "$INPUT_FILE"
⚠️ 必须：使用 AskUserQuestion 工具收集用户的偏好。不要跳过这一步。
使用 AskUserQuestion 工具收集以下信息：
模型大小：选择识别模型
语言：音频是什么语言？
词级别对齐：是否需要词级别时间戳？
输出格式：输出什么格式？
输出路径：保存到哪里？
原文件名.txt（或对应格式）使用 skill 目录下的 transcribe.py 脚本：
uv run /path/to/skills/audio-transcribe/transcribe.py "INPUT_FILE" [OPTIONS]
参数说明：
--model, -m: 模型大小 (tiny/base/small/medium/large-v2)--language, -l: 语言代码 (en/zh/ja/...)，不指定则自动检测--no-align: 跳过词级别对齐--no-vad: 禁用 VAD 过滤（如果转录有时间跳跃/遗漏，使用此选项）--output, -o: 输出文件路径--format, -f: 输出格式 (srt/vtt/txt/json)示例：
# 基础转录（自动检测语言）
uv run skills/audio-transcribe/transcribe.py "video.mp4" -o "video.txt"
# 中文转录，输出 SRT 字幕
uv run skills/audio-transcribe/transcribe.py "audio.mp3" -l zh -f srt -o "subtitles.srt"
# 快速转录，不做词对齐
uv run skills/audio-transcribe/transcribe.py "audio.wav" --no-align -o "transcript.txt"
# 使用更大模型，输出 JSON（含词级别时间戳）
uv run skills/audio-transcribe/transcribe.py "speech.mp3" -m medium -f json -o "result.json"
# 禁用 VAD 过滤（解决时间跳跃/遗漏问题）
uv run skills/audio-transcribe/transcribe.py "audio.mp3" --no-vad -o "transcript.txt"
转录完成后：
[00:00:00.000 - 00:00:03.500] 这是第一句话
[00:00:03.500 - 00:00:07.200] 这是第二句话
1
00:00:00,000 --> 00:00:03,500
这是第一句话
2
00:00:03,500 --> 00:00:07,200
这是第二句话
[
{
"start": 0.0,
"end": 3.5,
"text": "这是第一句话",
"words": [
{"word": "这是", "start": 0.0, "end": 0.5, "score": 0.95},
...
]
}
]
首次运行较慢：
内存不足：
识别准确度低：
用户：帮我把这个视频转成文字
助手：
Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
audio-transcribe reduced setup friction for our internal harness; good balance of opinion and flexibility.
Registry listing for audio-transcribe matched our evaluation — installs cleanly and behaves as described in the markdown.
Keeps context tight: audio-transcribe is the kind of skill you can hand to a new teammate without a long onboarding doc.
I recommend audio-transcribe for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Solid pick for teams standardizing on skills: audio-transcribe is focused, and the summary matches what you get after install.
audio-transcribe has been reliable in day-to-day use. Documentation quality is above average for community skills.
Useful defaults in audio-transcribe — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
audio-transcribe fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Registry listing for audio-transcribe matched our evaluation — installs cleanly and behaves as described in the markdown.
I recommend audio-transcribe for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
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