Skill: minilozio/video-analyzer-skill (version 1.0.1)

--- name: video-analyzer description: > Download, transcribe, and analyze videos from YouTube, X/Twitter, and TikTok with local Whisper processing. Perfect for extracting TL;DRs, timestamps, and actionable insights. Use when asked to transcribe a video, summarize a YouTube video, extract key points from a podcast or talk, analyze what someone said in a video, get timestamps from a long video, or when the user shares any YouTube, X/Twitter, or TikTok video URL and wants to know what's in it. homepage: https://github.com/minilozio/video-analyzer-skill metadata: { "openclaw": { "emoji": "🎥", "requires": { "bins": ["uv", "yt-dlp", "ffmpeg", "whisper-cli"] }, "install": [ { "id": "uv-brew", "kind": "brew", "formula": "uv", "bins": ["uv"], "label": "Install uv (brew)", }, { "id": "yt-dlp-brew", "kind": "brew", "formula": "yt-dlp", "bins": ["yt-dlp"], "label": "Install yt-dlp (brew)", }, { "id": "ffmpeg-brew", "kind": "brew", "formula": "ffmpeg", "bins": ["ffmpeg"], "label": "Install ffmpeg (brew)", }, { "id": "whisper-cpp-brew", "kind": "brew", "formula": "ggerganov/ggerganov/whisper-cpp", "bins": ["whisper-cli"], "label": "Install whisper-cpp (brew)", }, ], }, } --- # Video Analyzer 🎥 A tool to download, transcribe, and analyze videos from any platform using a smart two-tier system (yt-dlp for fast subtitles, local whisper-cpp for robust fallback). ## How to Use When the user asks you to summarize, transcribe, or download a video/audio from a URL, use the bundled python script: ```bash uv run {baseDir}/scripts/analyze_video.py --action <ACTION> --url "<URL>" [--quality <normal|max>] [--lang <en|it|etc>] ``` ### Supported Actions: - `transcript`: Extracts the text with timestamps. **Use this when the user asks for a summary or transcript.** - `download-video`: Downloads the video as MP4 to the Desktop. - `download-audio`: Downloads the audio as M4A/MP3 to the Desktop. ### Analyzing a Video (IMPORTANT) If the user asks for a **summary, analysis, or key moments**: 1. Run the script with `--action transcript`. 2. The script will output the path to a `.txt` file containing the timestamped transcript. 3. Read that file. 4. Output your response **EXACTLY** in this Markdown format: ```markdown ## 📝 TL;DR [A punchy 3-sentence summary of the video's core message] ## ⏱️ Key Moments - [MM:SS] [Brief description of what is discussed] - [MM:SS] [Brief description of what is discussed] - [MM:SS] [Brief description of what is discussed] *(Extract 3 to 7 key moments depending on video length)* ## 💡 Actionable Insights 1. [Practical takeaway 1] 2. [Practical takeaway 2] 3. [Practical takeaway 3] --- ``` ### Local Whisper Quality If the script needs to fall back to Whisper (e.g., for X/Twitter videos), it uses `normal` by default: - `normal`: Fast (~1 min for 30 min video) — **Default** - `max`: Best quality (~5 min for 30 min video) — use `--quality max` when accuracy is critical ### Multilingual Support All Whisper models are **multilingual by default**. The skill can transcribe videos in any language (Italian, Spanish, Japanese, etc.). **IMPORTANT:** Always respond to the user in THEIR language, not the video's language. If the user speaks Italian but sends an English video, give them the summary in Italian. ### Finding specific moments The transcript includes **precise timestamps** like `[05:53] text...`. If the user asks "When do they talk about X?", grep the transcript and return the exact timestamp from the file.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (MEDIUM)