x-video-digest

name

x-video-digest

description

Analyze the VIDEO inside an X/Twitter post — fetch it, transcribe the speech locally (Qwen3-ASR), and produce a chapter-anchored content summary / script. Use when the user shares a tweet/X video and asks "이 영상 분석/스크립트 뽑아줘", "무슨 내용인지 정리", "transcribe this tweet video", "summarize the video in this X post", or drops an x.com/twitter.com/status link pointing at a video. Handles long videos (1h+) by transcribing a window at each tweet-parsed chapter instead of the whole runtime. Do NOT use for: text-only tweets or articles (use x-to-slack / x-to-notion), non-tweet video files or YouTube (use video-to-text / transcribee), or timeline scraping (use twitter-timeline-to-slack).

x-video-digest

Tweet video → local transcript → chapter-anchored summary. Thin composer over the FxTwitter API (metadata, no auth), scripts/stt/stt_transcribe.py (Qwen3-ASR, the repo STT engine — see [[stt-generation]]), and ffmpeg range-seek (grab only needed windows, not the whole multi-GB file). Does NOT reinvent download/transcribe — video-to-text covers the general case; this adds tweet fetch + long-video chaptering

• the analysis step those skills stop short of.

Pipeline

1. Metadata + chapters — python3 .claude/skills/x-video-digest/scripts/tweet_video_fetch.py "<url>" --json. Returns author/date/lang/text/video_url/duration_sec and chapters parsed from the tweet body (lines like 00:00 - topic, • 2:05:08 topic). Inspect duration first.
2. Scope gate (ask the user for long videos) — if duration_sec > ~20 min, the full transcript is a big STT job. Offer: (a) chapter-anchored summary [default], (b) full transcript, (c) metadata-only. Short clips → just do full.
3. Extract audio windows — ... tweet_video_fetch.py "<url>" --extract --outdir /tmp/xvid. Short video → one full.wav. Long video → one ~6-min wav at each chapter (+ a midpoint sample when a chapter spans >45 min). Writes <outdir>/batch.json for the STT wrapper.
4. Transcribe (Qwen3-ASR) — python3 scripts/stt/stt_transcribe.py --batch /tmp/xvid/batch.json --language <lang> --context "<domain hotwords>" --format json --out /tmp/xvid/transcripts.json. Batch = model loads once. Pass --language from step-1 lang (full name handled by wrapper). Anchor proper nouns/jargon with --context (brands, technical terms) — measurably fixes them.
5. Summarize — read transcripts.json, write a chapter-by-chapter content summary + a one-line overall thesis. Correct any misleading tweet framing against what the audio actually says (see gotchas). Report transcript + wav paths.
6. (Optional) publish — only if the user asks: Slack via x-to-slack pipeline ([[slack-post-formatting]]), Notion via x-to-notion, or a docx ([[docx-readability]]).

Rules & gotchas

• Language = English-only for figure/caption text if this feeds a paper/deck, but the chat summary follows the project language (Korean). Transcript stays in source language.
• Trust the audio over the tweet copy. Promo tweets routinely misframe ("leaked secret course" that is actually a public freeCodeCamp video, fake "$2M" hooks, funnel CTAs). State the real source and flag the marketing framing — do not amplify it ([[news-freshness-and-sourcing]] no fabrication, [[critical-thinking]]).
• Never fabricate a transcript. Only summarize what STT actually returned. If a window fails, report the gap; don't invent content.
• ffmpeg range-seek pulls only the window bytes — a 3-h 1080p video costs a few short range requests, not a full download. Seeks are approximate (±few sec); fine for summary.
• Cost: STT is GPU/CPU-bound and sequential; the wrapper has a CPU-headroom gate. Keep windows bounded (--window-sec, default 360). Don't transcribe 3 h when 6 windows answer it.
• Path B (STT unavailable): stt_transcribe.py --allow-fallback degrades to whisper ([[stt-generation]] / [[mcp-graceful-degradation]]).
• No auth / no secrets. FxTwitter is a public read proxy; if it fails, the video URL can also be pulled via reclip-media-downloader (yt-dlp) as a fallback source.

Reference

• Helper: scripts/tweet_video_fetch.py (fetch meta, parse chapters, extract windows).
• STT engine: repo scripts/stt/stt_transcribe.py (Qwen3-ASR primary, whisper fallback).
• Adjacent skills: video-to-text (general video STT), transcribee (STT + KB categorize), x-to-slack / x-to-notion (text tweets), reclip-media-downloader (download fallback).