| name | video-breakdown |
| description | Break down any video (YouTube, Loom, Instagram, TikTok, local file) frame by frame. Downloads the video, rips scene-change frames + a timestamped transcript locally, then Claude reads the visuals AND the words together to produce a content teardown — hooks, structure, on-screen visuals, why it works. Built in-house, no third-party code, only yt-dlp + ffmpeg. Trigger on "/video-breakdown", "break down this video", "analyze this reel/video", "teardown <url>", "what's working in this video". |
Video Breakdown
Reverse-engineer any video the way you actually watch one: visuals + audio at the same time. Built for competitor teardowns and ripping apart trending content in your niche.
How it works
No video model. The skill splits the video into the two things Claude reads natively — images (frames) and text (transcript) — with timestamps lined up, so Claude knows what's on screen the moment something is said.
yt-dlp pulls the video + auto-captions from almost any site (YouTube, Loom, IG, TikTok) or a local file.ffmpeg rips frames at every scene change (falls back to interval sampling for static talking-heads), capped at ~80.- Captions become a clean timestamped
transcript.txt (no captions = on-screen text in frames still carries it).
- Claude reads the frames + transcript and writes the breakdown.
Run it
bash ~/.claude/skills/video-breakdown/scripts/grab.sh "<url-or-path>" [slug] [--start MM:SS] [--end MM:SS] [--every N]
slug — folder name (auto-derived if omitted)--start / --end — only analyze a window--every N — seconds between frames when falling back to interval sampling (default 3)
Output lands in ~/claude-watch/library/<slug>/:
frames/frame_*.jpg — the visualsframes/times.txt — ffmpeg pts_time per frame (maps frame number → timestamp)transcript.txt — [MM:SS] line formatpacing.txt — cut-rhythm summary (cut count, first-cut time, avg gap, slow-open/static/fast flags). Heuristic from ffmpeg scene detection, not a motion read.meta.txt — title, source, frame count
Then (Claude's job)
- Read
transcript.txt fully.
- Read the frames. Use
times.txt to know when each frame happens, so visuals align to the script.
- Produce the teardown. Default structure (adapt to what you asked for):
- TLDR — one line on why this video works
- Hook — first 0–3s, exact words + what's on screen
- Structure — beat-by-beat with timestamps (acts, retention turns, CTA)
- Visual playbook — cuts, text overlays, b-roll, motion, pacing
- Why it's trending / what to steal — the transferable moves
- If asked: graphics/motion prompts, script template, hook variations for a niche
Notes
- Caption-less sources (often IG/TikTok, local files): grab.sh falls back to local whisper.cpp (
whisper-cli + ~/.cache/whisper-cpp/ggml-small.en.bin, no API key, on-device, wired in 2026-07-05). If whisper-cli or the model is missing, transcript says NO_CAPTIONS. Lean on frames + on-screen text, say so rather than guessing audio.
- Everything runs locally. No content leaves the machine except the original yt-dlp fetch.
- Frame folder is wiped + rebuilt on each run for the same slug.
