claude-real-video

claude-real-video

▶ The 60-second pixel film — sound on (mp4 on GitHub) · an AI agent searches "how can an LLM truly understand video?", finds a key, and unlocks vision.

60-second real demo — real install, real run, real viewer.

Let Claude — or any LLM — actually watch a video.

pip install "claude-real-video[whisper]"
npx skills add HUANGCHIHHUNGLeo/claude-real-video   # one command, installs the skill into Claude Code, Cursor, Codex, Copilot, Gemini CLI & 50+ agent hosts

Claude Code plugin marketplace (enable auto-update in /plugin → Marketplaces if you want it):

/plugin marketplace add HUANGCHIHHUNGLeo/claude-real-video
/plugin install claude-real-video@claude-real-video

Then paste a video link into your agent and ask about it. (CLI-only use? crv "<url>" works with just the pip install.)

Naming: crv is the short name for claude-real-video (the PyPI package). The paid add-on, crv Pro, is sold on Capafy under the listing name "llm-real-video Pro".

Same 58-second clip: fixed 1 fps sampling = 58 frames. crv keeps the 26 that actually differ — and --grid packs them into 3 contact sheets. Fewer tokens, nothing missed.

This free version lets your AI see the video. crv Pro lets it understand it — how it was shot (cut rhythm, camera moves) plus a timestamped timeline of what frames can't show: gestures, expressions, voice pitch shifts, emotion, sound events. One-time price $29 — get it on Capafy or buy with card via Lemon Squeezy.

Most AI tools don't really see a video. Paste a YouTube link into ChatGPT and it reads the transcript, not the picture. Claude won't take a video file at all. Even Gemini, which can read video natively, has to send it up to Google and samples frames at a fixed interval (1 fps by default), so fast cuts slip past.

claude-real-video does it differently, and the processing runs locally: point it at a URL or a file, and it pulls the frames that actually matter (every scene change, not a fixed quota), throws away the near-duplicates, transcribes the audio, and hands you a clean folder any LLM can read. All the processing happens on your own machine — what gets sent anywhere is only the frames/text you choose to paste into an LLM afterwards.

crv "https://www.youtube.com/watch?v=..."
# → crv-out/frames/*.jpg  +  frames.json (per-frame timestamps)  +  transcript.txt/.json  +  MANIFEST.txt

Then drop the frames + MANIFEST.txt into Claude / ChatGPT / Gemini and ask away.

No terminal needed — run crv-web and a local page opens (Traditional Chinese / Simplified Chinese / English): paste a YouTube or Reels link or a file path, click Analyze, open the result viewer. Video analysis and output generation run on your machine — the source video never gets uploaded. (If you then paste the extracted frames or transcript into a cloud LLM, that data goes to that provider.)

Want to eyeball what the model will see first? Add --viewer — it writes a local viewer.html (video + keyframe grid + transcript) you can double-click open. No network, no extra installs.

Slow-changing content (animation tutorials, gradual morphs, slow pans): add --adaptive — frames are picked against their rolling neighbourhood instead of a fixed threshold, so a 2-3s squash-and-stretch that never spikes any single frame still gets captured.

Text-heavy content (lecture slides, screen recordings, talking-head explainers): add --text-anchors — extra frames are forced at subtitle-cue timestamps, so each spoken segment gets a matching visual even when the scene barely changes. Needs a sidecar .srt/.vtt or an embedded subtitle track — captions burned into the pixels can't be detected. At most one forced frame per second; scene detection is untouched.

Multi-speaker content (interviews, podcasts, meetings): add --speakers — every transcript line gets a speaker label ([SPEAKER_00], [SPEAKER_01], …) so the model can follow who said what. Runs a local diarization model (45 MB, downloads once, no account or token needed). Install with pip install "claude-real-video[speakers]".

Not doing LLM work? It also works as a general-purpose video keyframe extractor — scene-change detection + dedup, no ML models to download.

Using Claude Code — or any coding agent? One command installs the skill (works with Claude Code, Cursor, Codex, Copilot, Gemini CLI and other agentskills.io-compatible hosts):

pip install "claude-real-video[whisper]"
npx skills add HUANGCHIHHUNGLeo/claude-real-video

Then just paste a video link into your agent and ask about it.

Manual install (clone + copy)

git clone https://github.com/HUANGCHIHHUNGLeo/claude-real-video.git
mkdir -p ~/.claude/skills && cp -r claude-real-video/skills/claude-real-video ~/.claude/skills/

New in 0.3.0 — tell it why you're watching, and keep what it finds:

crv "https://youtu.be/..." --why "find the pricing strategy" --kb ~/notes

--why makes the analysis focus on what you care about instead of a generic summary; --kb saves the result as a dated note in your own notes folder, so it doesn't die in crv-out.

Measured numbers

Real run on a 3-minute 640x360 video (benchmark/jfk-rice.mp4), Mac mini M4, local CPU, frames + dedup only (--no-transcribe). Image tokens estimated with Anthropic's (width x height) / 750 — 307 tokens/frame at 640x360.

Mode	Frames kept	Wall time	Est. image tokens
default (scene-change + 1s floor)	170 (from 180 extracted)	23.5 s	~52k
--max-frames 80	80	23.4 s	~25k
--adaptive (catches slow morphs)	270	36.8 s	~83k

Dedup v0.7.16 — small-subject fast action no longer disappears. A percentage comparator is structurally blind to a subject that covers <1% of the frame (it can never change 8% of the pixels). Found in a user's 2,181-video batch run; fixed with a third "action channel". Synthetic repro — static 1280x720 shot, a 40x90 px subject (0.4% of frame) moves fast only in the last 10 of 65 frames:

	Frames kept	Action frames survived
v0.7.15	2	1 / 10
v0.7.16	11	10 / 10 — full trajectory

Why not just sample frames?

Most "let an LLM watch a video" scripts (and Gemini's own pipeline) grab frames at a fixed interval — e.g. one per second. That over-samples a static screencast and under-samples a fast-cut reel. claude-real-video is smarter:

	fixed-interval sampling	claude-real-video
Frame selection	every N seconds	scene-change detection + density floor
Repeated shots (A-B-A cuts)	sent again every time	sliding-window dedup sends each shot once
Static slide (10 min)	~600 near-identical frames	collapses to 1 (dedup)
Fast-cut reel	misses frames between samples	catches each visual change
Audio	often ignored	Whisper transcript w/ language detect
Where the processing happens	often in someone's cloud	on your machine (you choose what to share with an LLM afterwards)
Input	usually local file only	URL (yt-dlp) or local file

You feed the model fewer, more meaningful frames — cheaper context, better understanding.

Install

pip install "claude-real-video[whisper]"   # recommended: frames + dedup + audio transcription
pip install claude-real-video              # core only (frames + dedup)

pip extras never install themselves — without [whisper] there is no speech-to-text (videos that ship their own subtitles still get a transcript).

System requirement: ffmpeg

ffmpeg / ffprobe are used for frame extraction and audio, and aren't pip-installable. Install them once:

OS	command
macOS	brew install ffmpeg
Linux	sudo apt install ffmpeg (or your distro's package manager)
Windows	winget install Gyan.FFmpeg — or choco install ffmpeg — or download a build and add its bin\ folder to your PATH

Verify it's on your PATH:

ffmpeg -version

Transcription uses the whisper CLI (installed by the [whisper] extra, or pip install openai-whisper). Whisper also relies on ffmpeg.

Faster + hallucination-proof transcripts (recommended): install the [fast] extra and crv automatically switches to faster-whisper — same models, same output files, several times faster, and gated by Silero VAD (voice-activity detection): music-only or silent audio yields an honest "no speech" note instead of whisper's classic invented caption. No new flags to learn:

pip install 'claude-real-video[fast]'

If both are installed, faster-whisper wins; if it ever fails, crv falls back to the whisper CLI on its own.

Works on macOS, Windows, and Linux — Python 3.10+.

Usage

# A YouTube / Instagram / TikTok / ... link
crv "https://www.instagram.com/reel/XXXX/"

# A local file, English transcript, output to ./out
crv lecture.mp4 -o out --lang en

# Frames only, no transcription
crv clip.mp4 --no-transcribe

# A login-gated video (your own / authorised use): pass a Netscape cookie file
crv "https://..." --cookies cookies.txt

python -m claude_real_video ... works as an alias for crv too.

Options

flag	default	meaning
-o, --out	crv-out	output directory
--overwrite	off	replace a previous analysis living in the output directory (without this, a non-empty output dir is refused to avoid mixing videos)
--scene	0.30	scene-change sensitivity (lower = more frames)
--fps-floor	1.0	at least one frame every N seconds
--max-frames	auto: clamp(150, duration×1.5, 600)	hard cap on total frames (explicit value always wins)
--adaptive	off	adaptive scene detection: catches slow morphs (2-3s squash/stretch, gradual pans) a fixed threshold misses, by comparing each frame against its rolling neighbourhood
--text-anchors	off	force extra frames at subtitle-cue timestamps (sidecar .srt/.vtt or embedded track) — for videos where meaning changes faster than pixels; at most one forced frame per second
--speakers	off	label every transcript line with the speaker ([SPEAKER_00] …) via local diarization — needs pip install "claude-real-video[speakers]", 45 MB model downloads once
--lang	auto	Whisper language (en, zh, auto, ...)
--whisper-model	base	Whisper model for transcription (tiny/base/small/medium/large/turbo — base is fast; want sharper transcripts? --whisper-model turbo is one flag away: a pruned large-v3 — much faster than large with a minor quality trade-off, one-time 1.6GB download, ~6GB memory)
--dedup-threshold	8	% of pixels that must change for a frame to count as new; higher = fewer frames (the settled-local detector's gate scales with it too)
--dedup-window	4	compare against the last N kept frames — a shot the model already saw doesn't come back after a cutaway (1 = consecutive-only)
--report	off	keep dropped frames in ./dropped + write report.html visualising every keep/drop decision
--no-transcribe	off	skip audio
--keep-audio	off	also save the full soundtrack (audio.m4a) so audio models can hear it
--viewer	off	also write viewer.html — browse the video, keyframes and transcript in one local page (double-click to open)
--grid	off	also tile the kept frames into 3x3 contact sheets (./grids) — consecutive frames side by side help the model follow motion and progression
--why	–	why you're watching, e.g. --why "find the pricing strategy" — written into MANIFEST.txt so the model analyses with that lens instead of a generic summary
--kb	–	also save the analysis as a dated markdown note into this folder (your Obsidian vault, notes dir, ...) — so it joins your knowledge base instead of dying in crv-out
--cookies	–	Netscape cookie file for login-gated sources
--cookies-from-browser	–	read login cookies straight from your own browser — chrome, safari, firefox or edge (your own account only)

What --grid output looks like

One contact sheet = nine consecutive keyframes, in order, filenames on each cell — the model reads a sequence, not scattered stills:

MCP server (Claude Desktop / Cursor / any MCP client)

crv also ships as an MCP server, so MCP clients can ask for a video to be watched directly — same local pipeline, zero cloud.

pip install 'claude-real-video[mcp]'

Claude Code:

claude mcp add crv -- crv-mcp

Claude Desktop — add to claude_desktop_config.json:

{ "mcpServers": { "crv": { "command": "crv-mcp" } } }

Tools: watch_video(source, max_frames, language, transcribe) returns the timestamped transcript plus the first batch of keyframes as images; get_frames(source, start_index, count) pages through the rest. Analyses are cached under ~/.cache/crv-mcp, so follow-up questions about the same video are instant.

mcp-name: io.github.HUANGCHIHHUNGLeo/claude-real-video

Verified end-to-end on Claude Code (the model described a test video's frames correctly through the tool). Claude Desktop and Cursor speak the same MCP stdio protocol — config above; open an issue if anything misbehaves.

Use it from Python

from claude_real_video import process

r = process("https://youtu.be/...", "out", lang="en")
print(r.frame_count, r.transcript_path)

How it works

• Fetch — yt-dlp for URLs (optional cookies), or copy a local file.
• Extract — one chronological ffmpeg select pass grabs every scene change plus a density floor (at least one frame every --fps-floor seconds), so fast cuts and slow screencasts are both covered.
• Dedup — three channels against a sliding window of the last --dedup-window kept frames, so an A-B-A cutaway doesn't re-send a shot the model has already seen. A global channel measures real pixel difference (downscaled RGB, not a perceptual hash — hashes go blind on flat colours and equal-luma hue changes); --dedup-threshold is the % of it that must change. A settled-local channel (v0.7.4) catches what the global one can't see: thin pen strokes, caption/text-card swaps and small UI updates that average out to ~0% globally. It looks, on a finer signature, for a region that differs strongly from every recent kept frame (with 1px shift tolerance, so film grain and frame jitter don't trigger) and is no longer changing — a settled new state, not motion mid-flight — with a cooldown so continuous motion that pauses every second (a waving flag, drifting smoke) can't keep re-firing. The final frame is evaluated even if still in motion (so a video's closing state is never lost), but it must clear both contrast gates like any other frame. --report writes report.html showing every keep/drop decision with its diff % (settled-local keeps are labelled), for tuning.
• Text — if the video already has subtitles (a sidecar .srt/.vtt next to a local file, or an embedded subtitle track), those are used as the transcript — faster and more accurate than re-transcribing. Only when there are no subtitles does it fall back to Whisper on the audio (skipped cleanly if there's no audio).
• Audio (optional, --keep-audio) — save the full original soundtrack (audio.m4a: music + speech + effects, copied losslessly when possible). The transcript only has the words; the audio file lets a model that can listen (Gemini, GPT-4o, …) actually hear the music and tone.
• Timestamps — every kept frame's source-video time survives the whole pipeline (extraction → dedup → --max-frames thinning → renaming) and is written to frames.json (file / timestamp_sec / timestamp / selection_reason). Cite visual evidence as frame_012 @ 00:03:41, align frames with transcript.json segments, or feed the map to a video-RAG pipeline. In viewer.html, click any keyframe → "play video from here".
• Manifest — MANIFEST.txt summarises everything for the model.

So the model can see (key frames), read (transcript) and — with --keep-audio — hear (full soundtrack) the video. The transcript is plain text any model can read; the tool doesn't burn subtitles into the video — burning is a presentation choice, not something needed to make a video AI-readable.

Notes

• Only download content you have the right to. The --cookies option is for your own, authorised access — don't ship credentials in a repo.
• Use one output folder per video. Re-running into a folder that already holds an analysis is refused (so two videos never mix); pass --overwrite to replace it.

crv Pro — understand how a video was shot

The free tool gives your AI keyframes and a transcript — enough to know what a video is about. crv Pro adds everything else: how it's shot, how it's cut, how it's spoken, what it feels like. All computed on your machine, written as plain text any LLM can read.

• Camera & pacing (--motion) — every shot auto-labelled: static, pan, tilt, zoom, handheld. Full shot table: per-shot duration, cuts per minute, pacing across open/middle/close. High-motion shots get 0.2s-apart burst frames.
• Sound & emotion (--senses) — voice emotion, tone curves and audio events (laughter, SFX, ambience) timestamped segment by segment. Vocals and music auto-separated: emotion reads the clean voice, music gets its own BPM + energy track. No-dialogue footage (MVs, film) falls back to reading mood from color and light.
• Interactive viewer (--viewer) — one self-contained web page per analysis: the video, a clickable event timeline that jumps to the second, a transcript that highlights along with playback. EN / 繁中 / 简中.
• Two reports, one flag (--ai-report) — with your own API key: one report on how it's shot, one on what it says.
• Breakdown report (--breakdown) — hook analysis, pacing curve, camera language, and a rubric your own LLM completes into a full teardown.

One-time price $29:

• Buy on Capafy (instant download, license key included): https://capafy.ai/agent/llm-real-video-pro-let-any-llm-watch-videos/5451082151
• Buy with credit card (Lemon Squeezy checkout, instant download): https://leoaido.lemonsqueezy.com/checkout/buy/ff552000-adc0-49f1-8eec-5e8ada1905a1
• Product page & demo: https://leoaido.com/crv-pro/

Following the build? I'm documenting the road from open-source tool to first paying customer, in public — @LeoAidoAI on X.

FAQ

What is the best tool to let an LLM watch or analyze a video?

It depends on what "watch" means for you. If you just want one answer about one clip and don't mind uploading it, a hosted multimodal model (e.g. Gemini) is the shortest path. If you want any LLM — Claude, GPT, Gemini or a local model — to analyze video reproducibly and locally, you want a preprocessing pipeline: scene-aware keyframes + a timestamped transcript, handed to the model as evidence it can cite. That pipeline is exactly what claude-real-video does, in one command, with nothing leaving your machine. Uniform frame sampling (1 fps) either misses cuts or floods the context window; scene-aware extraction keeps the frames that carry information.

How can I make Claude understand a video?

Claude cannot ingest video files directly. The working approach:

pip install "claude-real-video[fast]"
npx skills add HUANGCHIHHUNGLeo/claude-real-video   # or install via the Claude Code plugin marketplace

Then in Claude Code: Analyze this video: /path/to/video.mp4. The skill extracts scene-aware keyframes, a timestamped transcript (transcript.json), a frame→timestamp map (frames.json) and a MANIFEST.txt that tells the model how to read the folder — so Claude can cite frame_012 @ 00:03:41 instead of guessing.

What is claude-real-video?

An MIT-licensed Python CLI (crv) that turns a video into what an LLM can actually read: scene-aware keyframes (with real source timestamps that survive dedup and renaming), sliding-window deduplication so small-subject motion isn't thrown away, and local Whisper transcription with optional speaker labels. Works with YouTube URLs or local files, runs 100% locally. It exists because subtitles alone are not watching — models that only read the transcript hallucinate everything visual.

Who makes this

Built by Leo Huang — a one-person company running on AI. I post what actually breaks and what works while building tools like this: https://x.com/LeoAidoAI

License

MIT