watch-cli

Watch any social video → get an architecture diagram, working component, runnable notebook, or step-by-step cheat sheet — automatically.

Eyes and ears for your AI agent. watch-cli composes yt-dlp + ffmpeg + a Whisper-class ASR into a single command that hands an agent the raw materials to "watch" any video: VIDEO + FRAMES + TRANSCRIPT, ready for an LLM to read frames as images and transcript as text.

watch https://twitter.com/anyone/status/12345

Works on YouTube, X, LinkedIn, TikTok, Reddit, Vimeo, and Facebook. Login-walled posts (LinkedIn, private X, FB) fall back to your browser cookies automatically.

What you can build

Hand the watch output to your agent with one of five prompts in prompts/:

Drop in a video of…	Get back
A coding walkthrough	Working project files
A system architecture talk	Interactive architecture diagram
A UI / motion demo	Working React component
A paper or research talk	Runnable notebook
A long tutorial	Step-by-step cheat sheet

The prompt library is what turns "video → frames + transcript" into "video → working artifact". The full Prompt library section below has copy-paste templates.

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Why this exists

Large language models can't watch video natively — they read text and look at still images. You can hand a video to a multimodal API and get back a chat-style summary, but for an agent workflow that's the wrong artifact: the agent wants the raw frames and the full transcript so it can reason for itself, not someone else's pre-digested recap.

A video is just frames + audio, and each piece already has a fast, near-free primitive:

• yt-dlp downloads from any social platform
• ffmpeg extracts evenly-spaced frames
• An ASR model transcribes the audio
• A multimodal LLM hears tone, music, SFX, language, mood

Compose them and your agent has the materials to watch any social video.

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What it looks like

$ watch https://www.linkedin.com/posts/some-talk_activity-12345

VIDEO: /tmp/dl-video/abc123.mp4
DURATION: 218
FRAMES:
  /tmp/frames_abc123/frame_01.jpg
  /tmp/frames_abc123/frame_02.jpg
  …
TRANSCRIPT:
  Today I want to talk about how decomposition unlocks 10× cost reduction in
  multimodal pipelines …

Your agent reads the JPGs and the transcript. That's the whole watch.

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Why pay-per-use, not subscription

Most subscription summary tools start around $15/month and deliver a polished, human-readable summary. If you're feeding an AI agent, that's the wrong artifact — agents need raw frames and the full transcript to reason for themselves, not someone else's pre-digested recap.

A typical research session is 1–3 videos, not 100. Through Kyma — the default backend — a 1-hour video costs ~$0.05 (transcribe is the only paid step; frame extraction is local ffmpeg).

This month you watch	You pay
0 videos	$0
1 one-hour video	~$0.05
100 one-hour videos	~$5

No monthly minimum, no seat license, no lock-in. The free credit at Kyma signup is enough to run the full pipeline end-to-end before you spend a cent.

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Install

# macOS — Homebrew (recommended)
brew tap sonpiaz/tap
brew install watch-cli

# Any OS — curl
curl -fsSL https://github.com/sonpiaz/watch-cli/releases/latest/download/install.sh | bash

The curl one-liner auto-falls back to git clone of main if no published release tarball is reachable.

Claude Code (skill marketplace)

If you use Claude Code, install watch-cli as a skill:

/plugin marketplace add sonpiaz/watch-cli
/plugin install watch-cli@watch-cli

The agent then picks up watch <url> as a first-class command.

Pin a specific version:

WATCH_CLI_VERSION=0.3.0 curl -fsSL \
  https://github.com/sonpiaz/watch-cli/releases/download/v0.3.0/install.sh | bash

Or from a clone:

git clone https://github.com/sonpiaz/watch-cli ~/.watch-cli
cd ~/.watch-cli && ./install.sh

The installer checks for yt-dlp, ffmpeg, jq, curl, python3 and symlinks the commands into ~/.local/bin. On macOS:

brew install yt-dlp ffmpeg jq

On Debian/Ubuntu:

sudo apt install yt-dlp ffmpeg jq python3 curl

Optional install flags

./install.sh --with-skill   # also drop SKILL.md into ~/.claude/skills/watch-cli/
./install.sh --with-mcp     # print the npm install hint for the MCP stdio server

• --with-skill copies the portable SKILL.md into ~/.claude/skills/watch-cli/ so Claude Code picks up watch-cli as a skill on next start. The same file works in OpenClaw and hermes-agent — see SKILL.md.
• --with-mcp prints the manual install line for @sonpiaz/watch-cli-mcp, the MCP stdio server that exposes watch-cli to Claude Desktop, Cursor, Cline, Continue.dev, Windsurf, Zed, and any other MCP-capable client. The flag will auto-install once the package is published to npm.

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Setup

export KYMA_API_KEY=kyma-xxxxxxxx

Get a Kyma key at kymaapi.com — 60 seconds, no card.

Prefer bring-your-own-keys? Comment in GROQ_API_KEY and GOOGLE_AI_KEY in .env.example and watch-cli falls back to direct provider calls.

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Why Kyma

watch-cli uses Kyma as its AI backend. A few things you get for free:

• One key, every model in this CLI. watch-cli calls Kyma using capability aliases (transcribe, audio-understand). When Kyma swaps in a better model behind the alias, your scripts keep working unchanged.
• Per-call cost in the response. Every transcribe gives you a real number, not an end-of-month dashboard surprise.
• Auto-fallback across providers. If the underlying audio provider is throttling or down, Kyma routes through another. Your script never sees the outage.
• Free credit at signup. About 9 hours of audio at the default rate. Enough to know if you like it before you spend a cent.

The badges above pull live from api.kymaapi.com/api/stats, so the model count and free-credit number stay current without a watch-cli release.

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Commands

watch <url> [frame-count] [--cookies <file>]
  Orchestrator. Downloads, extracts frames, transcribes — one block out.

dl-video <url> [out-dir] [--cookies <file>]
  Just download the video. Returns the local mp4 path.

extract-frames <video> [count] [out-dir]
  Pull N evenly-spaced JPG frames. Default 8.

transcribe <audio-or-video> [language]
  Speech-to-text. Auto-extracts audio from video first.

audio-q <audio-or-video> "<question>"
  Audio scene Q&A — tone, music, SFX, language, emotion.
  Beyond pure transcription.

models [--all]
  List audio models available on Kyma (live, no hardcoded list).
  --all to see every Kyma SKU (text + image + video + audio).

How transcribe and audio-q stay current

The scripts call Kyma using the transcribe and audio-understand aliases, not raw model IDs. When Kyma swaps the underlying model (Whisper v4, Voxtral, a faster ASR), watch-cli keeps working without an update — the alias points to whichever model is current. Run watch-cli models any time to see what's behind the alias today.

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Login-walled videos

Most YouTube / TikTok / Reddit / Vimeo / public X work without setup. LinkedIn, private X posts, and Facebook need a session.

watch-cli auto-detects cookies from any signed-in browser (Chrome → Firefox → Safari → Edge → Brave → Chromium). Just sign in normally and re-run.

For servers / CI without browsers, pass a manual cookies file:

watch <url> --cookies ~/cookies.txt

Full setup walkthrough: docs/cookies.md.

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Use with Claude Code (or any agent)

You have access to a `watch` command that takes a URL and returns
a video, 8 frames, and the transcript. Read the frames as images and
the transcript as text — that's enough to "watch" any social video.

The output block is structured so an agent can parse it without help: VIDEO: line, FRAMES: block (one path per line), TRANSCRIPT: block.

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Prompt library

Beyond the generic prompt above, five copy-paste prompts in prompts/ turn watch output into a specific artifact:

Goal	File
Coding walkthrough → working project	implement-from-video.md
System talk → interactive architecture diagram	extract-architecture.md
UI / motion demo → working React component	clone-ux.md
Paper / research talk → runnable notebook	paper-to-code.md
Long tutorial → step-by-step cheat sheet	tutorial-walkthrough.md

Paste the chosen prompt above the watch output, hand the whole thing to your agent.

Use as a Claude Code skill

Drop skills/watch-cli/ into your ~/.claude/skills/ folder and the agent will pick up /watch <url> as a first-class command, including the prompt library above.

mkdir -p ~/.claude/skills
cp -r skills/watch-cli ~/.claude/skills/

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How it works

URL ──▶ yt-dlp ──▶ video.mp4 ──┬──▶ ffmpeg ──▶ frames/*.jpg
                                │
                                └──▶ ffmpeg ──▶ audio.mp3 ──┬──▶ Kyma /v1/audio/transcriptions
                                                            │     (Whisper Large v3 Turbo, 228× realtime)
                                                            │
                                                            └──▶ Kyma /v1/audio/understand
                                                                  (Gemini 3 Flash audio — tone/music/SFX)

Each step is a primitive. None of them needs a vision LLM.

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Show what you build

Built something cool from a video? Drop it in Discussions under Show and tell. Post the source URL, the prompt you used, and your artifact. Curated highlights make it back into the README.

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Limitations and cost

Watch-cli is fast and cheap because it composes primitives instead of calling a video LLM. The tradeoffs are honest.

Cost per video

Transcription is the only paid step. Frame extraction is local ffmpeg, free.

Video length	Transcribe cost
5 minutes (tweet, short demo)	~$0.005
1 hour (LinkedIn talk, podcast)	~$0.05
2 hours (conference talk)	~$0.11

Free credit at Kyma signup covers about 9 hours of transcribe. A BYOK path is available — see .env.example.

What works well

• Talking-head content: tutorials, conference talks, lectures, walkthroughs
• Architecture and system diagrams shown for at least 3 seconds
• Code that stays on screen long enough to read
• ~95 languages (anything Whisper v3 turbo supports)

What works poorly

• Music videos, action movies, fast-cut content. Eight evenly-spaced frames miss key moments. Bump count: watch <url> 24.
• Editor sessions that scroll fast through code. Same fix.
• Audio with heavy background music and overlapping speakers. Transcript quality drops. Use audio-q for a scene description instead.
• Videos longer than ~2 hours. The transcribe provider has a 25MB audio cap. Watch-cli auto-downsamples but a 3-hour talk may still exceed. Workaround: split via ffmpeg -ss before piping.

What does not work yet

• Region-locked videos (some YouTube, TikTok). yt-dlp returns an error; watch-cli surfaces it.
• Live streams. Download finishes only after the stream ends.
• Silent screencasts. Transcribe returns empty. Increase frame count and use audio-q for any sound design instead.

Frame count guidance

Video type	Recommended frame-count
Short tweet / clip (<2 min)	4 to 8 (default)
Standard tutorial / talk (5–20 min)	8 to 16
Long talk / lecture (20–60 min)	16 to 24
Conference talk / multi-hour (>1 hr)	24 to 32
Fast-cut or dense UI demo	Double the recommendation for that length

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License

MIT. © 2026 Son Piaz.