Watch a video (URL or local path) like an editor. Extracts scene-change frames, pacing metrics (cuts/min, shot length), and a dense 0-10s hook microscope; pulls transcript from captions or Whisper. Produces an ingest-ready `report.md` and, after answering the user, optionally auto-ingests the analysis into your Obsidian vault (configurable via `$WATCH_VAULT_DIR`) — tied to *why* the user watched it.
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Watch a video (URL or local path) like an editor. Extracts scene-change frames, pacing metrics (cuts/min, shot length), and a dense 0-10s hook microscope; pulls transcript from captions or Whisper. Produces an ingest-ready `report.md` and, after answering the user, optionally auto-ingests the analysis into your Obsidian vault (configurable via `$WATCH_VAULT_DIR`) — tied to *why* the user watched it.
argument-hint
<video-url-or-path> [why you're watching it]
allowed-tools
Bash, Read, AskUserQuestion
homepage
https://github.com/arcasilesgroup/agent-watch
repository
https://github.com/arcasilesgroup/agent-watch
author
Arcasiles Group — continuation of claude-watch by taoufik
license
MIT
user-invocable
true
/watch — your agent watches a video
You don't have a video input; this skill gives you one. A Python script downloads the video, extracts frames as JPEGs (one per detected shot via scene-change), gets a timestamped transcript (native captions first, then Whisper API as fallback), runs editorial pacing metrics, and microscopes the first 10 seconds at higher density. You then Read each frame path to see the images, combine them with the transcript to answer the user, fill the structured report.md, and offer to ingest the analysis into Taoufik's Second Brain.
What v2 does differently
Scene-change frame sampling — one frame per detected shot instead of uniform ticks. Cuts the frame budget on long videos while capturing every transition.
Editorial pacing metrics — cuts/min, mean shot length, motion (when available). Lets you reason about pacing the way an editor does.
Hook microscope — first 10s auto-runs at 2 fps + word-level Whisper. The single most leveraged 10 seconds of any video deserves dense treatment.
Structured report.md — every watch emits an ingest-shaped report at <workdir>/report.md with TL;DR, key moments, hook breakdown, editorial profile, quotable moments, entities, concepts, and transcript. Narrative sections are emitted as <!-- pending agent fill: ... --> markers — you fill them in before offering ingest.
Step 4.5 — Ingest gate — after answering the user, you ask once: "Want to ingest this into your Obsidian vault?" If yes, and a vault is detected, you read $VAULT_DIR/CLAUDE.md (if it exists) and run that vault's Ingest op against the report.
None of the above add new dependencies — pure ffmpeg + stdlib + the existing Whisper backend.
Configuration — finding the user's Obsidian vault
Steps 4.4 and 4.5 stage the report inside an Obsidian vault so the user can read it where they read everything else. Resolve the vault directory in this order — first hit wins, and the result is what $VAULT_DIR refers to everywhere below:
$WATCH_VAULT_DIR env var — if set and the path exists, use it. This is the user-controlled override.
~/Second brain/ — if it exists as a directory.
~/Documents/Obsidian/ — if it exists as a directory.
~/Obsidian/ — if it exists as a directory.
None found — skip Steps 4.4 and 4.5 entirely. Print one line in chat so the user knows what happened: . Suggest they set if they want auto-ingest.
📄 Report (no vault detected): <workdir>/report.md
WATCH_VAULT_DIR
A quick way to resolve it in bash inside the skill:
The vault's URL-name (for the obsidian:// URL scheme in Step 4.4) is the final path component — e.g. $HOME/Second brain → Second brain. URL-encode spaces as %20.
Step 0 — Setup preflight (runs every /watch invocation, silent on success)
Python interpreter: every python3 ... command in this skill is for macOS/Linux. On Windows, substitute python — the python3 command on Windows is the Microsoft Store stub and will not run the script.
Before every /watch run, verify that dependencies and an API key are in place:
This is a <100ms lookup. On exit 0, the script emits nothing — proceed to Step 1 without comment. Do NOT announce "setup is complete" to the user — they don't need a status message on every turn. The only acceptable user-visible output from Step 0 is when remediation is required.
On non-zero exit, follow the table:
Exit
Meaning
Action
2
Missing binaries (ffmpeg / ffprobe / yt-dlp)
Run installer
3
No Whisper API key
Run installer to scaffold .env, then ask user for a key
4
Both missing
Run installer, then ask for a key
The installer is idempotent — safe to re-run:
python3 "${CLAUDE_SKILL_DIR}/scripts/setup.py"
On macOS with Homebrew, it auto-installs ffmpeg and yt-dlp. On Linux/Windows, it prints the exact install commands for the user to run. It scaffolds ~/.config/watch/.env with commented placeholders at 0600 perms, and writes SETUP_COMPLETE=true once deps + a key are in place so the next session knows this user has already been through the wizard.
If an API key is still missing after install: use AskUserQuestion to ask the user whether they have a Groq API key (preferred — cheaper, faster), an OpenAI key, or a NaN Builders key (cloud.nan.builders → API Keys — free with community membership). Then write it into ~/.config/watch/.env — set the matching GROQ_API_KEY=..., OPENAI_API_KEY=..., or NAN_API_KEY=... line. If the user keeps keys in files (agent managers, secret stores), set WATCH_<NAME>_KEY_FILE in the .env instead of pasting the key. If they don't want to set up Whisper, proceed with --no-whisper and tell them videos without native captions will come back frames-only.
Structured mode (optional):python3 "${CLAUDE_SKILL_DIR}/scripts/setup.py" --json emits {status, first_run, missing_binaries, whisper_backend, has_api_key, config_file, platform} where status is one of ready | needs_install | needs_key | needs_install_and_key. Use this when you need to branch on specifics (e.g. "is this the user's very first run?" → first_run: true).
Within a single session, you can skip Step 0 on follow-up /watch calls — once --check returned 0, nothing about the environment changes between turns.
When to use
User pastes a video URL (YouTube, Vimeo, X, TikTok, Twitch clip, most yt-dlp-supported sites) and asks about it.
User points at a local video file (.mp4, .mov, .mkv, .webm, etc.) and asks about it.
User types /watch <url-or-path> [question].
Recommended limits
Best accuracy: videos under 10 minutes. Frame coverage scales inversely with duration.
Hard caps: 100 frames total and 2 fps. Token cost grows with frame count, so the script targets a frame budget by duration (and never exceeds 2 fps even when the budget would imply more):
If the user hands you a long video, consider asking whether they want a specific section before burning tokens on a sparse scan.
How to invoke
Step 1 — parse the user input. Separate the video source from any question the user asked. The question (or the user's prior stated interest) IS the intent — pass it to the script via --intent. Example: /watch https://youtu.be/abc what's the hook pattern? → source = https://youtu.be/abc, intent = what's the hook pattern?. If no question is given, use a brief inferred intent ("general summary") so the report's TL;DR has a lens. The intent shapes how the report's TL;DR and entity/concept sections get filled at Step 4 — same video with intent "pricing tactics" vs "editing style" produces different reports.
Step 2 — run the watch script. Pass the source verbatim. Do not shell-escape it yourself beyond normal quoting:
Pass --intent whenever you have any signal from the user about why they want this video — the question they asked, a stated goal, or a brief inferred summary. Empty --intent works but produces less-targeted report sections.
Optional flags:
--start T / --end T — focus on a section. Accepts SS, MM:SS, or HH:MM:SS. When either is set, fps auto-scales denser (see "Focusing on a section" below).
--max-frames N — lower the cap for tighter token budget (e.g. --max-frames 40)
--resolution W — change frame width in px (default 512; bump to 1024 only if the user needs to read on-screen text)
--fps F — override auto-fps (clamped to 2 fps max). Setting --fps disables scene-change sampling.
--out-dir DIR — keep working files somewhere specific (default: an auto-generated tmp dir)
--whisper groq|openai|nan — force a specific Whisper backend (default: prefer Groq, then OpenAI, then NaN)
--no-whisper — disable the Whisper fallback entirely (frames-only if no captions)
--no-scene-change — force uniform frame sampling (debug only; usually leave on)
When the user asks about a specific moment — "what happens at the 2 minute mark?", "zoom into 0:45 to 1:00", "the first 10 seconds" — pass --start and/or --end. The script switches to focused-mode budgets, which are denser than full-video budgets (still capped at 2 fps):
≤5s → 2 fps (up to 10 frames)
5-15s → 2 fps (up to 30 frames)
15-30s → ~2 fps (up to 60 frames)
30-60s → ~1.3 fps (up to 80 frames)
60-180s → ~0.6 fps (100 frames, capped)
Focused mode is the right call for:
Any moment/range the user names explicitly ("around 2:30", "the intro", "the last 30 seconds").
Any video longer than ~10 minutes where the user's question is about a specific part — running focused on the relevant section is far more useful than a sparse scan of the whole thing.
Re-runs after a full scan didn't have enough detail in some region.
Transcript is auto-filtered to the same range. Frame timestamps are absolute (real video timeline, not offset-from-start).
Examples:
# Last 10 seconds of a 1 minute video
python3 "${CLAUDE_SKILL_DIR}/scripts/watch.py" video.mp4 --start 50 --end 60
# Zoom into 2:15 → 2:45 at 3 fps (90 frames)
python3 "${CLAUDE_SKILL_DIR}/scripts/watch.py""$URL" --start 2:15 --end 2:45 --fps 3
# From 1h12m to the end of the video
python3 "${CLAUDE_SKILL_DIR}/scripts/watch.py""$URL" --start 1:12:00
Step 3 — Read every frame path the script lists. The Read tool renders JPEGs directly as images for you. Read all frames in a single message (parallel tool calls) so you see them together. The frames are in chronological order with a t=MM:SS timestamp so you can align them to the transcript.
Step 4 — answer the user, then fill the report. You now have three streams of evidence:
Frames — what's on screen at each timestamp
Transcript — what's said at each timestamp
report.md — structured artifact at <workdir>/report.md with <!-- pending agent fill: ... --> markers
First, answer the user's question in chat citing timestamps.
Then, fill in the pending markers in report.md using the Edit tool. Walk every <!-- pending agent fill: ... --> in order:
TL;DR — 3-5 bullets through the lens of the user's intent (read from the frontmatter)
Quotable moments — top 3-5 punchy, standalone lines from the transcript
Entities mentioned — people, companies, tools, places — formatted to match wiki/entities/ slugs (kebab-case, lowercase). Use [[wikilink]] style.
Concepts surfaced — frameworks, mental models, named patterns — short gist each
The fully-filled report.md is what gets ingested at Step 4.5. Do not skip the fill — empty markers won't ingest cleanly.
Step 4.4 — Stage to the Obsidian vault and open in Obsidian (when a vault is detected). After filling every marker, resolve $VAULT_DIR per the Configuration section. If no vault is detected, skip this step and emit 📄 Report (no vault detected): <workdir>/report.md in chat instead.
When $VAULT_DIR resolves:
Derive the slug now (do not wait for Step 4.5). Take the video title from report.md frontmatter, slugify (lowercase, ASCII-only, hyphens, max 60 chars), append -YYYY-MM-DD. Example: karpathy-claude-md-43k-installs-2026-05-24.
Create the staging dir:mkdir -p "$VAULT_DIR/raw/watched/<slug>".
Copy report.md + every hero frame (filenames in the report frontmatter under hero_frames:) into that dir. The report MUST live inside the vault for Obsidian to open it.
Open in Obsidian via URL scheme (macOS). The vault URL-name is the final component of $VAULT_DIR with spaces URL-encoded as %20:
VAULT_NAME=$(basename"$VAULT_DIR" | sed 's/ /%20/g')
open "obsidian://open?vault=${VAULT_NAME}&file=raw/watched/<slug>/report.md"
The file= value is the path relative to the vault root, no leading slash. Don't ask permission — the user has already opted in by running /watch.
Echo the vault-relative path in chat on its own line: 📄 Report (open in Obsidian): raw/watched/<slug>/report.md. So if Obsidian was closed / the URL handler missed, the user can still navigate to it manually inside the vault.
Rationale: the report is the leverage point of /watch. If the user reads everything in Obsidian, opening in Preview or VS Code defeats the purpose. Staging at 4.4 also means Step 4.5's "Yes / Stage" branches are no-ops on the copy step (the file is already in the vault); they only differ in whether the Ingest op runs.
Cleanup implication for Step 4.5: if the user picks "No, drop it" at 4.5 AND a vault was staged at 4.4, ALSO rm -rf "$VAULT_DIR/raw/watched/<slug>" since we pre-staged. Do NOT drop the vault copy if they picked Yes or Stage.
Step 4.5 — Offer ingest into the Obsidian vault.Skip this step entirely if no vault was detected at Step 4.4. Otherwise use AskUserQuestion once, with these options (do NOT skip if a vault was found unless the user explicitly said "don't ingest" before /watch ran):
Question: "Want to ingest this into your Obsidian vault?"
Yes — same angle ("")
Yes — different angle (user specifies in the notes field)
Stage to raw/watched/ for later
No, drop it
Routing based on response:
A. Yes (same or different angle):
Derive the slug: take the video title from report.md frontmatter, slugify (lowercase, ASCII-only, hyphens, max 60 chars), append -YYYY-MM-DD. Example: me-at-the-zoo-2026-05-24.
Confirm the staging dir exists at $VAULT_DIR/raw/watched/<slug>/ (Step 4.4 already created it).
The report + hero frames are already copied there from Step 4.4.
If "different angle": Re-edit the TL;DR + Entities + Concepts sections of the copied report to reflect the new angle the user specified, before running ingest.
If $VAULT_DIR/CLAUDE.md exists: Read it to refresh the Ingest op definition — that file is authoritative; this skill must not duplicate its steps. Execute the Ingest op against raw/watched/<slug>/report.md exactly as $VAULT_DIR/CLAUDE.md defines it.
If no $VAULT_DIR/CLAUDE.md exists: Run a generic ingest — read the report, identify entities + concepts, append a one-line entry to $VAULT_DIR/log.md (create if missing), and tell the user the report is staged at raw/watched/<slug>/report.md and they can wire up an Ingest op of their own.
Report back to the user in chat: which entity pages were touched (if any), the path to the staged report, and the log.md entry written.
B. Stage to raw/watched/ for later:
The staging from Step 4.4 already did the file copy.
Do NOT touch the wiki. Do NOT append to log.md.
Tell the user in chat: "Staged at $(basename $VAULT_DIR)/raw/watched/<slug>/. Run an Ingest op against it when you're ready."
C. No, drop it: proceed to Step 5 (cleanup) — and per the cleanup-implication note in Step 4.4, rm -rf "$VAULT_DIR/raw/watched/<slug>" to undo the pre-staging.
The "different angle" path is what makes /watch truly plug-and-play — the user can watch a video for one reason, then on the way out decide it's actually more useful for a different concept, and the resulting wiki entry reframes accordingly.
Step 5 — clean up. The script prints a working directory at the end. If you ingested (Step 4.5 path A), the hero frames + report.md are already copied to Second Brain — you can rm -rf the original workdir. If you staged (path B), same — the workdir copy is no longer needed. If the user picked "no, drop it" (path C) and isn't going to ask follow-ups, delete with rm -rf <dir>. If they might ask follow-ups, leave it in place.
Transcription
The script gets a timestamped transcript in one of two ways:
Native captions (free, preferred). yt-dlp pulls manual or auto-generated subtitles from the source platform if available.
Whisper API fallback. If no captions came back (or the source is a local file), the script extracts audio (ffmpeg -vn -ac 1 -ar 16000 -b:a 64k, ~0.5 MB/min) and uploads it to whichever Whisper API has a key configured:
Groq — whisper-large-v3. Preferred default: cheaper, faster. Get a key at console.groq.com/keys.
OpenAI — whisper-1. Fallback. Get a key at platform.openai.com/api-keys.
NaN Builders — whisper (= large-v3 on the community cluster, api.nan.builders). Free with a NaN membership key (NAN_API_KEY). Rate limits: 10 rpm, 25 MB per upload — the script chunks long audio into ≤110 s pieces automatically and re-aligns timestamps.
Keys live in ~/.config/watch/.env (or env vars). The script prefers Groq, then OpenAI, then NaN when several are set; override with --whisper openai / --whisper nan. Any other OpenAI-compatible endpoint (Ollama, faster-whisper, vLLM, a company gateway) is registrable via WATCH_EXTRA_PROVIDERS — see PROVIDERS.md. Use --no-whisper to skip the fallback entirely.
Failure modes and handling
Setup preflight failed → run python3 "${CLAUDE_SKILL_DIR}/scripts/setup.py" (auto-installs ffmpeg/yt-dlp via brew on macOS, scaffolds the .env). For API key, ask the user via AskUserQuestion and write it to ~/.config/watch/.env.
No transcript available → captions missing AND (no Whisper key OR Whisper API failed). Script prints a hint pointing to setup. Proceed frames-only and tell the user.
Long video warning printed → acknowledge it in your answer. Offer to re-run focused on a specific section via --start/--end rather than a sparse full-video scan.
Download fails → yt-dlp's error goes to stderr. If it's a login-required or region-locked video, tell the user plainly; do not keep retrying.
Whisper request fails → the error is printed to stderr (likely: invalid key, rate limit, or 25 MB upload limit on a very long video). The report will say "none available" for transcript. You can retry with --whisper openai if Groq failed (or vice versa). On --whisper nan: a 524 is the proxy timeout on long audio (the script already chunks >2 min files into ≤110 s pieces); a 429 is the 10 rpm whisper cap — the script backs off via Retry-After, so wait a minute before re-running.
Report has unfilled <!-- pending agent fill: ... --> markers → you skipped Step 4. Go back, read the report, fill every marker via Edit, then offer ingest. Never ingest a half-filled report — the Second Brain Ingest op will produce sparse/wrong entity pages.
Ingest fails partway → do not roll back. The Second Brain Ingest op is idempotent on re-run (it updates existing pages rather than duplicating). Tell the user what failed, leave the staged artifact in raw/watched/<slug>/, and they can re-run by saying "ingest the staged report at <slug>".
Token efficiency
This skill burns tokens primarily on frames. Order of magnitude:
80 frames at 512px wide is roughly 50-80k image tokens depending on aspect ratio.
The transcript is cheap (a few thousand tokens at most for a 10-minute video).
Bumping --resolution to 1024 roughly quadruples the image tokens per frame. Only do it when necessary.
If you already watched a video this session and the user asks a follow-up, do not re-run the script — you already have the frames and transcript in context. Just answer from what you have.
Security & Permissions
What this skill does:
Runs yt-dlp locally to download the video and pull native captions when the source supports them (public data; the request goes directly to whatever host the URL points at)
Runs ffmpeg / ffprobe locally to extract frames as JPEGs and, when Whisper is needed, a mono 16 kHz audio clip
Sends the extracted audio clip to Groq's Whisper API (api.groq.com/openai/v1/audio/transcriptions) when GROQ_API_KEY is set (preferred — cheaper, faster)
Sends the extracted audio clip to OpenAI's audio transcription API (api.openai.com/v1/audio/transcriptions) when OPENAI_API_KEY is set and Groq is not, or when --whisper openai is forced
Sends the extracted audio clip to the NaN Builders cluster (api.nan.builders/v1/audio/transcriptions) when --whisper nan is forced or NAN_API_KEY is the only key available
Writes the downloaded video, frames, audio, and an intermediate transcript to a working directory under the system temp dir (or --out-dir if specified) so the agent can Read them
Reads / creates ~/.config/watch/.env (mode 0600) to store the Whisper API key(s) and a SETUP_COMPLETE marker. As a fallback, also reads .env in the current working directory
Reads $VAULT_DIR/CLAUDE.md at orchestration time (only when ingest is requested and the file exists) to follow that vault's Ingest operation definition
Writes a structured report.md plus copies of hero frames into $VAULT_DIR/raw/watched/<slug>/ when a vault is detected at Step 4.4
When ingest is consented to: reads and writes pages under $VAULT_DIR/wiki/ (entities, concepts, sources, index.md) and appends to $VAULT_DIR/log.md — following the actions defined by the vault's Ingest op (or a generic fallback if no CLAUDE.md is present)
What this skill does NOT do:
Does not upload the video itself to any API — only the extracted audio goes out, and only when native captions are missing AND Whisper is not disabled with --no-whisper
Does not access any platform account (no login, no session cookies, no posting)
Does not share API keys between providers (Groq key only goes to api.groq.com, OpenAI key only goes to api.openai.com, NaN key only goes to api.nan.builders)
Does not log, cache, or write API keys to stdout, stderr, or output files
Does not persist anything outside the working directory and ~/.config/watch/.env (and Second Brain when ingest is consented to) — clean up the working directory when you're done (Step 5)
Does not write to the Second Brain without explicit user consent at the Step 4.5 prompt
Does not silently overwrite wiki claims — contradictions surface as WARN flags per the Ingest op contract
