Agent skill

Claude Watch

by devinilabs in devinilabs/claude-watch

Watch a tutorial or lecture video (URL or local path) and produce structured study notes.

MITAuto-check: notesMedia & Creative

Install Claude Watch

skills CLI
$ npx skills add devinilabs/claude-watch --skill claude-watch -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install devinilabs/claude-watch claude-watch --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
claude-watch
GitHub stars
117
Token cost
~1.6k tokens
SKILL.md length
678 words
Files
41 (incl. scripts)
Skills in repo
1
Repo updated
First seen
Licence
MIT

At a glance

Watch a tutorial or lecture video (URL or local path) and produce structured study notes.

  • Works in 3 steps: Title and slug → Number of sections + key concepts → Path to the notes file
  • Tasks that involve Study guides and flashcards
  • SKILL.md covers Step 0 — Setup preflight…, When to use, How to invoke and Notes template (non-negotiable…, plus 5 more sections
  • Runs Python and Shell scripts from its folder; calls python3 and brew

What it does

Claude Watch is an agent skill from devinilabs/claude-watch. Watch a tutorial or lecture video (URL or local path) and produce structured study notes. Downloads with yt-dlp, detects scene changes with ffmpeg, pulls a timestamped transcript (captions or Whisper API fallback), and writes a section-by-section markdown notes file with embedded screenshots to ~/claude-watch/library/<slug/.

Its SKILL.md is about 1.6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 47 other files, including scripts (for example `.claude-plugin/marketplace.json`, `.claude-plugin/plugin.json` and `.codex-plugin/skill.json`).

It sits in Media & Creative, covering Study guides and flashcards, Video production and Note-taking. It works with FFmpeg and Whisper. The repository describes itself as: Turn any tutorial or lecture video into structured study notes — scene-aware frames, persistent library, Claude-vision OCR. The licence is MIT.

When your agent uses it

  • Tasks that involve Study guides and flashcards
  • Tasks that involve Video production
  • Tasks that involve Note-taking

Example prompts

  • “/claude-watch”

Requirements

  • Python 3
  • A Bash shell
  • Pre-approved tools (allowed-tools): Bash, Read, Write, AskUserQuestion

Workflow steps

3 steps, taken from the first numbered list in SKILL.md.

  1. Title and slug
  2. Number of sections + key concepts
  3. Path to the notes file

What it can do on your machine

Read from SKILL.md and the folder at commit ade7d61. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Bash
    • Read
    • Write
    • AskUserQuestion

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/ (Python and Shell, from the files we listed), which the agent can run.

    Shell commands in SKILL.md call:

    • python3
    • brew

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Claude Watch loads about 1.6k tokens when it runs. Until then it costs about 85 tokens; SKILL.md has 678 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~85
When it runs · the whole SKILL.md, loaded when a task matches
~1.6k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NoteMentions a .env fileSKILL.md:30
    ds. It scaffolds `~/.config/claude-watch/.env` (mode 0600) with commented placeholders.
  • NoteMentions a .env fileSKILL.md:32
    and write it to `~/.config/claude-watch/.env`. If they don't want to, run with `--no-whisper`; videos without native ca
  • NoteMentions a .env fileSKILL.md:146
    - Reads/writes `~/.config/claude-watch/.env` (mode 0600) for keys
  • NotePre-approves every shell command (allowed-tools: Bash)SKILL.md
    allowed-tools: Bash, Read, Write, AskUserQuestion

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from devinilabs/claude-watch at commit ade7d61, republished under its MIT licence (© devinilabs). 678 words, ~1,639 tokens.

Download SKILL.mdSave it as .claude/skills/claude-watch/SKILL.md (or your agent's skills folder). This skill also uses 40 other files; get the full folder from GitHub.
name
claude-watch
description
Watch a tutorial or lecture video (URL or local path) and produce structured study notes. Downloads with yt-dlp, detects scene changes with ffmpeg, pulls a timestamped transcript (captions or Whisper API fallback), and writes a section-by-section markdown notes file with embedded screenshots to ~/claude-watch/library/<slug>/.
allowed-tools
Bash, Read, Write, AskUserQuestion
argument-hint
<video-url-or-path> [topic-or-question]
homepage
https://github.com/devinilabs/claude-watch
repository
https://github.com/devinilabs/claude-watch
license
MIT
user-invocable
true

/claude-watch — Claude turns a video into study notes

You don't have a video input. This skill gives you one and turns each viewing into a saved notes artifact.

Step 0 — Setup preflight (silent on success)

Run on every /claude-watch invocation:

bash
python3 "${CLAUDE_SKILL_DIR}/scripts/setup.py" --check

Exit codes: 0 ready (silent — proceed), 2 missing binaries, 3 missing API key, 4 both. On non-zero, run the installer:

bash
python3 "${CLAUDE_SKILL_DIR}/scripts/setup.py"

On macOS this auto-brew installs ffmpeg + yt-dlp. On Linux/Windows it prints the right commands. It scaffolds ~/.config/claude-watch/.env (mode 0600) with commented placeholders.

If a Whisper key is still missing afterwards, use AskUserQuestion to ask whether the user has a Groq key (preferred — cheaper, faster) or an OpenAI key, and write it to ~/.config/claude-watch/.env. If they don't want to, run with --no-whisper; videos without native captions will come back frames-only.

When to use

  • User pastes a tutorial / lecture / talk URL and asks to study it
  • User points at a local screen recording or video and wants notes
  • User types /claude-watch <url-or-path> [topic]

How to invoke

Step 1 — parse input. Separate the source (URL or path) from any topic the user mentioned. The topic shapes which sections you emphasize in the notes — pass it through to your synthesis, not to the script.

Step 2 — run the watch script.

bash
python3 "${CLAUDE_SKILL_DIR}/scripts/watch.py" "<source>"

Optional flags:

  • --start T / --end T — focus on a section (SS, MM:SS, or HH:MM:SS)
  • --max-frames N — lower budget (default 80)
  • --resolution W — bump frame width to 1024 px when on-screen text is tiny
  • --scene-threshold X — sensitivity (default 0.30; raise for fewer cuts, lower for more)
  • --max-gap S — coverage floor in seconds (default 45)
  • --whisper groq|openai — force backend
  • --no-whisper — disable Whisper entirely
  • --out-dir DIR — override library root

Step 3 — read every frame. The script ends with a structured === frames === block listing each frame's path and timestamp. Read them all in parallel — they render as images in your context.

Step 4 — load the transcript. The === transcript === block points to transcript.json (or transcript.window.json for focused mode). Read it — it's a list of {t_start, t_end, text, speaker_break}.

Step 5 — write notes.md to the library directory. Use the strict template below. Save to <library_dir>/notes.md. Then print a 3-line summary to chat:

  1. Title and slug
  2. Number of sections + key concepts
  3. Path to the notes file

Do not delete the library dir. It is the artifact.

Notes template (non-negotiable structure)

markdown
# <Video Title>

**Source:** <URL or path>  ·  **Duration:** MM:SS  ·  **Watched:** YYYY-MM-DD

## TLDR
<3-4 sentences: what the video is about and the single most important takeaway.>

## Key Concepts
- **<concept>** — <one-line definition> · `[t=MM:SS]`
- ...

## Notes

### [t=00:04] <Section title you derive from on-screen + spoken content>

![](frames/0001_t00-04.jpg)

**On screen:** <Transcribe / describe the slide, code, diagram. If code, transcribe verbatim.>

**Said:** <Relevant transcript excerpt for this scene, lightly cleaned.>

**Synthesis:** <Your connection — what this section is teaching, how it links to prior section.>

### [t=00:31] <next section>
...

## Code & Commands
<every code-on-screen frame's content as a runnable fenced block, language-tagged, with [t=MM:SS] back-link>

```python
# [t=03:45]
def forward(x):
    return x @ W + b
```

## Diagrams Referenced
- `[t=02:10]` — <one-line description of the diagram in frame 0008>
- ...

## Open Questions
- <things mentioned but not fully covered, or follow-ups to explore>
Show full SKILL.md (296 more words)Show less

Rules baked into the template

  • One scene = one section. Use the t=MM:SS from each frame as the section anchor.
  • Adjacent scenes that are clearly the same topic can be merged. When you do, mention it parenthetically: (merged scenes at t=02:10 and t=02:42)
  • Code blocks must be fenced with the right language tag, transcribed verbatim from the frame.
  • The "On screen" block is required even for title slides. Keeps the structure parallel.
  • Timestamps are absolute (real video timeline) — for YouTube sources, a viewer can paste <URL>&t=<seconds> to jump there.

Re-runs

If the user re-watches the same URL, the script reuses the cached download, transcript, and scenes. Only frames + notes regenerate. To force a full re-run, delete <library_dir>/meta.json first.

Failure modes

  • Setup preflight non-zero → run setup.py, then ask for a key via AskUserQuestion.
  • No transcript → script emits transcript_source: none. Generate notes frames-only and tell the user.
  • Long video sparse-scan warning → offer to re-run with --start/--end focused on the part the user cares about.
  • Whisper failure → retry with --whisper openai (if Groq failed) or vice versa.

Token budget

Frames dominate cost (~50-80k input tokens for 60 frames at 512 px). Transcripts are cheap. --resolution 1024 quadruples per-frame cost — only when the user must read tiny on-screen text.

If the user asks a follow-up about a video you already watched in this session, do NOT re-run the script. The library directory is on disk; re-Read only the frames you need.

Security

  • Runs yt-dlp, ffmpeg, ffprobe locally
  • Sends extracted mono 16 kHz audio to Groq (preferred) or OpenAI Whisper API only when captions are missing
  • Reads/writes ~/.config/claude-watch/.env (mode 0600) for keys
  • Persists artifacts to ~/claude-watch/library/<slug>/ — review the directory after first run if you're cautious
  • Does NOT log or transmit API keys, video files, or the original URL outside the audio-to-Whisper call

© devinilabs, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 40 other files (scripts) in the repository root of devinilabs/claude-watch.

  • SKILL.md
  • .claude-plugin/marketplace.json
  • .claude-plugin/plugin.json
  • .codex-plugin/skill.json
  • .gitattributes
  • .github/workflows/release.yml
  • .gitignore
  • CHANGELOG.md
  • LICENSE
  • README.md
  • commands/claude-watch.md
  • conftest.py
  • hooks/hooks.json
  • hooks/scripts/session_start.sh
  • … and 27 more

Open the folder on GitHubat commit ade7d61

Compare with similar skills

Claude Watch next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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Bggg Tiktok Readvideobinggandata/bggg-skills604—~1.6kAutomated safety check: PassMIT
Watch Videocoreyhaines31/makerskills850—~3.8kAutomated safety check: PassMIT
Video Clippergooseworks-ai/goose-skills1.2k1 repos~3.1kAutomated safety check: NotesMIT
Video EditingLeoYeAI/openclaw-master-skills2.2k—~2.7kAutomated safety check: NotesMIT

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Works with

Questions about Claude Watch

What does Claude Watch do?

Watch a tutorial or lecture video (URL or local path) and produce structured study notes. Claude Watch is an agent skill from devinilabs/claude-watch. Watch a tutorial or lecture video (URL or local path) and produce structured study notes.

When should I use Claude Watch?

Claude Watch fits situations like: tasks that involve Study guides and flashcards; tasks that involve Video production; tasks that involve Note-taking.

How do I install Claude Watch in Claude Code?

Run `npx skills add devinilabs/claude-watch --skill claude-watch -a claude-code`. Or copy the skill folder (the devinilabs/claude-watch repository) into .claude/skills/claude-watch in your project. Claude Code loads it when a task matches its description.

How do I install Claude Watch in Codex?

Run `npx skills add devinilabs/claude-watch --skill claude-watch -a codex`. Or copy the skill folder (the devinilabs/claude-watch repository) into .agents/skills/claude-watch in your project. Codex loads it when a task matches its description.

Can I use Claude Watch in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add devinilabs/claude-watch --skill claude-watch -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/claude-watch, .gemini/skills/claude-watch, .github/skills/claude-watch and .opencode/skills/claude-watch in your project.

What does Claude Watch need to run?

Going by SKILL.md and its folder, Claude Watch needs Python and a shell for the scripts in its folder and the command-line tools its instructions call (python3 and brew). Our summary lists: Python 3; A Bash shell. Its frontmatter pre-approves these tools: Bash, Read, Write, AskUserQuestion.

Does Claude Watch access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Claude Watch safe to install?

Our automated static check of SKILL.md found notes only (mentions a .env file; pre-approves every shell command (allowed-tools: bash)), nothing it rates as a warning. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Claude Watch use?

Claude Watch is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Claude Watch use?

About 1.6k tokens (SKILL.md is roughly 6.6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Claude Watch?

Skills that share tags, products or a category with Claude Watch: Shorts (AgriciDaniel/claude-shorts, 219 stars), Bggg Tiktok Readvideo (binggandata/bggg-skills, 604 stars), Watch Video (coreyhaines31/makerskills, 850 stars) and Video Clipper (gooseworks-ai/goose-skills, 1.2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Claude Watch?

devinilabs (a GitHub user) maintains it in devinilabs/claude-watch, which has 117 GitHub stars. The repository was last updated on May 4, 2026.

Source: devinilabs/claude-watch on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.