Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/oxbshw/watch-skill/watchnpx skills add oxbshw/watch-skill --skill watchgit clone --depth 1 https://github.com/oxbshw/watch-skillWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/oxbshw/watch-skill/watch)<a href="https://agentmods.dev/skills/oxbshw/watch-skill/watch"><img src="https://agentmods.dev/badge/skills/oxbshw/watch-skill/watch.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00071 | $0.01340 |
| Opus 5 | $0.00036 | $0.00670 |
| Sonnet 5 | $0.00014 | $0.00268 |
| Haiku 4.5 | $0.00007 | $0.00134 |
Grade A, and why
watch scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 125 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/watch (Watch Skill)
You don't have a video input; this skill gives you one. It is a thin wrapper
around the watch-skill CLI — all logic lives in the engine, so this skill
works identically on every harness (Claude Code, Codex, Cursor, ...).
This is a drop-in upgrade of the classic claude-video /watch skill:
same invocation shape, plus a persistent index (ask answers follow-ups
without re-processing), OCR on frames, scene-aware sampling with perceptual
dedup, local Whisper (offline by default, no API key needed), and THE LOOP
(capture -> critique -> fix -> re-capture) for iterating on your own output.
Step 0 — Preflight (first invocation per session)
watch-skill doctor --json
- Exit 0 → proceed silently. Do NOT announce that setup is fine.
- Non-zero → the JSON lists each failing check with a
fix.doctorauto-bootstraps ffmpeg and yt-dlp into a managed bin dir on Windows/macOS/ Linux; re-run once after it reports fixes. Only involve the user when a check still fails after remediation. - If
watch-skillitself is not on PATH:pip install watch-skill(oruv tool install watch-skill), then re-run the doctor.
No API key is required for acquisition, transcription, OCR, indexing, or
search: transcription falls back to local faster-whisper. Visual synthesis
and verification can use the user's existing Anthropic, OpenAI, Gemini, or
OpenRouter key, or an optional local Ollama model. The agent and provider are
independent; see the configuring-vision skill. Cloud STT is opt-in
(--cloud-stt) and only ever uploads extracted mono audio — the video file
never leaves the machine.
Step 1 — Watch
Parse the user input into source + optional question, then:
watch-skill watch "<source>" [--start T --end T] [--max-frames N] [--transcript-only]
- Any yt-dlp-supported site (1800+), direct media URLs, HLS/DASH manifests
(
--duration 60bounds live streams), and local files all work. --start/--end(SS,MM:SS,HH:MM:SS) switch to dense focused sampling of that window — use for "what happens at 2:30?" questions and for any video over ~10 minutes when the user cares about one section.--timestamps T1,T2,...pins frames at transcript-flagged moments ("look here", "as you can see") that visual selection may miss.--transcript-onlyskips frames entirely (fastest; no video download when captions exist).--max-frames Ntightens the token budget (default: duration-tiered, hard cap 100, max 2 fps).
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 6d ago First seen · 125 lines · 71 tokens per session scan A 5b86badd6ad0
watch is a skill published in the GitHub repository oxbshw/watch-skill (333 stars, last pushed yesterday), licensed MIT. It adds 71 tokens to every session and 1,340 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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