Goose Skills is a library of workflows and data APIs that lets coding agents handle growth and go-to-market work such as advertising, social media, content, SEO, lead generation, and customer research. It is intended for teams using Claude Code, Cursor, Codex, and similar agents. The catalogue entries are its reusable skills.
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 skills add gooseworks-ai/goose-skills --skill watchgit clone --depth 1 https://github.com/gooseworks-ai/goose-skillsWrote 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/gooseworks-ai/goose-skills/watch)<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/watch"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/watch/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/watch"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/watch.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00036 | $0.00954 |
| Opus 5 | $0.00018 | $0.00477 |
| Sonnet 5 | $0.00007 | $0.00191 |
| Haiku 4.5 | $0.00004 | $0.00095 |
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 8d 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 — 64 lines — stays where its author put it; the contents beside it link to each section on GitHub.
watch
Purpose
Look at a rendered video and report what is actually on screen and in the audio. By default it watches the entire video and considers visuals, voiceover, music, and sound effects. Callers can narrow the scope (specific timestamp ranges, lower frame rate, disable audio tracks) when they want a cheaper or more focused pass.
This is the observation primitive that watch-and-refine calls before deciding what to fix. Other review and editing skills can call it directly.
Inputs
video— path to a local video file. Required.ranges— optional list of[start, end]timestamps to watch. AcceptsSS,MM:SS, orHH:MM:SS. Defaults to the whole video.fps— frame sampling rate. Defaults to auto by duration (≤30s → 1–2 fps, 30s–1min → ~1 fps, 1–3min → ~0.5 fps, 3–10min → ~0.25 fps). Hard cap 2 fps.max_frames— hard cap on total frames sampled across all ranges. Default 100.resolution— frame width in px. Default 512. Bump to 1024 only when on-screen text legibility matters.include_voice— bool, defaulttrue. Transcribe spoken VO/dialogue.include_music— bool, defaulttrue. Describe music presence, swells, drops, gain relative to VO.include_sfx— bool, defaulttrue. Note sound effects, foley, transition stingers.focus— optional free-text prompt describing what to pay attention to (e.g. "watch the end card", "judge cut timing on the beat drop").
If all three audio flags are false, the skill runs frames-only and notes this in the manifest.
Workflow
- Validate
videoexists and is readable. Probe duration withffprobe. - Resolve
ranges: if empty, use[0, duration]. Reject ranges outside the file duration. - Resolve
fps: use caller value if provided, else auto-scale from total resolved range duration. Clamp at 2 fps. - Allocate the
max_framesbudget across ranges proportionally to range duration. - Extract frames with
ffmpegintoframes/at the resolvedfpsandresolution. - If any audio flag is true, extract the audio for the resolved ranges to a working WAV. Run a transcript pass when
include_voice=true; degrade to frames-only and flag a warning if no Whisper backend is available. - Compose
observation.md— a timestamp-keyed report. Each entry references the frame paths visible during that window plus any transcript line and audio notes (music/SFX) for the same window. Iffocusis set, lead each entry with what was observed about that focus. - Write
manifest.jsoncapturing the resolved inputs (ranges, fps, frame count, audio flags) and output paths.
What ships with it
7 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 8d ago First seen · 64 lines · 36 tokens per session scan A b8de719e55f5
watch is a skill published in the GitHub repository gooseworks-ai/goose-skills (1,202 stars, last pushed 11d ago), licensed MIT. It adds 36 tokens to every session and 954 once invoked, about $0.0002 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 2026-09-03.
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