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 | $0.00040 | $0.00697 |
| Opus 5.5 | $0.00016 | $0.00279 |
| Sonnet 5.5 | $0.00008 | $0.00139 |
| Haiku 4.5 | $0.00004 | $0.00070 |
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 2d 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 — 70 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Human version
Report what the actual file shows and sounds like, with timestamps. Keep sampled frames, moving-picture review, listening and transcription separate. Missing a capability stays explicit; observations are not an approval verdict.
Agent version
Inputs and modes
Accept the video, optional timestamp ranges, sampling rate, frame budget, resolution, audio dimensions and focus. Defaults describe whole-file scope, not completed review. Read the parameter reference for how to partition work without omitting intervals.
Before reporting, resolve exact source/version, checksum, duration and actual reviewer capabilities. Returned audio bytes or transcription do not establish perception. Use complete normal-speed moving-picture and sound review for a full audiovisual observation. Add sampled frames for scene/text evidence and suitably dense, original-resolution motion/mouth inspection for suspicious intervals. Sparse frames cannot certify action continuity or fine lip-sync. There is no universal two-fps ceiling for targeted inspection; remain within source rate and available tools.
Workflow
- Read the exact file and its real duration. Resolve all requested ranges and keep actual decoded source timestamps. Never substitute another master or thumbnails.
- Observe the complete requested moving-picture ranges at normal speed when capable. Record uncovered ranges/capabilities. A frame budget cannot silently omit a scene; divide work into explicitly covered passes when needed.
- Inspect sampled or targeted frames at sufficient resolution for the question. Record sampling density, actual frame times and coverage separately from playback.
- Hear requested sound, including speech, music and effects. Listen to isolated speech when available and the mix. Transcribe speech as supporting word evidence; unavailable ASR leaves transcription incomplete but does not erase actual listening. Unavailable listening cannot be cleared by ASR, waveform or inclusion flags.
- Report observed picture/action/text and heard speech/sound with exact intervals, source evidence and observation method. Label transcript-only words as ASR output, not heard speech. Corroborate suspicious ASR against audio; silence can yield false words. Do not invent music taste, voice character or lip-sync observations.
- Save timestamped observations and the coverage manifest. Keep each duty complete, partial or unavailable, with covered ranges and remaining gaps. No edits, quality verdicts or recommendations are supplied by this primitive.
What ships with it
6 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.
- 2d ago Changed b98df937ca44
- 11d ago Changed · +4 lines · +4 tokens per session e5f64b8971a6
- 19d ago Changed · +2 lines 6b6357785f0e
- 1mo 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 (1,271 stars, last pushed yesterday), licensed MIT. It adds 40 tokens to every session and 697 once invoked, about $0.0002 per session on Opus 5.5. 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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