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 goose-videogit 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/goose-video)<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/goose-video"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/goose-video/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/goose-video"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/goose-video.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00033 | $0.01076 |
| Opus 5.5 | $0.00013 | $0.00430 |
| Sonnet 5.5 | $0.00007 | $0.00215 |
| Haiku 4.5 | $0.00003 | $0.00108 |
Grade A, and why
goose-video 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 today.
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 — 43 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GooseWorks video
Installed entry: catalog_fetch type skill goose-video on this connection and follow it. Already fetched this run: no repeat.
For a video already started, call video_read and follow its next_step.
New videos default to a real reference ad remade for the brand (custom); ready-made styles are the fallback. Start with get_started (wants video, request). Cards prove no pick or viewing.
- The request. If they haven't said what the video is about, ask in one short question.
- The brand. Ask which brand only when brand_read lists several; with one, use it.
- The product. Before reference or style choices, ask which product or service to advertise, even with only one saved. Use the product card, or ask by name in chat, and wait. Only their stated choice settles it; a brand name does not. Reuse an explicit pick or request for none; find ids with brand_read, [] for none.
- Research. Read the kit, learnings and selected products with brand_read, then knowledge_search. Verify product size and use from facts and size-reference photos before planning. Note what to say and avoid.
- The reference. Their own video ad: video_create with style custom, request, products, its reference_url and a new UUID idempotency_key, then the custom rule. Otherwise, custom asked or not, video_references (action find) with the request and products ([] for none); free, and it reuses the saved list unless they want new ideas (fresh: true). Show it as the answer says; they pick.
- The pick. video_references (action pick) with candidate_id, request, products and a new UUID idempotency_key starts the custom video. Then the custom rule.
- Styles, only if no found ad fits or they ask for a style or template: video_formats with the request and products; they pick. Then video_create with that style and the products.
A supplied video reference comes first
Watch the whole video with sound before choosing a style or writing a custom plan, or use the server's measured reference profile. URLs, titles, thumbnails and frames are not viewing. Log the hook, scene order, pace, speech, framing, product use, text and ending with timestamps. Unreadable essentials need an accessible video or their brief-only choice; never claim a viewing. Styles vs a reference: fetch plausible templates and compare plan_note, scenes, duration, cast, sound and assets; titles and rankings prove no fit. Custom route (a picked or supplied reference, or explicit custom): catalog_fetch type skill make-custom-video on this connection before any project write and follow it. Keep their mechanics and assets. If anything is unavailable, say so and keep the brief; never run a template's renderer as custom, import the reference as a finished video, use retired actions or switch environments. Fetching a skill proves no execution capability.
What ships with it
1 file 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.
- today First seen · 43 lines · 33 tokens per session scan A c3d58e5a3158
goose-video is a skill published in the GitHub repository gooseworks-ai/goose-skills (1,265 stars, last pushed yesterday), licensed MIT. It adds 33 tokens to every session and 1,076 once invoked, about $0.0001 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 2026-10-26.
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