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 make-custom-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/make-custom-video)<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/make-custom-video"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/make-custom-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/make-custom-video"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/make-custom-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 | $0.00022 | $0.01323 |
| Opus 5.5 | $0.00009 | $0.00529 |
| Sonnet 5.5 | $0.00004 | $0.00265 |
| Haiku 4.5 | $0.00002 | $0.00132 |
Grade A, and why
make-custom-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 — 57 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Human version
The owner approves script, then ingredients and budget here; cards and Studio are optional. Keep reference format, product and sound. GooseWorks checks the final.
Agent version
Non-negotiables
- Watch every reference end to end with sound, or use the server's measured reference profile, and save the evidence before any script.
- Write the script with write-video-ad-script (catalog_fetch it).
- Keep the reference's format: people stay people, demos stay demos, sound type stays. Otherwise list the change in changes_from_reference.
- The voice is the master clock. Never cut or retime sound apart from a talking face.
- Always real sound. No silent or tone tracks unless the owner asked for silence.
- Show the product correctly, at real size, from real photos.
- The review is listened or measured, never claimed.
- Under a deadline, deliver fewer correct videos. Never downgrade a format.
Read one file per call; if output says truncated, re-read in parts. Fetch make-custom-video here; inspect live schemas before writes. Keep this environment; no retired actions or reference-as-final import.
Load and resume
Fetch video-production-harness with catalog_fetch delivery archive; read SKILL.md and phase references; keep version/contentHash/files. Incomplete packages block work. Resume jobs with video_read; never resend instead of polling. Custom skips styles. Ask which product or service to advertise; wait even with one saved. A brand name is not a product choice. Reuse an explicit pick or request for none. With a reference, video_create once with style custom, request (the owner's own words), reference_url and a UUID idempotency_key reused on retries. Without one, run video_references find with the request and products and show the list; the owner's pick goes to video_references pick, which creates the project. Never use a template demo as reference. Keep requested mechanics/assets/products/campaign. Resume the same project, plan_revision, approvals and production_manifest.
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 · 57 lines · 22 tokens per session scan A 42e87c5fb9b4
make-custom-video is a skill published in the GitHub repository gooseworks-ai/goose-skills (1,265 stars, last pushed yesterday), licensed MIT. It adds 22 tokens to every session and 1,323 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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