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 work-with-creators-creator-vettinggit 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/work-with-creators-creator-vetting)<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/work-with-creators-creator-vetting"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/work-with-creators-creator-vetting/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/work-with-creators-creator-vetting"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/work-with-creators-creator-vetting.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 | $0.00110 | $0.00726 |
| Opus 5.5 | $0.00044 | $0.00290 |
| Sonnet 5.5 | $0.00022 | $0.00145 |
| Haiku 4.5 | $0.00011 | $0.00073 |
Grade A, and why
work-with-creators-creator-vetting 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 4d 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.
What it actually says
creator-vetting
Reads: finalists; the persona and goal; signed-off claims and never-says (to spot conflicting past claims); competitor list; ../work-with-creators/references/vetting.md; ../work-with-creators/references/data-sources.md.
Does, per finalist — at the depth set in discovery (full, light, UGC rubric, B2B branch; see section 0 of the reference). Free first: everything below is done from the public profile and post pages in the host browser (reference §15 says what each platform shows logged-out), labelled "public-page read ". What a page hides (views on some platforms, comments behind a login, audience countries, transcripts) is listed for the paid top-up, never guessed.
- Read the profile and the last 12–20 posts on the main platform; drop pinned, giveaway and too-fresh posts; split sponsored and organic.
- Compute median views, view rate, engagement on the right basis for the platform, sponsored ÷ organic, mean ÷ median, cadence.
- Read the visible comments on 2–3 posts in the free pass (50–100 across ~5 posts in a top-up); record the share of generic/bot-like comments, self-descriptions, purchase questions, language and place signals.
- Audience fit: evidence and a confidence level (high / medium / low), from what people say about themselves — quoted and counted. Audience countries only for reach/install roles, top five at most. UGC-for-ads: skip audience; judge hooks (transcripts), product handling, past brand-ad work, portfolio, turnaround, honesty risk; mark delivery "not watched" and record 2–3 video links for the human.
- Brand safety: one web name search per shortlisted pick is part of the free pass — always run it (" scam / apology / drama / controversy"); then ~12 months of captions and a few transcripts; a name search with "scam / apology / drama / controversy" (if no search ran, the verdict says "captions and comments only"); past claims the brand could never make.
- Conflicts: competitor partnership ads, competitor tags in the last 3–6 months.
- Regulated and minors checks per the reference; any fail → out.
- Write the verdict line (the shape at the end of the reference). Fails become near-misses with the number that failed.
Writes: a verdict per finalist with all numbers, vetted_on date and post links; near-misses with reasons.
Rules: no number, no verdict. Label all authenticity findings "signals, not verified". Never infer ethnicity, age, gender, skin tone or health from images or names. No business-contact lookups here — they wait until the human approves the names.
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.
- 4d ago First seen · 29 lines · 110 tokens per session scan A a38e55cb2c0d
work-with-creators-creator-vetting is a skill published in the GitHub repository gooseworks-ai/goose-skills (1,265 stars, last pushed today), licensed MIT. It adds 110 tokens to every session and 726 once invoked, about $0.0004 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 2026-10-21.
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