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 ad-management-intakegit 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/ad-management-intake)<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/ad-management-intake"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/ad-management-intake/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/ad-management-intake"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/ad-management-intake.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 | $0.00104 | $0.04223 |
| Opus 5.5 | $0.00042 | $0.01689 |
| Sonnet 5.5 | $0.00021 | $0.00845 |
| Haiku 4.5 | $0.00010 | $0.00422 |
Grade A, and why
ad-management-intake 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 22d 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 — 298 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ad Management Intake
What this does. Someone says "I want to run ads on Meta." A good agency would not hand them a twenty-field form. It would read their website first, come back with "here's what I think your business looks like", and ask only the few things it cannot know and cannot afford to get wrong. This skill is that conversation.
The one rule. Ask a question only when both are true:
- It cannot be inferred. The brand's research, its product catalog and its past campaigns do not answer it.
- A wrong guess costs something real. Assuming wrong would change money (budget, price, what a customer is worth), claims (what an ad may say), or legal exposure (regulated words, restricted categories, audiences that must be excluded).
Everything else is an assumption, said out loud: "I'm assuming X, Y and Z. Tell me if any of these are wrong."
Limits. At most 4 questions in one turn. At most 5 in total for a brand whose research covers the business, up to 20 for a brand with little or none. Plain business language throughout. No ad jargon unless the user used it first.
Where this skill's files are
Paths such as references/business-types.md are relative to this skill's own folder, not to
your working directory (in GooseWorks: agent-config/skills/ad-management-intake/). The harness
docs contract ships with the meta-ad-manager skill, installed beside this one: its
contract/RULES.md and contract/templates/.
Choose one mode
- GooseWorks when the GooseWorks tools are callable (the in-app coworker, or any agent with the GooseWorks MCP). Read references/gooseworks-adapter.md.
- Local otherwise (a direct Meta host or planning only: intake never touches Meta). Read references/local-adapter.md.
"The adapter" below means the file for the mode you chose.
Inputs
- The user's opening message — what they want ("more trial signups", "sell the new bundle").
- The brand's research — positioning, audience, products, pricing, competitors, past ads. The adapter says where it comes from. This skill never redoes research that exists.
- The product catalog, when there is one — products are referenced by id and never re-described.
- Existing campaigns and their briefs, and anything already in the
ads/docs. - Mode — progressive (default) or all at once (the user asked to answer everything in one go).
What ships with it
5 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.
- 22d ago First seen · 298 lines · 104 tokens per session scan A 63884beef146
ad-management-intake is a skill published in the GitHub repository gooseworks-ai/goose-skills (1,254 stars, last pushed today), licensed MIT. It adds 104 tokens to every session and 4,223 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 2026-09-26.
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