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 where-to-start-situation-savegit 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/where-to-start-situation-save)<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/where-to-start-situation-save"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/where-to-start-situation-save/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/where-to-start-situation-save"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/where-to-start-situation-save.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 | $0.00052 | $0.00875 |
| Opus 5.5 | $0.00021 | $0.00350 |
| Sonnet 5.5 | $0.00010 | $0.00175 |
| Haiku 4.5 | $0.00005 | $0.00088 |
Grade A, and why
where-to-start-situation-save 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 — 45 lines — stays where its author put it; the contents beside it link to each section on GitHub.
situation-save
Input
The founder's answers (their words), the brand (id, slug, coworker_agent_id) unless there is no real brand, what the short look at the site showed, the constraint you named, the recommendation and whether they agreed, and today's date.
No real brand (none, or an empty placeholder): save nothing, and say so plainly ("I can't save these yet — there's no brand set up"). If a brand is set up later in this same conversation, run this atom then, before anything else asks the founder again.
Steps
-
The situation file.
gooseworks:file_readbrand/situation.md(brand coworker scope; no coworker → default workspace underbrands/<brand-slug>/, and say so once). Replace the "Latest" section; move the previous "Latest" under "History" (keep the last three). Format:# Situation — <Brand> Do this first, because: <the constraint in one clause> → <the agreed first move>. ## Latest — <YYYY-MM-DD> Founder said [owner, <date>]: sells <…> to <…>; where it sells <…>; stage <…>; goal <…> by <date>; can put in <…>/month and <…> h/week; does the work: <…>; next big date <…>; tried <…>; has <list / audience / network…>. Seen on the site [site, read <date>]: <offer, price, first reason to buy, list sign-up>. Unknown: <what they didn't know>. Agent's read [guess, <date>]: constraint <…>; first move <…> (<agreed | adjusted to … | not agreed>); not yet <…>; worked if <…>; come back when <…>. ## History <earlier "Latest" sections, newest first, at most three>Keep the founder's words, what the site showed and your read on separate lines. Never put a guess on the "Founder said" line. No plan names or credit sums.
-
Save it through the
save-to-brainskill (../save-to-brain/SKILL.md): pathbrand/situation.md, artifactSituation, titleSituation — <Brand> — <YYYY-MM-DD>, tags[where-to-start, situation],versioned: supersede. -
Profile fields (only those the founder stated, only clear matches to the listed values):
gooseworks:brand_updatewithbrand_id,knowledge_intent: "user_correction",user_statement= the founder's words that gave them, andpatch.onboarding:role;ad_spendonly if they said what they spend on ads today (question 4 asks what they can put in, which is not the same);goalsandplatformsonly where a listed value plainly matches. -
Check the answers. If a write failed, say in one line that it wasn't saved, and carry on.
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 · 45 lines · 52 tokens per session scan A 24656d07a7ab
where-to-start-situation-save is a skill published in the GitHub repository gooseworks-ai/goose-skills (1,262 stars, last pushed today), licensed MIT. It adds 52 tokens to every session and 875 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 2026-10-21.
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