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 save-learninggit 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/save-learning)<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/save-learning"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/save-learning/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/save-learning"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/save-learning.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 | $0.00077 | $0.02499 |
| Opus 5.5 | $0.00031 | $0.01000 |
| Sonnet 5.5 | $0.00015 | $0.00500 |
| Haiku 4.5 | $0.00008 | $0.00250 |
Grade A, and why
save-learning 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 — 71 lines — stays where its author put it; the contents beside it link to each section on GitHub.
save-learning
Learnings are the brand's short rules. Every playbook writes them through this atom and nowhere else. There are two server paths, and this atom picks between them:
- Brain path: when the Brand Brain tools are listed (
brand_knowledge_*). Save withbrand_knowledge_save, read back withbrand_knowledge_read/brand_knowledge_search. Exception: claims-gate's claim facts and marker always use the older path below. - Older path: otherwise (older servers), and always for claims-gate's claim facts. Save with
gooseworks:brand_updatepatch.facts, read back withgooseworks:brand_read. - Local mode (no GooseWorks, the
marketing-guideskill §5): append togooseworks-local/<brand-slug>/brand-notes.md, same opening, their words, dated. Read the file first; mark your own superseded lineretired <date>; read it back.
The playbooks do not change between the paths.
Long evidence, tables and reports belong in a brand document (the save-to-brain skill (../save-to-brain/SKILL.md)); a learning is the rule or finding itself, in the human's words.
When you may write one
- Only with the human's own words in
user_statement: what they typed. An explicit approval such as "yes, save that rule" counts; your draft never goes there. On the older path also sendknowledge_intent: "user_correction"; on the brain path, sourceyou. Without their words, don't write it: put the finding in your document instead. - Brand facts the marketer states (an offer, a price) follow the guide's rule of that name (§3): save only an unambiguous fact, ask once if their words conflict, and search first (step 1 of either path) so one fact is one save.
mustanddontonly for rules the human stated or approved. Findings areinsight; style guidance isdo.- One exception, brain path only: a numbers-only measured readout of our own work (a budget move's before → after, a test result) may be saved as a fact without the human's words. It comes from the
run-memoryskill (../run-memory/SKILL.md) and goes to kind 23, fieldexperiment_readout.<id>, with sourceour_work. Only aworkedordidn't workverdict, labelled truth or claimed, and never from a scheduled run. It states numbers and dates, never a rule. - Found, not confirmed (the marketing-guide §1.4). A save waits for the human's yes in the GooseWorks app even in their own words: never call it confirmed or approved, and never accept a proposal unless the human said so in this conversation. Even then, the app may require them to accept it there; relay the link the refusal gives.
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 Changed 236150f90788
- 5d ago First seen · 71 lines · 77 tokens per session scan A 6c151779d778
save-learning is a skill published in the GitHub repository gooseworks-ai/goose-skills (1,265 stars, last pushed today), licensed MIT. It adds 77 tokens to every session and 2,499 once invoked, about $0.0003 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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