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-to-braingit 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-to-brain)<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/save-to-brain"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/save-to-brain/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-to-brain"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/save-to-brain.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 | $0.00068 | $0.01723 |
| Opus 5.5 | $0.00027 | $0.00689 |
| Sonnet 5.5 | $0.00014 | $0.00345 |
| Haiku 4.5 | $0.00007 | $0.00172 |
Grade A, and why
save-to-brain 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 yesterday.
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 — 60 lines — stays where its author put it; the contents beside it link to each section on GitHub.
save-to-brain
Every durable artifact goes through this atom. Never keep the only copy in chat or on the local disk, and never tell the marketer about workspaces, documents or imports — say "saved to your brand" or what failed in plain words.
Local mode (no GooseWorks, the marketing-guide skill §5) is the one exception. It calls no gooseworks: tool, and brand_id and coworker_agent_id are not needed:
- Host with files: with the host's own file tools, read
gooseworks-local/<brand-slug>/<path>if it exists, merge by step 1's rules (never-overwrite included), write it, and read it back. Skip steps 2–6. Say "saved in this folder", never "saved to your brand". - Hosted chat without files: nothing can be saved. Give the whole file in chat under its path and say "This isn't saved anywhere: paste it back next time and I'll pick up from it." Skip steps 1–6.
Inputs (the calling playbook supplies them)
| Input | Rule |
|---|---|
brand_id, brand_slug, coworker_agent_id |
From the gooseworks:brand_read list answer. |
path |
The playbook's own path in the marketing-guide's §4 table (research/market/…, research/competitors/…). Dates YYYY-MM-DD. |
artifact |
The artifact name, used in the title and tags (for example Voice-of-customer bank, voc-bank). |
title |
<Artifact> — <Brand> — <YYYY-MM-DD>; records with numbered versions add v<N>, and a suffixed file adds the same suffix (step 1). |
tags |
[<playbook>, <artifact-slug>], plus any extra key the caller needs to tell copies apart (for example rival:<slug>). All lower-case. The first tag is the document's creator: normally the calling playbook; a shared atom that writes on behalf of another owner puts that owner first (voice-card, claims-gate). |
versioned |
supersede — a record that is replaced by its newest version (a bank, a profile, a landscape, an angle bank, a watchlist). keep-history — a dated period artifact (a weekly report, a digest, a dated read); older ones are history and are never deleted. |
import |
yes (default) or no — no for working files that must stay out of brand knowledge (unsent reply drafts, raw item lists, pasted private material). |
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.
- yesterday Changed · +2 lines 09c4a0d1db96
- 6d ago First seen · 58 lines · 68 tokens per session scan A 1b24da8f0185
save-to-brain is a skill published in the GitHub repository gooseworks-ai/goose-skills (1,270 stars, last pushed today), licensed MIT. It adds 68 tokens to every session and 1,723 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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