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 brand-researchgit 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/brand-research)<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/brand-research"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/brand-research/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/brand-research"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/brand-research.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00058 | $0.01383 |
| Opus 5 | $0.00029 | $0.00691 |
| Sonnet 5 | $0.00012 | $0.00277 |
| Haiku 4.5 | $0.00006 | $0.00138 |
Grade A, and why
brand-research 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 12d 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 — 135 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Brand Research
Build a sourced Brand Core that future research, analysis, and creative workflows can reuse without rediscovering the company every time.
The required path is research-only and works with local files. GooseWorks sync, ad imports, and paid asset generation are optional extensions—not prerequisites for a complete result.
Inputs
website— required canonical company or brand website.focus— optional product, collection, market, or campaign to prioritize.output_dir— optional; defaults to a clearly named local brand folder.depth—quickorfull(defaultfull).sync_to_gooseworks— optional, default false.include_existing_ads— optional, default true in full mode.generate_assets— optional paid extension, default false.
Brand Core output
Create:
brand-core/
summary.md
products.md
audience.md
competitors.md
positioning-and-offers.md
messaging.md
visual-identity.md
sources.md
brand-core.json
Use local paths that work outside GooseWorks. brand-core.json is a structured echo for other agent skills; the Markdown remains the human-readable source of truth.
Workflow
1. Resolve the entity
Open the provided website and confirm the company name, canonical domain, market, and focus product. If the site is inaccessible or the identity remains ambiguous, ask for the minimum clarification instead of researching the wrong entity.
2. Research the first-party source
Review the homepage, product/collection pages, about page, pricing or offer pages, FAQ, policies, store navigation, social links, and press/brand resources. Capture:
- what the company sells and how the catalog is organized;
- prices, offers, bundles, guarantees, subscriptions, and availability;
- product claims, ingredients/materials, use cases, and differentiators;
- stated audiences and customer outcomes;
- brand voice, visual system, proof, and trust markers.
Do not turn marketing claims into facts. Label them as brand-stated claims until corroborated.
What ships with it
13 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.
- .env.example 607 B
- examples/liquid-death-sparkling-water.md 1.0 KB
- examples/notion-calendar-with-ads.md 1.1 KB
- README.md 2.8 KB
- references/output-contract.md 3.1 KB
- requirements.txt 249 B
- scripts/fetch_asset.py 2.5 KB runs code
- scripts/lib.py 3.7 KB runs code
- scripts/register_asset.py 1.2 KB runs code
- scripts/render_product_shot.py 2.5 KB runs code
- scripts/scaffold_brand.py 2.7 KB runs code
- scripts/verify_pack.py 7.0 KB runs code
- skill.meta.json 392 B
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.
- 12d ago First seen · 135 lines · 58 tokens per session scan A 8cb7e25dd766
brand-research is a skill published in the GitHub repository gooseworks-ai/goose-skills (1,202 stars, last pushed 11d ago), licensed MIT. It adds 58 tokens to every session and 1,383 once invoked, about $0.0003 per session on Opus 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 2026-08-30.
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