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-voice-extractorgit 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-voice-extractor)<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/brand-voice-extractor"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/brand-voice-extractor/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-voice-extractor"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/brand-voice-extractor.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 | $0.00076 | $0.01825 |
| Opus 5 | $0.00038 | $0.00912 |
| Sonnet 5 | $0.00015 | $0.00365 |
| Haiku 4.5 | $0.00008 | $0.00183 |
Grade A, and why
brand-voice-extractor 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 9d 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 — 227 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Brand Voice Extractor
Analyze a company's published content to extract their brand voice and writing style. Reads their top content pieces and produces actionable guidelines for matching their voice in future content, outreach, or campaigns.
Quick Start
Extract brand voice for [company]. Use their blog at [url].
Or with content already cataloged:
Extract brand voice for [client]. Use the content inventory at clients/[client]/research/content-inventory.json.
Inputs
| Input | Required | Source |
|---|---|---|
| Content URLs | Yes | User provides, or pulled from site-content-catalog output |
| Company name | Yes | For context in the analysis |
| Number of pages | No | Default: 15. How many pages to analyze. |
Process
Phase 1: Select Content to Analyze
If content URLs are provided directly, use those. Otherwise:
- Read the content inventory from
site-content-catalogoutput - Select a diverse sample of 10-20 pages, prioritizing:
- Blog posts (primary voice indicator)
- Landing pages (marketing voice)
- Case studies (storytelling voice)
- Mix of recent and older content (to detect voice evolution)
- Mix of topics (to see consistency across subjects)
Selection heuristic:
- 8-10 blog posts (mix of how-to, opinion, product updates)
- 2-3 landing pages (homepage, product page, solutions page)
- 2-3 case studies or customer stories (if available)
- 1-2 comparison/vs pages (if available)
Phase 2: Fetch and Extract Text
For each selected URL:
- WebFetch the page
- Extract the main content body (strip nav, footer, sidebar)
- Store: title, URL, raw text, word count
Phase 3: Analyze Voice Dimensions
Analyze across these dimensions:
A) Tone
- Formality spectrum: Casual ↔ Professional ↔ Academic
- Emotional register: Excited ↔ Measured ↔ Dry
- Authority stance: Peer/friend ↔ Expert/teacher ↔ Institution
- Humor usage: Frequent ↔ Occasional ↔ None
- Directness: Direct/bold ↔ Hedged/diplomatic
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.
- 9d ago First seen · 227 lines · 76 tokens per session scan A 8a8638a64b2d
brand-voice-extractor is a skill published in the GitHub repository gooseworks-ai/goose-skills (1,202 stars, last pushed 11d ago), licensed MIT. It adds 76 tokens to every session and 1,825 once invoked, about $0.0004 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 2026-09-03.
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