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 research-competitors-category-passgit 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/research-competitors-category-pass)<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/research-competitors-category-pass"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/research-competitors-category-pass/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/research-competitors-category-pass"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/research-competitors-category-pass.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 | $0.00050 | $0.01107 |
| Opus 5.5 | $0.00020 | $0.00443 |
| Sonnet 5.5 | $0.00010 | $0.00221 |
| Haiku 4.5 | $0.00005 | $0.00111 |
Grade A, and why
research-competitors-category-pass 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 2d 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 — 59 lines — stays where its author put it; the contents beside it link to each section on GitHub.
category-pass
You cover what no single rival pass sees: how buyers search and choose in this category, and where our brand stands. Do every section; never stop to ask whether to go deeper. Same rules as the research-competitors-rival-pass skill (../research-competitors-rival-pass/SKILL.md): don't load any skill or start subagents; about 12 searches and 10 fetches in a standard run; public pages only; every fact with its link and date inline; how sure in words; "Needs the browser" and "Couldn't read" lists. The main run has already done the field searches and chosen the rivals — you go deeper on the category: sections 3–7 (and 1–2 only to add what it missed).
Inputs: our brand, what it sells, its category in buyers' words, the rival list, company type, today's date.
1. Money searches
Search, in a clean browser or web search where possible: best <category> · <category> software / tool / app · <rival> alternative (each main rival) · <rival> vs <rival> · <our brand> vs <rival> · <our brand> reviews · our brand name alone. Record who ranks in the top results, who runs search ads on each query (including on our brand name — a rival bidding on us is a threat), and which "vs" and "alternatives" pages exist and who wrote them. Results vary by location; say where you searched from.
2. AI-answer presence
Ask the same prompts each time — best <category> tool, alternatives to <main rival>, <our brand> vs <main rival>, what is <our brand> — in every AI answer engine the host can reach (Google's AI Overview in search results counts; others as available). Record who is named, in what order, and how our brand is described, with the date. Where none can be reached, say so, and read improve-search-visibility's latest search/visibility/<date>.md if it exists.
3. Community — how buyers frame the choice
Last 90 days on Reddit (the category's subreddits), Hacker News, LinkedIn comment threads, Quora: best <category>, <category> recommendation, <category> tool for <job>, switching from <rival>. Themes with links: what buyers compare on, the words they use, which names come up together, and any rival drawing anger.
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.
- 2d ago Changed c5e5390ae0e5
- 7d ago First seen · 59 lines · 50 tokens per session scan A ea1fadf509ee
research-competitors-category-pass is a skill published in the GitHub repository gooseworks-ai/goose-skills (1,271 stars, last pushed yesterday), licensed MIT. It adds 50 tokens to every session and 1,107 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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