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 pain-language-engagersgit 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/pain-language-engagers)<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/pain-language-engagers"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/pain-language-engagers/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/pain-language-engagers"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/pain-language-engagers.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 | $0.00108 | $0.02002 |
| Opus 5 | $0.00054 | $0.01001 |
| Sonnet 5 | $0.00022 | $0.00400 |
| Haiku 4.5 | $0.00011 | $0.00200 |
Grade A, and why
pain-language-engagers 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 — 189 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Pain-Language Engagers
Find warm leads by scraping LinkedIn for pain-language posts and their engagers. People who write about, react to, or comment on posts expressing operational frustrations are signaling they live with a problem your product solves. This skill turns those signals into a qualified lead list.
Core principle: Search for pain-language, not solution-language. Solution keywords ("AI automation", "workflow optimization") attract builders and VCs. Pain keywords ("can't find drivers", "check calls are killing us") attract operators living with the problem.
Phase 0: Intake
Before generating keywords or running anything, ask the user these questions. Present them as a numbered list and tell the user to answer what's relevant and skip what's not.
Product & Pain Context
- What does your product/service do in one sentence?
- What specific problem does it solve? Who feels this pain most acutely?
- What does your ICP's day-to-day look like WITHOUT your product? (The frustrations, workarounds, manual processes)
- What phrases would someone use when complaining about this problem on LinkedIn? (e.g., "check calls are killing us", "can't find drivers", "spending hours on manual data entry")
ICP Definition
- What industries/verticals are your target buyers in?
- What job titles or roles are your ideal buyers? (e.g., "VP Operations", "Broker owner", "Head of Logistics")
- What titles should be EXCLUDED? (e.g., "Software Engineer", "AI researcher")
- Any specific competitors whose employees should be filtered out?
- Geographic focus? (e.g., "United States only", "global")
LinkedIn Signal Sources
- Any LinkedIn company pages where your ICP is likely to engage? (Industry publications, communities, competitor pages)
- Any specific LinkedIn posts or content creators your ICP follows?
Phase 1: Generate Pain-Language Keywords
Based on the intake answers, generate ~15-25 pain-language keywords in LinkedIn boolean search syntax. Organize into categories:
What ships with it
11 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.
- configs/artisan-ai.json 3.9 KB
- configs/happy-robot.json 3.1 KB
- configs/outset-ai.json 6.7 KB
- output/artisan-ai-20260225_1237.csv 72 KB
- output/artisan-ai-20260225_1344.csv 506 KB
- output/outset-ai-20260225_1232.csv 63 KB
- output/outset-ai-20260225_1237.csv 102 KB
- output/outset-ai-20260225_1250-cleaned.csv 429 KB
- output/outset-ai-20260225_1250.csv 429 KB
- scripts/pain_language_engagers.py 36 KB runs code
- skill.meta.json 311 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.
- 9d ago First seen · 189 lines · 108 tokens per session scan A 7caecd743618
pain-language-engagers is a skill published in the GitHub repository gooseworks-ai/goose-skills (1,202 stars, last pushed 11d ago), licensed MIT. It adds 108 tokens to every session and 2,002 once invoked, about $0.0005 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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