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 competitor-post-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/competitor-post-engagers)<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/competitor-post-engagers"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/competitor-post-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/competitor-post-engagers"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/competitor-post-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 | $0.00086 | $0.01848 |
| Opus 5 | $0.00043 | $0.00924 |
| Sonnet 5 | $0.00017 | $0.00370 |
| Haiku 4.5 | $0.00009 | $0.00185 |
Grade A, and why
competitor-post-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 — 169 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Competitor Post Engagers
Find ICP-fit leads by scraping engagers from a competitor's top-performing LinkedIn posts. Given one or more company page URLs, this skill finds their highest-engagement recent posts, extracts everyone who reacted or commented, and classifies by ICP fit.
Core principle: Scrape all posts in one call per company, then locally rank and select the top N. This minimizes Apify costs while maximizing lead quality.
Phase 0: Intake
Ask the user these questions:
Target Companies
- LinkedIn company page URL(s) to scrape (e.g.,
https://www.linkedin.com/company/11x-ai/) - Time window — how many days back to look (default: 30)
- Top N posts per company to extract engagers from (default: 1)
ICP Criteria
- ICP keywords — job title/role terms that indicate a good lead (e.g., "sales", "SDR", "revenue")
- Exclude keywords — roles to filter out (e.g., "software engineer", "designer")
- Geographic focus (optional, e.g., "United States")
Save config in the current working directory (or user-specified path):
competitor-post-engagers-config.json
Config JSON structure:
{
"name": "<run-name>",
"company_urls": ["https://www.linkedin.com/company/<competitor>/"],
"days_back": 30,
"max_posts": 50,
"max_reactions": 500,
"max_comments": 200,
"top_n_posts": 1,
"icp_keywords": ["sales", "revenue", "growth", "SDR", "BDR", "outbound"],
"exclude_keywords": ["software engineer", "developer", "designer"],
"enrich_companies": true,
"competitor_company_names": ["<competitor-name>"],
"industry_keywords": ["freight", "logistics", "trucking", "transportation", "3pl", "supply chain", "carrier", "brokerage", "shipping", "warehousing"],
"output_dir": "output"
}
enrich_companies— Enable Apollo company enrichment (default: true). Set to false or use--skip-company-enrichto skip.competitor_company_names— Company names to exclude from enrichment (the competitor itself).industry_keywords— Industry terms that indicate ICP fit. Matched against Apollo's industry field.
What ships with it
2 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.
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 · 169 lines · 86 tokens per session scan A 335e93bdaabc
competitor-post-engagers is a skill published in the GitHub repository gooseworks-ai/goose-skills (1,202 stars, last pushed 11d ago), licensed MIT. It adds 86 tokens to every session and 1,848 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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