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-signalsgit 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-signals)<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/competitor-signals"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/competitor-signals/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-signals"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/competitor-signals.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Data Exfiltration · line 29 Code or instructions that leak agent conversation context to external services, potentially exposing sensitive user interactions.Fix: Remove any code that sends prompts, responses, or session data externally. Preserve user privacy; never exfiltrate conversation content.
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 | $0.02776 |
| Opus 5 | $0.00025 | $0.01388 |
| Sonnet 5 | $0.00010 | $0.00555 |
| Haiku 4.5 | $0.00005 | $0.00278 |
Grade A, and why
competitor-signals 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 — 280 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Competitor Signals
Find leads by monitoring competitor product activity. Instead of looking for your prospects directly, watch your competitors' audience — every person engaging with a competitor launch is self-identifying as in-market for your category.
When to Use
- User wants to find people engaging with competitor products
- User mentions Product Hunt launches, competitor press coverage, or competitor case studies
- User wants to find people switching from or evaluating competitor products
- User asks "who is using [competitor]" or "who is looking at alternatives to [competitor]"
- User wants to monitor competitor activity for lead generation
- User has a clear list of competitors and wants to mine their audience
Prerequisites
- Python 3.9+ with
requestsand optionallypython-dotenv - Product Hunt developer token (free, optional — get at
api.producthunt.com/v2/oauth/applications) - Apify API token in
.env(fallback for PH if API names are redacted, optional) - Working directory: the project root containing this skill
Phase 1: Collect Context
Step 1: Gather Competitor Information
Ask the user:
"To find leads from competitor activity, I need:
- Who are your competitors? (product names and company names)
- Do you know their Product Hunt slugs? (the URL path on producthunt.com/posts/SLUG)
- Any specific competitor launches or announcements you've seen recently?
- Are there competitors or signals you specifically want to track? (e.g., a competitor just raised funding, launched a new feature, or got press coverage)"
Step 2: Discover Competitors (if user needs help)
If the user doesn't have a complete competitor list, help them discover competitors:
2a. Product Hunt search:
- Search producthunt.com for the user's product category
- Note: PH doesn't have a great search API — use web search: "site:producthunt.com [product category]"
2b. G2/Capterra category pages:
- Search: "[product category] G2" or "[product category] Capterra"
- These pages list all competitors in a category with rankings
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 · 280 lines · 50 tokens per session scan A 8249819306e6
competitor-signals is a skill published in the GitHub repository gooseworks-ai/goose-skills (1,202 stars, last pushed 11d ago), licensed MIT. It adds 50 tokens to every session and 2,776 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 2026-09-03.
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