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 outbound-prospecting-enginegit 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/outbound-prospecting-engine)<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/outbound-prospecting-engine"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/outbound-prospecting-engine/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/outbound-prospecting-engine"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/outbound-prospecting-engine.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 | $0.00032 | $0.00721 |
| Opus 5 | $0.00016 | $0.00360 |
| Sonnet 5 | $0.00006 | $0.00144 |
| Haiku 4.5 | $0.00003 | $0.00072 |
Grade A, and why
outbound-prospecting-engine 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 — 104 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Outbound Prospecting Engine
Build and run a complete outbound prospecting system: signal detection → company research → contact finding → personalization → campaign launch.
When to Use
- "Set up outbound prospecting for [client]"
- "Build a lead gen engine targeting [ICP]"
- "Find and reach out to companies that need [solution]"
Prerequisites
- Client context.md with ICP, value props, positioning
- Signal keywords (what to monitor for intent)
- Approved messaging / email sequences (or generate them)
Steps
1. Define Signal Sources
Based on the client's ICP and motion, select which signals to monitor:
| Signal Source | Best For | Skill |
|---|---|---|
| Job postings | Companies with allocated budget | job-posting-intent |
| Funding announcements | Companies with fresh capital | funding-signal-monitor |
| LinkedIn posts/comments | Practitioners discussing the problem | linkedin-post-research + linkedin-commenter-extractor |
| Conference attendees | People actively engaged with the space | luma-event-attendees |
| Competitor customers | Companies already buying similar solutions | competitor-post-engagers |
2. Run Signal Detection
Execute selected signal skills with client-specific keywords. Run in parallel.
Output: Raw signal list — companies + signal context.
3. Qualify & Score
Skill: lead-qualification
Filter against ICP criteria. Score each lead:
- Multi-signal leads = highest priority
- Job posting + funding = strongest intent
- Single social mention = lowest (awareness only)
4. Find Decision-Maker Contacts
Skill: company-contact-finder
For top qualified companies, find the specific decision-makers:
- Target titles from client's ICP
- Get email addresses and LinkedIn URLs
5. Deduplicate
Skill: contact-cache
Check all leads against the contact cache. Add new leads to cache. Skip any that have been contacted before.
6. Personalize Outreach
For each lead, generate personalized email sequence using:
- The signal that surfaced them (the "why now")
- Their company context (what they do, their pain)
- The client's value proposition (how it solves their pain)
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 · 104 lines · 32 tokens per session scan A 78d6f710dbf1
outbound-prospecting-engine is a skill published in the GitHub repository gooseworks-ai/goose-skills (1,202 stars, last pushed 11d ago), licensed MIT. It adds 32 tokens to every session and 721 once invoked, about $0.0002 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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