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 lead-qualificationgit 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/lead-qualification)<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/lead-qualification"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/lead-qualification/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/lead-qualification"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/lead-qualification.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 | $0.00074 | $0.03559 |
| Opus 5 | $0.00037 | $0.01780 |
| Sonnet 5 | $0.00015 | $0.00712 |
| Haiku 4.5 | $0.00007 | $0.00356 |
Grade A, and why
lead-qualification 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 — 373 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Lead Qualification Engine
Qualify leads against custom criteria through a structured intake process, then score lead lists in parallel with confidence ratings and reasoning.
Three Modes of Operation
Mode 1: Full Intake + Qualify
No existing qualification prompt. Run intake to build one, save it, then qualify leads.
Trigger: User provides no qualification prompt file.
Mode 2: Reuse Prompt + Qualify
User references an existing qualification prompt file — skip intake, go straight to scoring.
Trigger: User tags or references a file in skills/lead-qualification/qualification-prompts/.
Mode 3: Refine / Calibrate
User has seen results and wants to adjust criteria. Update the saved prompt, re-run.
Trigger: User says something like "refine", "adjust", "that's wrong", or provides feedback on qualification results.
Phase 1: Intake (Mode 1 Only)
The goal is to build a complete picture of who the user considers qualified vs disqualified. Present questions in bulk rounds so the user can answer efficiently.
Round 1 — Core Questions (Present All at Once)
Present these questions as a numbered list. Tell the user: "Answer what's relevant, skip what's not. I'll follow up on anything I need to clarify."
Product & Campaign Context:
- What's your product/service in one sentence?
- What problem does it solve and for whom?
- What's the specific campaign or outreach angle? (e.g., "targeting companies that just raised Series A", "going after teams switching from Competitor X")
Company-Level Criteria: 4. What company sizes are you targeting? (e.g., 1-10, 11-50, 51-200, 201-1000, 1000+) 5. What industries or verticals are a good fit? 6. Any industries or company types to explicitly EXCLUDE? 7. Geographic targets? Or is this global? 8. Geographic exclusions? 9. Does company stage matter? (e.g., seed, Series A, Series B+, public) 10. Any revenue range or funding range that matters?
Person-Level Criteria: 11. What job titles or roles are your ideal buyers? 12. What titles are explicitly disqualified? 13. Does seniority level matter? (e.g., must be Director+, VP+, C-level) 14. What departments should they be in? (e.g., growth, marketing, sales, engineering) 15. Minimum tenure at current company? (e.g., 6+ months to have buying power) 16. Does total years of experience matter?
What ships with it
4 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 · 373 lines · 74 tokens per session scan A 76441c66b0de
lead-qualification is a skill published in the GitHub repository gooseworks-ai/goose-skills (1,202 stars, last pushed 11d ago), licensed MIT. It adds 74 tokens to every session and 3,559 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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