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 ad-lead-quality-analyzergit 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/ad-lead-quality-analyzer)<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/ad-lead-quality-analyzer"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/ad-lead-quality-analyzer/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/ad-lead-quality-analyzer"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/ad-lead-quality-analyzer.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 | $0.00056 | $0.02640 |
| Opus 5 | $0.00028 | $0.01320 |
| Sonnet 5 | $0.00011 | $0.00528 |
| Haiku 4.5 | $0.00006 | $0.00264 |
Grade A, and why
ad-lead-quality-analyzer 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 13d 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 — 199 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ad Lead Quality Analyzer
Meta optimizes for whatever conversion event you fire. For lead-gen and participant-recruitment campaigns that's almost always "signup" — but a signup is worthless if the lead never qualifies, never completes the requested action, or never gets paid out. The lowest-CPA campaign is often the one bringing in the worst leads.
This skill joins what the ad platform knows (spend, signups) with what your own product knows (downstream funnel) and replaces vanity CPA with true CAC per qualified lead. It then classifies every creative into actionable buckets so you stop scaling the wrong winners.
Core principle: The ad platform's CPA is a half-truth. Real optimization needs both halves of the funnel — pre-signup (the platform has it) and post-signup (you have it). Until they're joined, you're flying blind.
When to Use
- "Which ads are bringing in real leads vs. junk?"
- "True CAC per qualified contributor / customer / participant"
- "Why is my lowest-CPA campaign performing worst downstream?"
- "Audit lead quality across creatives / audiences / placements"
- "Should I trust Meta's CPA when scaling?"
- "Find the creatives that look like winners but aren't"
Pipeline Pattern Assumptions (Read First)
This skill is opinionated about what to measure (true CAC per qualified lead, with cohort maturation, with vanity scoring) and agnostic about how the data is sourced.
It assumes one of three standard attribution patterns:
| Pattern | Setup | Join Key |
|---|---|---|
| A. UTM-only (most common) | UTM params captured on signup form, stored on lead/user record. Downstream events joined by user_id inside your DB. | utm_content (typically the ad ID) on both sides, or fbclid |
| B. UTM + CAPI send-back (best) | Same as A, plus your app fires Conversions API events back to Meta when downstream stages hit. Meta then optimizes for quality, not signups. | event_id / external_id |
| C. Meta Lead Ads + CRM sync | Meta-hosted lead form, lead_id syncs to CRM/DB, joined there. |
lead_id |
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.
- 13d ago First seen · 199 lines · 56 tokens per session scan A e6cf215c470d
ad-lead-quality-analyzer is a skill published in the GitHub repository gooseworks-ai/goose-skills (1,202 stars, last pushed 11d ago), licensed MIT. It adds 56 tokens to every session and 2,640 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 2026-08-30.
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