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-campaign-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-campaign-analyzer)<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/ad-campaign-analyzer"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/ad-campaign-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-campaign-analyzer"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/ad-campaign-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 | $0.00060 | $0.03551 |
| Opus 5 | $0.00030 | $0.01775 |
| Sonnet 5 | $0.00012 | $0.00710 |
| Haiku 4.5 | $0.00006 | $0.00355 |
Grade A, and why
ad-campaign-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 — 361 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ad Campaign Analyzer
Take raw campaign performance data and turn it into clear decisions. This skill doesn't just summarize metrics — it diagnoses problems, identifies winners, checks statistical significance, and tells you exactly what to cut, scale, and test next. Then it goes further: it compares channels on equal terms, finds where you're over-spending vs under-spending relative to results, and produces a concrete budget reallocation plan.
Core principle: Most startup founders check their ad dashboard, see a ROAS number, and either panic or celebrate. This skill gives you the nuanced analysis a paid media specialist would: what's actually significant, what's noise, and where your next dollar should go. It also solves the allocation problem — most startups either spread budget too thin across channels (no channel gets enough to learn) or dump everything into one channel (missing cheaper opportunities elsewhere).
When to Use
- "Analyze my Google Ads performance"
- "Which ads should I kill?"
- "Is this campaign working?"
- "Where am I wasting ad spend?"
- "Optimize my Meta Ads"
- "How should I split my ad budget?"
- "Should I spend more on Google or Meta?"
- "Reallocate my ad spend across channels"
- "Where am I getting the best return?"
- "I have $X/month for ads — how should I distribute it?"
Phase 0: Intake
- Campaign data — One of:
- CSV export from Google Ads / Meta Ads Manager / LinkedIn Campaign Manager
- Pasted performance table
- Screenshots of dashboard (we'll extract the data)
- Platform(s) — Google / Meta / LinkedIn / All
- Time period — What date range does this cover?
- Monthly budget — Total ad spend in this period
- Primary goal — What conversion are you optimizing for? (Demos / Trials / Purchases / Leads)
- Target metrics — Do you have target CPA or ROAS? (If not, we'll benchmark)
- Any known changes? — Did you change creative, budget, or targeting during this period?
- Channels currently running — Google Ads, Meta Ads, LinkedIn Ads, Twitter/X Ads, TikTok Ads, other
- Funnel data (if available):
- Lead → MQL rate
- MQL → SQL rate
- SQL → Close rate
- Average deal size
- Channels you're considering but haven't tried — Want to test new channels?
- Constraints — Minimum spend on any channel? Platform you must stay on?
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 · 361 lines · 60 tokens per session scan A 62ebbdebd7b8
ad-campaign-analyzer is a skill published in the GitHub repository gooseworks-ai/goose-skills (1,202 stars, last pushed 11d ago), licensed MIT. It adds 60 tokens to every session and 3,551 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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