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 meta-ads-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/meta-ads-analyzer)<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/meta-ads-analyzer"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/meta-ads-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/meta-ads-analyzer"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/meta-ads-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 | $0.00093 | $0.04118 |
| Opus 5 | $0.00046 | $0.02059 |
| Sonnet 5 | $0.00019 | $0.00824 |
| Haiku 4.5 | $0.00009 | $0.00412 |
Grade A, and why
meta-ads-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 — 299 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Meta Ads Analyzer
Most "Meta Ads analysis" stops at "this CPA is high, pause it." That's wrong more often than it's right. Meta's delivery system optimizes for marginal efficiency — the cost of the next conversion — not average efficiency across a snapshot. A segment with a higher average CPA is often the one keeping your overall campaign cheap. Pausing it makes things worse.
This skill diagnoses Meta campaigns the way a senior media buyer would: at the right evaluation level, accounting for learning state, separating noise from signal, and explaining why the system is making the decisions it's making before recommending any change. It can also audit whether the account supports the complete customer journey without assuming that TOF, MOF, and BOF must be separate campaigns.
Core principle: Holistic first, then drill down. Marginal over average. Customer-journey coverage over rigid funnel structure. Dynamic over static. Every recommendation is a testable hypothesis with expected impact, not a directive.
For account audits, full-funnel reviews, or questions about what is missing, read and apply references/customer-journey-coverage.md before analyzing the account.
When to Use
- "Analyze my Meta Ads campaign performance"
- "Why is the system spending more on the higher-CPA placement?"
- "Diagnose what's wrong with this ad set"
- "Should I pause this audience / placement / ad?"
- "My CPA jumped — is this normal or a real problem?"
- "Audit this campaign before I scale budget"
- "I exported my Meta data — what does it actually mean?"
- "Audit my Meta ad account and tell me what is missing"
- "Do I have enough TOF, MOF, and BOF coverage?"
- "Why are customers not moving through the funnel?"
- "What ads should I create next?"
Phase 0: Intake
- Campaign data — One of:
- CSV export from Meta Ads Manager (Campaign / Ad Set / Ad level + breakdowns)
- Pasted performance table
- Screenshots (we'll extract the metrics)
- Live data via your existing Meta Marketing API connection
- Campaign setup:
- Objective (Awareness / Traffic / Engagement / Lead Gen / Conversions / Sales / App Installs)
- Budget type (Advantage+ Campaign Budget = CBO, or Ad Set Budget = ABO)
- Placements (Automatic vs. manual)
- Number of ad sets and ads
- Time period — Date range covered, with any known events (creative refresh, budget change, audience edit, account issue)
- Target metrics — CPA target, ROAS target, or "no target — benchmark me"
- Funnel context (if relevant) — On-platform conversion vs. website event vs. downstream qualification rate
- What's making you ask? — Specific concern ("CPA up 40%"), routine review, or pre-scale audit
- Account coverage evidence (for account/funnel audits, when available):
- Campaign objective, optimization event, and attribution setting
- Audience strategy, exclusions, and retargeting windows
- Creative format, message, proof, offer, and landing-page destination
- Pixel/CAPI and relevant conversion-event health
- Campaign, ad-set, and ad-level spend and results
- Report style —
guidedby default; useexpertwhen the user asks for technical detail or demonstrates strong media-buying knowledge
What ships with it
3 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.
- 13d ago First seen · 299 lines · 93 tokens per session scan A f9aef49f0043
meta-ads-analyzer is a skill published in the GitHub repository gooseworks-ai/goose-skills (1,202 stars, last pushed 11d ago), licensed MIT. It adds 93 tokens to every session and 4,118 once invoked, about $0.0005 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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