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 swan-gtm/gtm-skills --skill meta-adsgit clone --depth 1 https://github.com/swan-gtm/gtm-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/swan-gtm/gtm-skills/meta-ads)<a href="https://agentmods.dev/skills/swan-gtm/gtm-skills/meta-ads"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/meta-ads/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/swan-gtm/gtm-skills/meta-ads"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/meta-ads.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 | $0.00094 | $0.01768 |
| Opus 5.5 | $0.00038 | $0.00707 |
| Sonnet 5.5 | $0.00019 | $0.00354 |
| Haiku 4.5 | $0.00009 | $0.00177 |
Grade A, and why
meta-ads 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 1mo 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.
Meta Ads Management for B2B
Orchestrator for all Meta Ads (Facebook/Instagram) tasks. Meta Ads for B2B can deliver half the cost per lead and lower cost per qualified opportunity compared to LinkedIn - but only when audience data quality is right and creative does the targeting work.
Methodology
This skill implements the Ivan Falco B2B demand generation methodology for Meta - a data-first, creative-as-targeting approach that leverages Meta's algorithm (Andromeda + Gem) with high-quality audience inputs and systematic creative testing.
Core Philosophy
On Meta, your data is everything. Your creative is your targeting.
Meta's native targeting can't match LinkedIn's B2B precision (no job title, company, seniority filters). The way to make Meta work for B2B is to bring your own high-quality audience data (CRM, third-party providers) and use creative specificity to filter for ICP. The algorithm (Andromeda + Gem) does the rest.
Routing Logic
Always Load First
Any Meta work starts here:
| Intent | File | Priority |
|---|---|---|
| Scaling qualified B2B pipeline end to end (the goal is SQLs / qualified pipeline, not lead volume) | scale-b2b-qualified-pipeline.md | LOAD FIRST when the goal is qualified pipeline. The 6-step spine (map market -> audiences -> funnel events/CAPI -> creative -> segments -> SQL reporting). |
| ANY operational decision (pause, scale, graduate, budget, creative count) | meta-operating-system.md | ALWAYS LOAD FIRST. This is THE decision framework. All formulas, thresholds, and actions live here. |
| Creative production decisions (what to build, when to iterate, cadence, formats) | meta-creative-system.md | Iteration hierarchy, concept sourcing, format playbook, testing cadence, fatigue detection, quality scoring. |
Deeper Context (Load When Needed)
These files provide detailed methodology on specific topics. The operating system drives decisions; these explain the deeper why and how.
What ships with it
10 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.
- references/advantage-plus.md 9.9 KB
- references/audience-strategy.md 8.3 KB
- references/campaign-structure.md 15 KB
- references/lead-form-optimization.md 6.6 KB
- references/meta-b2b-overview.md 15 KB
- references/meta-creative-system.md 38 KB
- references/meta-operating-system.md 40 KB
- references/meta-tracking-and-capi.md 27 KB
- references/offer-strategy.md 9.4 KB
- references/scale-b2b-qualified-pipeline.md 9.3 KB
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.
- 1mo ago First seen · 104 lines · 94 tokens per session scan A 50dc2c8ab209
meta-ads is a skill published in the GitHub repository swan-gtm/gtm-skills (221 stars, last pushed 8d ago), licensed MIT. It adds 94 tokens to every session and 1,768 once invoked, about $0.0004 per session on Opus 5.5. 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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