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 creating-campaigns-ads-audiencesgit 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/creating-campaigns-ads-audiences)<a href="https://agentmods.dev/skills/swan-gtm/gtm-skills/creating-campaigns-ads-audiences"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/creating-campaigns-ads-audiences/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/creating-campaigns-ads-audiences"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/creating-campaigns-ads-audiences.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 | $0.00040 | $0.01131 |
| Opus 5 | $0.00020 | $0.00566 |
| Sonnet 5 | $0.00008 | $0.00226 |
| Haiku 4.5 | $0.00004 | $0.00113 |
Grade A, and why
creating-campaigns-ads-audiences 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 — 87 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Creating Campaigns, Ads, and Audiences on Meta
The full build chain, end to end. Meta's structure is strict and nested:
Campaign (objective, budget if CBO)
-> Ad Set (audience, placement, budget if ABO, optimization goal, schedule)
-> Ad (references a creative + an ad set)
-> Ad Creative (media, copy, CTA, link, url_tags)
Custom / Lookalike Audiences (built at the account level, referenced by ad sets)
Golden rules (always):
- Everything is created PAUSED. Never set a campaign, ad set, or ad to
ACTIVEwithout explicit user confirmation. Spend only starts when the user says go. - Budgets are in cents.
5000= $50.00. - Build order: audiences first (ad sets reference them) -> campaign -> ad set -> creative -> ad.
- Full field-level schemas (every param, every enum) are in ../api-reference.md. This guide is the flow; the api-reference is the dictionary.
1. Audiences
Custom audience (from a customer list, pixel traffic, engagement) - use the script:
python create_custom_audience.py --name "CRM - Closed Won" --type CUSTOM --subtype CRM
For B2B, follow the data hierarchy in audience-strategy.md: CRM lookalikes (Tier 1) -> third-party data (Tier 2) -> broad (Tier 3). Validate audience quality before scaling creative.
Lookalike - create the source custom audience first, then create a lookalike from it (subtype=LOOKALIKE, with origin_audience_id and country/ratio). See api-reference.md for the lookalike spec.
2. Campaign
Use the script:
python create_campaign.py --name "Q2 Leads" --objective OUTCOME_LEADS --daily-budget 5000
OUTCOME_LEADSfor lead gen. Objectives are listed increate_campaign.py.- Add
is_adset_budget_sharing_enabled: "true"for CBO (budget on the campaign) or"false"for ABO (budget on each ad set). ABO is the default for audience testing - one ad set per audience source, same ads across all. - Starts PAUSED. The script prints the
campaign_idfor the next step.
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 · 87 lines · 40 tokens per session scan A 392e5a5a2906
creating-campaigns-ads-audiences is a skill published in the GitHub repository swan-gtm/gtm-skills (153 stars, last pushed 2d ago), licensed MIT. It adds 40 tokens to every session and 1,131 once invoked, about $0.0002 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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