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-audience-strategygit 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-audience-strategy)<a href="https://agentmods.dev/skills/swan-gtm/gtm-skills/meta-audience-strategy"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/meta-audience-strategy/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-audience-strategy"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/meta-audience-strategy.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 | $0.00046 | $0.02043 |
| Opus 5.5 | $0.00018 | $0.00817 |
| Sonnet 5.5 | $0.00009 | $0.00409 |
| Haiku 4.5 | $0.00005 | $0.00204 |
Grade A, and why
meta-audience-strategy 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 — 164 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Audience Strategy for B2B Meta Ads - High-ACV SaaS
How to reach B2B buyers on a platform built for consumers. Your data quality determines everything. Meta's algorithm is powerful at finding lookalike audiences - but only if you feed it high-quality seed data.
Core Principle
Meta Ads for B2B can deliver half the cost per lead and lower cost per qualified opportunity compared to LinkedIn - but only when the audience data is right. Proof points: teams driving 100+ scheduled demos/month from Meta at $849 cost per demo, $2-4K cost per opportunity (mid-market and enterprise). Others opening $800K+ in new pipeline after being skeptical.
The catch: You can't sell to everyone. B2B success on Meta depends on data quality and creative doing the targeting work - not Meta's native B2B targeting (which is weak).
Start With Customer Intelligence (Before Touching Ads Manager)
Before building audiences, do this research:
Interview Existing Customers
- Why they bought - what problem were they solving, what triggered the search?
- What made them hesitate - address this in ad messaging and pre-call sequences
- Where they hang out online - validates whether Meta is the right channel
Segment Your Best Customers
Build your seed audience from customers who:
- Paid the most (highest ACV)
- Bought quickest (shortest sales cycle)
- Stayed longest (best retention/LTV)
These three signals identify your top 5% of ideal buyers. Your entire targeting strategy should start with finding more of THESE people, not more people in general.
The Top 5% Framework
Instead of targeting your entire TAM, start with the top 5% of in-market buyers - those with the shortest sales cycle, fewest objections, and most urgency. Land them first. Once you've converted (or reached) most of that 5% (typically after ~12 months of focused prospecting), expand to the next tier.
The Data Hierarchy
Three tiers of audience sources, in priority order. Always validate quality before scaling.
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 · 164 lines · 46 tokens per session scan A b2b67de4fc77
meta-audience-strategy is a skill published in the GitHub repository swan-gtm/gtm-skills (221 stars, last pushed 8d ago), licensed MIT. It adds 46 tokens to every session and 2,043 once invoked, about $0.0002 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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