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 linkedin-ads-audience-guidegit 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/linkedin-ads-audience-guide)<a href="https://agentmods.dev/skills/swan-gtm/gtm-skills/linkedin-ads-audience-guide"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/linkedin-ads-audience-guide/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/linkedin-ads-audience-guide"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/linkedin-ads-audience-guide.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 | $0.00048 | $0.05236 |
| Opus 5 | $0.00024 | $0.02618 |
| Sonnet 5 | $0.00010 | $0.01047 |
| Haiku 4.5 | $0.00005 | $0.00524 |
Grade A, and why
linkedin-ads-audience-guide 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 — 445 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LinkedIn Ads Audience Strategy - Guide
Audience sizing, targeting approaches, exclusion strategy, retargeting setup, and ABM list targeting for B2B campaigns on LinkedIn.
Audience Sizing Rules
Cold Audience Sweet Spots by Stage
| Stage | Recommended Size | Why |
|---|---|---|
| TOF (Cold Awareness) | ~50K sweet spot (up to 100K with budget; 10K-20K on a small budget) | 50K is the target. Push toward 100K only if you have the budget to show up repeatedly - a bigger audience needs more spend to hit meaningful frequency and >30% penetration. On a small budget, run tighter (10K-20K) so you can still show up enough times to be remembered. |
| MOF (Retargeting/Nurture) | 1K-30K | Warm audiences are naturally smaller - this is expected |
| BOF (Conversion) | 1K-5K | Smallest, highest intent - retargeting from MOF engagers |
| ABM Company Lists | 300+ matched members minimum | LinkedIn's hard floor - below this, ads will not serve |
Why bigger is not better. An audience over ~100K rarely makes sense, and over 300K makes no sense at all. The point of ABM is to show up to the same decision-makers multiple times until they remember you - that requires frequency and penetration above ~30% of the audience. The larger the audience, the more budget you need to reach that penetration; a huge audience on a normal budget just means you show up once to a lot of people and stick with none of them.
Why size is really a frequency decision. ABM works by showing the same buying committee your message enough times that they remember you - not by reaching the most people once. Two levers: penetration (the share of your target audience that has seen your ads) and frequency (how many times each person saw them). Both cost budget. A smaller audience lets a fixed budget hit higher penetration and frequency; a large one spreads the same money thin, so you reach many accounts once and stick with none. That is why ~50K is the target and you only push toward 100K when the budget can fund real frequency on top of it.
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 · 445 lines · 48 tokens per session scan A 127b32c3ab16
linkedin-ads-audience-guide is a skill published in the GitHub repository swan-gtm/gtm-skills (153 stars, last pushed 2d ago), licensed MIT. It adds 48 tokens to every session and 5,236 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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