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-enterprise-ads-auditgit 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-enterprise-ads-audit)<a href="https://agentmods.dev/skills/swan-gtm/gtm-skills/linkedin-enterprise-ads-audit"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/linkedin-enterprise-ads-audit/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-enterprise-ads-audit"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/linkedin-enterprise-ads-audit.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00043 | $0.00968 |
| Opus 5.5 | $0.00017 | $0.00387 |
| Sonnet 5.5 | $0.00009 | $0.00194 |
| Haiku 4.5 | $0.00004 | $0.00097 |
Grade A, and why
linkedin-enterprise-ads-audit 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 — 53 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Audit enterprise LinkedIn campaigns by looking beyond platform-level vanity metrics, isolating account penetration from generic reach, evaluating native lead form pipeline conversion, and measuring revenue impact against contract margin.
Quick verdict
Execute a two-stage evaluation before adjusting campaign budgets or targeting settings.
Stage 1: Audience precision and target account reach
- Load campaign demographics and match impressions against the designated ABM account list.
- Check whether Audience Expansion or Audience Network placements are draining spend outside target accounts.
- If spend on out-of-ICP accounts exceeds target thresholds, label as Audience Dilution.
- If account coverage is below target depth while frequency rises, label as Reach Bottleneck.
Stage 2: Lead quality and pipeline velocity impact
- Evaluate conversion rates from native Lead Gen Forms vs. landing page requests into qualified opportunities.
- Correlate prior account ad exposure with sales cycle velocity and opportunity creation.
The four tests
A campaign is classified as Healthy & Scalable only if it satisfies all four conditions. Full thresholds are in references/methodology-and-thresholds.md.
| Test | Healthy | Critical |
|---|---|---|
| 1. Account coverage and reach depth | At least 60% of target accounts in the ABM list receive at least one impression in the evaluation window | Less than 30% of target accounts are reached while average frequency per persona climbs |
| 2. Audience saturation and frequency | Average exposure frequency remains balanced alongside stable click-through rates | Frequency per persona exceeds 10 impressions in 30 days, with engagement down more than 25% and rising cost per target account reached |
| 3. Form factor quality ratio | Lead-to-opportunity conversion for native Lead Gen Forms reaches at least 40% of the rate for website demo requests on matching segments | Native Lead Gen Form conversion to qualified opportunity drops below 20% of website demo request performance |
| 4. CAC vs. contract value sustainability | Media Cost per Acquired Customer stays below 20% of ACV multiplied by gross margin, with payback within 12 months | Media Cost per Acquired Customer exceeds 35% of gross-margin-adjusted ACV, or payback exceeds 18 months |
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.
- 9d ago First seen · 53 lines · 43 tokens per session scan A fce536172240
linkedin-enterprise-ads-audit is a skill published in the GitHub repository swan-gtm/gtm-skills (181 stars, last pushed 2d ago), licensed MIT. It adds 43 tokens to every session and 968 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 2026-10-08.
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