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 dalroot/agentic-seo --skill linkedin-adsgit clone --depth 1 https://github.com/dalroot/agentic-seoWrote 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/dalroot/agentic-seo/linkedin-ads)<a href="https://agentmods.dev/skills/dalroot/agentic-seo/linkedin-ads"><img src="https://agentmods.dev/badge/skills/dalroot/agentic-seo/linkedin-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/dalroot/agentic-seo/linkedin-ads"><img src="https://agentmods.dev/badge/skills/dalroot/agentic-seo/linkedin-ads.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 | $0.00111 | $0.02477 |
| Opus 5.5 | $0.00044 | $0.00991 |
| Sonnet 5.5 | $0.00022 | $0.00495 |
| Haiku 4.5 | $0.00011 | $0.00248 |
Grade A, and why
linkedin-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 today.
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 — 150 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Paid Ads: LinkedIn Ads
Guides LinkedIn Ads setup, targeting, and optimization. LinkedIn excels at B2B and professional targeting; use when job title/company targeting matters and ACV is higher.
When invoking: On first use, if helpful, open with 1–2 sentences on what this skill covers and why it matters, then provide the main output. On subsequent use or when the user asks to skip, go directly to the main output.
Account and product context
- Campaign Manager is the main UI: campaignmanager.linkedin.com · Get started (help)
- Marketing Solutions = ads for brands/leads; Talent Solutions = hiring products (separate when choosing objectives).
- Sponsored Content in-feed and Sponsored Messaging generally require a free LinkedIn Page (per official getting-started guidance—confirm in your account).
- Review: build at least ~1–2 days before go-live; last-minute changes may miss launch. Ad set best practices
Campaign structure (naming and hierarchy)
Naming (example): LI_[Objective]_[Audience]_[Offer]_[Date] (e.g. LI_LeadGen_CMOs-SaaS_Whitepaper_Mar24)
Hierarchy in Campaign Manager (names vary slightly over time):
| Level | Role |
|---|---|
| Campaign group | Groups related programs; shared status and budgets (optional). |
| Campaign | Marketing objective; budget/scheduling as configured. |
| Ad set | Accelerate or Classic; audience, placements, format, budget, schedule, bid; conversion/URL params. Compare types |
| Ads | Creative; multiple ads per ad set for A/B. |
Billing model: Objective-based pricing — billed on the action tied to the objective (e.g. clicks for traffic-oriented objectives). Pricing overview
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.
- today First seen · 150 lines · 111 tokens per session scan A 3a5296518a87
linkedin-ads is a skill published in the GitHub repository dalroot/agentic-seo (25 stars, last pushed 6d ago), licensed MIT. It adds 111 tokens to every session and 2,477 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 2026-10-26.
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