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.
git clone --depth 1 https://github.com/thatrebeccarae/claude-marketingWrote 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/rules/thatrebeccarae/claude-marketing/linkedin-ads)<a href="https://agentmods.dev/rules/thatrebeccarae/claude-marketing/linkedin-ads"><img src="https://agentmods.dev/badge/rules/thatrebeccarae/claude-marketing/linkedin-ads.svg" alt="Measured on agentmods" 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 | $0.00072 | $0.01500 |
| Opus 5 | $0.00036 | $0.00750 |
| Sonnet 5 | $0.00014 | $0.00300 |
| Haiku 4.5 | $0.00007 | $0.00150 |
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 8d 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 — 141 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LinkedIn Ads
B2B advertising on LinkedIn — campaigns, targeting, creative, Lead Gen Forms, and ABM.
Campaign Types
| Type | Objective | Best For |
|---|---|---|
| Sponsored Content | Brand awareness, engagement, conversions | Thought leadership, content promotion |
| Message Ads | Direct outreach at scale | Event invites, demo requests, high-value offers |
| Text Ads | Cost-efficient clicks | Always-on brand visibility |
| Dynamic Ads | Personalized (follower, spotlight, jobs) | Follower growth, personalized CTAs |
| Document Ads | Lead gen through gated content | Whitepapers, reports, guides |
| Video Ads | Brand awareness, engagement | Product demos, testimonials, thought leadership |
| Conversation Ads | Multi-CTA interactive messages | Complex offers, event + content combos |
| Lead Gen Forms | In-platform lead capture | Gated content, demo requests, newsletter signups |
Targeting Capabilities
Professional Targeting (LinkedIn-Exclusive)
| Dimension | Examples | Use Case |
|---|---|---|
| Job Title | VP Marketing, CMO, Head of Growth | Role-specific targeting |
| Job Function | Marketing, Sales, Engineering | Broad function targeting |
| Seniority | C-Suite, VP, Director, Manager, Entry | Decision-maker targeting |
| Company Name | Specific company lists | ABM campaigns |
| Company Size | 1-10, 11-50, 51-200, 201-500, 500+ | Segment by org size |
| Company Industry | SaaS, Healthcare, Finance, etc. | Vertical targeting |
| Skills | Digital Marketing, SEO, Data Analysis | Interest/expertise targeting |
| Groups | LinkedIn Group membership | Community targeting |
| Years of Experience | 1-2, 3-5, 6-10, 10+ | Career stage |
| Education | Degree, field of study, school | Academic targeting |
Matched Audiences
| Type | Source | Use Case |
|---|---|---|
| Contact Targeting | Email lists (300+ match) | CRM retargeting, ABM |
| Company Targeting | Company name lists | ABM account lists |
| Website Retargeting | Insight Tag pixel | Site visitor retargeting |
| Lookalike Audiences | Expand from any source | Scale proven audiences |
| Engagement Retargeting | Ad/page engagers | Warm audience nurturing |
| Event Retargeting | LinkedIn Event attendees | Post-event follow-up |
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.
- 8d ago First seen · 141 lines · 72 tokens per session scan A 5ba09240ffa6
linkedin-ads is a cursor rule published in the GitHub repository thatrebeccarae/claude-marketing (132 stars, last pushed 3mo ago), licensed MIT. It adds 72 tokens to every session and 1,500 once invoked, about $0.0004 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 2026-08-30.
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