linkedin-cost-monitor

linkedin-cost-monitor is a skill for Claude Code from Ad-Superpowers/ad-superpowers-plugin. It costs 111 tokens per session (5,864 once invoked), scanned A, original, MIT.

A monitor for LinkedIn advertising costs, with a focus on European and Dutch markets. It compares measures such as cost per click and cost per lead with regional or industry expectations and looks for unusual increases.

In plain words
What is it for?
Use it to track LinkedIn ad costs, compare EU or Netherlands benchmarks, spot cost changes, and assess whether campaign costs are unusually high.
Why use it?
It helps determine whether high costs are normal for a market or indicate a problem. Early detection of cost spikes can support better budget decisions.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin. Also seen: positional $N argument.

Part of the ad-superpowers plugin — 120 skills, 35 commands, 5 agents, 1 MCP server shipped together

Good fit Use it to track LinkedIn ad costs, compare EU or Netherlands benchmarks, spot cost changes, and assess whether campaign costs are unusually high.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/ad-superpowers/ad-superpowers-plugin/linkedin-cost-monitor
Install

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.

Any agent
npx skills add Ad-Superpowers/ad-superpowers-plugin --skill linkedin-cost-monitor
Clone the repo
git clone --depth 1 https://github.com/Ad-Superpowers/ad-superpowers-plugin

Made for: Claude Code.

Or install ad-superpowers, the plugin that ships this one along with the rest of its 120 skills, 35 commands, 5 agents, 1 MCP server.

Wrote 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.

agentmods badge for linkedin-cost-monitor

README.md
[![agentmods](https://agentmods.dev/badge/skills/ad-superpowers/ad-superpowers-plugin/linkedin-cost-monitor/github.svg)](https://agentmods.dev/skills/ad-superpowers/ad-superpowers-plugin/linkedin-cost-monitor)
Your own site
<a href="https://agentmods.dev/skills/ad-superpowers/ad-superpowers-plugin/linkedin-cost-monitor"><img src="https://agentmods.dev/badge/skills/ad-superpowers/ad-superpowers-plugin/linkedin-cost-monitor/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.

agentmods 80×15 button for linkedin-cost-monitor

Your own site · 80×15
<a href="https://agentmods.dev/skills/ad-superpowers/ad-superpowers-plugin/linkedin-cost-monitor"><img src="https://agentmods.dev/badge/skills/ad-superpowers/ad-superpowers-plugin/linkedin-cost-monitor.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 111 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 5,864 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00111 $0.05864
Opus 5.5 $0.00044 $0.02346
Sonnet 5.5 $0.00022 $0.01173
Haiku 4.5 $0.00011 $0.00586

Measured 1mo ago against content hash 84e2125091df, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

linkedin-cost-monitor 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.

plugin/skills/linkedin-cost-monitor/SKILL.md · 556 lines

How it starts

The opening of the file, as written. The whole thing — 556 lines — stays where its author put it; the contents beside it link to each section on GitHub.

LinkedIn Cost Monitor

Purpose

Track, analyze, and benchmark LinkedIn Ads costs with a focus on European (EU) and Dutch (NL) markets. This skill helps advertisers understand if their costs are normal for their industry and region, detect cost spikes early, and optimize budget efficiency on the most expensive B2B platform.

The LinkedIn Cost Reality

┌─────────────────────────────────────────────────────────────────────────────┐
│                    LINKEDIN: THE EXPENSIVE TRUTH                             │
├─────────────────────────────────────────────────────────────────────────────┤
│                                                                              │
│  LinkedIn vs Other Platforms (Average CPC):                                 │
│  ───────────────────────────────────────────                                │
│                                                                              │
│  LinkedIn:     ████████████████████████████████  €5-8+                      │
│  Google Ads:   ███████████████                   €2-4                       │
│  Meta:         ████████                          €0.80-2                    │
│  TikTok:       ██████                            €0.50-1.50                 │
│                                                                              │
│  WHY LINKEDIN IS 2-5x MORE EXPENSIVE:                                       │
│  ────────────────────────────────────                                       │
│  ├─ Smaller audience (900M vs 3B+ on Meta)                                  │
│  ├─ Higher value professionals (decision makers)                            │
│  ├─ B2B intent = higher competition                                         │
│  ├─ Limited inventory = premium pricing                                     │
│  └─ 2025 trend: Costs still rising (+20% YoY NL)                           │
│                                                                              │
│  KEY INSIGHT:                                                               │
│  ═══════════                                                                │
│  High CPCs are normal on LinkedIn. The question is:                         │
│  Is your cost HIGH FOR LINKEDIN, or just high compared to Meta?             │
│                                                                              │
└─────────────────────────────────────────────────────────────────────────────┘

Read the full file on GitHub · 556 lines

Changes

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.

  1. 1mo ago First seen · 556 lines · 111 tokens per session scan A 84e2125091df

Subscribe to this mod's changes

linkedin-cost-monitor is a skill published in the GitHub repository Ad-Superpowers/ad-superpowers-plugin (5 stars, last pushed 29d ago), licensed MIT. It adds 111 tokens to every session and 5,864 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 2026-09-11.

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