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 Ad-Superpowers/ad-superpowers-plugin --skill linkedin-revenue-attributiongit clone --depth 1 https://github.com/Ad-Superpowers/ad-superpowers-pluginWrote 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/ad-superpowers/ad-superpowers-plugin/linkedin-revenue-attribution)<a href="https://agentmods.dev/skills/ad-superpowers/ad-superpowers-plugin/linkedin-revenue-attribution"><img src="https://agentmods.dev/badge/skills/ad-superpowers/ad-superpowers-plugin/linkedin-revenue-attribution/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/ad-superpowers/ad-superpowers-plugin/linkedin-revenue-attribution"><img src="https://agentmods.dev/badge/skills/ad-superpowers/ad-superpowers-plugin/linkedin-revenue-attribution.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 | $0.05519 |
| Opus 5.5 | $0.00044 | $0.02208 |
| Sonnet 5.5 | $0.00022 | $0.01104 |
| Haiku 4.5 | $0.00011 | $0.00552 |
Grade A, and why
linkedin-revenue-attribution 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.
How it starts
The opening of the file, as written. The whole thing — 617 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LinkedIn Revenue Attribution
Purpose
Connect LinkedIn Ads to actual revenue and prove B2B ROI. This skill helps advertisers navigate the complex world of B2B attribution, integrate CRM data with LinkedIn, and build executive-ready reporting that demonstrates LinkedIn's true pipeline impact.
The B2B Attribution Challenge
┌─────────────────────────────────────────────────────────────────────────────┐
│ WHY B2B ATTRIBUTION IS HARD │
├─────────────────────────────────────────────────────────────────────────────┤
│ │
│ THE B2B BUYER JOURNEY (typical 90-200+ day cycle) │
│ ───────────────────────────────────────────────── │
│ │
│ Day 1: CMO sees LinkedIn ad while scrolling │
│ Day 7: CFO sees retargeting ad, mentions to CMO │
│ Day 14: CMO Googles brand, visits website │
│ Day 30: Sales gets inbound inquiry │
│ Day 45: First sales call │
│ Day 60: Product demo │
│ Day 90: Proposal sent │
│ Day 120: Negotiation │
│ Day 150: Closed-won │
│ │
│ WHO GETS CREDIT? │
│ ───────────────── │
│ LinkedIn: Created awareness, influenced multiple stakeholders │
│ Google: Got the last click (brand search) │
│ Sales: Closed the deal │
│ │
│ In-platform attribution (LinkedIn Insight Tag): │
│ └─ Captures: Day 1-14 (maybe) │
│ └─ Misses: Day 15-150 (entire sales cycle!) │
│ │
│ KEY INSIGHT: │
│ ═══════════ │
│ LinkedIn's in-platform metrics capture only 20-30% of true impact. │
│ CRM integration is REQUIRED to see the full picture. │
│ │
└─────────────────────────────────────────────────────────────────────────────┘
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.
- 1mo ago First seen · 617 lines · 111 tokens per session scan A 80502eb8f445
linkedin-revenue-attribution 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 5,519 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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