ads-linkedin

ads-linkedin is a skill for Claude Code from naveedharri/benai-skills. It costs 85 tokens per session (1,267 once invoked), scanned A, original, MIT.

A review guide for checking LinkedIn advertising accounts, which are used by businesses to reach professional audiences. It examines setup, audience choices, ad quality, lead forms, and bidding.

In plain words
What is it for?
Use it to review LinkedIn campaign data, mark checks as passed or needing attention, calculate an account health score, and create an action plan.
Why use it?
It helps find tracking problems, poorly targeted audiences, weak ads, and other issues that can make business advertising harder to measure or less relevant.

Skill for Claude Code

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

Part of the ads plugin — 14 skills shipped together

Good fit Use it to review LinkedIn campaign data, mark checks as passed or needing attention, calculate an account health score, and create an action plan.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/naveedharri/benai-skills/ads-linkedin
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 naveedharri/benai-skills --skill ads-linkedin
Clone the repo
git clone --depth 1 https://github.com/naveedharri/benai-skills

Made for: Claude Code.

Or install ads, the plugin that ships this one along with the rest of its 14 skills.

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 ads-linkedin

README.md
[![agentmods](https://agentmods.dev/badge/skills/naveedharri/benai-skills/ads-linkedin/github.svg)](https://agentmods.dev/skills/naveedharri/benai-skills/ads-linkedin)
Your own site
<a href="https://agentmods.dev/skills/naveedharri/benai-skills/ads-linkedin"><img src="https://agentmods.dev/badge/skills/naveedharri/benai-skills/ads-linkedin/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 ads-linkedin

Your own site · 80×15
<a href="https://agentmods.dev/skills/naveedharri/benai-skills/ads-linkedin"><img src="https://agentmods.dev/badge/skills/naveedharri/benai-skills/ads-linkedin.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 85 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,267 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. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
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 $0.00085 $0.01267
Opus 5 $0.00043 $0.00633
Sonnet 5 $0.00017 $0.00253
Haiku 4.5 $0.00009 $0.00127

Measured 12d ago against content hash 1b74e7f37724, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

ads-linkedin 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 12d 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.

Origin

Copies of this mod

2 near-identical copies found in the catalogue:

plugins/ads/skills/ads-linkedin/SKILL.md · 124 lines

How it starts

The opening of the file, as written. The whole thing — 124 lines — stays where its author put it; the contents beside it link to each section on GitHub.

LinkedIn Ads Deep Analysis

Process

  1. Collect LinkedIn Ads data (Campaign Manager export, Insight Tag status)
  2. Read ads/references/linkedin-audit.md for full 25-check audit
  3. Read ads/references/benchmarks.md for LinkedIn-specific benchmarks
  4. Read ads/references/scoring-system.md for weighted scoring
  5. Evaluate all applicable checks as PASS, WARNING, or FAIL
  6. Calculate LinkedIn Ads Health Score (0-100)
  7. Generate findings report with action plan

What to Analyze

Technical Setup (25% weight)

  • Insight Tag installed and firing on all pages (L01)
  • Conversions API (CAPI) active — launched 2025 (L02)
  • Conversion events configured for full funnel
  • Revenue attribution tracking enabled

Audience Targeting (25% weight)

  • Job title targeting uses specific titles, not just functions (L03)
  • Company size filtering matches ICP (L04)
  • Seniority level appropriate for offer (L05)
  • Matched Audiences active: retargeting + contact lists (L06)
  • ABM company lists uploaded (up to 300,000 companies) (L07)
  • Audience expansion OFF for precision campaigns, ON for scale (L08)
  • Predictive audiences tested — replaced Lookalikes Feb 2024 (L09)

Creative Quality (20% weight)

  • Thought Leader Ads active, ≥30% budget allocation for B2B (L10)
  • Ad format diversity: ≥2 formats tested (L11)
  • Video ads tested (L12)
  • Creative refresh every 4-6 weeks (L13)

Lead Gen & Performance (15% weight)

  • Lead Gen Form ≤5 fields (13% CVR benchmark) (L14)
  • Lead Gen Form synced to CRM in real-time (L15)
  • Campaign objective matches funnel stage (L18)
  • A/B testing active: creative or audience (L19)
  • Message ad frequency ≤1 per 30-45 days (L20)

Bidding & Budget (15% weight)

  • Bid strategy: CPS for Messages, Max Delivery for Content (L16)
  • Daily budget ≥$50 for Sponsored Content (L17)
  • CTR ≥0.44% for Sponsored Content (L21)
  • CPC within benchmark: $5-7 average, senior $6.40+ (L22)
  • Lead-to-opportunity rate tracked, not just CPL (L23)
  • Attribution: 30-day click / 7-day view configured (L24)
  • Demographics report reviewed monthly (L25)

Read the full file on GitHub · 124 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. 12d ago First seen · 124 lines · 85 tokens per session scan A 1b74e7f37724

Subscribe to this mod's changes

ads-linkedin is a skill published in the GitHub repository naveedharri/benai-skills (61 stars, last pushed 8d ago), licensed MIT. It adds 85 tokens to every session and 1,267 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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