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linkedin-ads-research

linkedin-ads-research is a skill for Claude Code, Codex from ad-vertly/adextract-mcp-server. It costs 51 tokens per session (280 once invoked), scanned A, original, MIT.

A research workflow for studying business and employer-brand adverts in LinkedIn Ad Library, LinkedIn's searchable collection of ads.

In plain words
What is it for?
It helps compare advertisers, audiences, messages, calls to action, formats, and campaign goals, then produce evidence-based recommendations.
Why use it?
It organizes scattered adverts into comparable patterns instead of relying on guesses about competitors' messaging.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

not rated 0repo yesterday A scan 51 tokens original MIT

Good fit It helps compare advertisers, audiences, messages, calls to action, formats, and campaign goals, then produce evidence-based recommendations.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/ad-vertly/adextract-mcp-server/linkedin-ads-research
View source ↗ ad-vertly/adextract-mcp-server adextract.co
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-vertly/adextract-mcp-server --skill linkedin-ads-research
Clone the repo
git clone --depth 1 https://github.com/ad-vertly/adextract-mcp-server

Made for: Claude Code, Codex.

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-ads-research

README.md
[![agentmods](https://agentmods.dev/badge/skills/ad-vertly/adextract-mcp-server/linkedin-ads-research/github.svg)](https://agentmods.dev/skills/ad-vertly/adextract-mcp-server/linkedin-ads-research)
Your own site
<a href="https://agentmods.dev/skills/ad-vertly/adextract-mcp-server/linkedin-ads-research"><img src="https://agentmods.dev/badge/skills/ad-vertly/adextract-mcp-server/linkedin-ads-research/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-ads-research

Your own site · 80×15
<a href="https://agentmods.dev/skills/ad-vertly/adextract-mcp-server/linkedin-ads-research"><img src="https://agentmods.dev/badge/skills/ad-vertly/adextract-mcp-server/linkedin-ads-research.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 51 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 280 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 $0.00051 $0.00280
Opus 5.5 $0.00020 $0.00112
Sonnet 5.5 $0.00010 $0.00056
Haiku 4.5 $0.00005 $0.00028

Measured yesterday against content hash 4eeb08e9ae36, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

linkedin-ads-research 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 yesterday.

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.

plugins/adextract/skills/linkedin-ads-research/SKILL.md · 35 lines

What it actually says

LinkedIn Ads Research

When to use

Use this skill for LinkedIn Ad Library research where professional audience, B2B positioning, lead generation, recruiting, or thought leadership context is important.

Tool workflow

  1. Search with search_linkedin_ads using the brand, competitor, product category, or campaign theme.
  2. Apply available country or date filters when market context matters.
  3. Group results by advertiser, message angle, CTA, format, and funnel role.
  4. Use ad_reader on available image or video media before inferring creative strategy.

Research defaults

  • Treat LinkedIn as a professional intent channel; look for proof, credibility, offers, and audience specificity.
  • Separate employer-brand ads from product, event, webinar, report, and demo campaigns.
  • Compare hooks and CTAs across competitors instead of relying only on volume.

Output

Return key advertisers, audience assumptions, offer types, CTA patterns, content formats, funnel role, and 3-5 evidence-backed recommendations for B2B or professional messaging.

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. yesterday First seen · 35 lines · 51 tokens per session scan A 4eeb08e9ae36

Subscribe to this mod's changes

linkedin-ads-research is a skill published in the GitHub repository ad-vertly/adextract-mcp-server (0 stars, last pushed yesterday), licensed MIT. It adds 51 tokens to every session and 280 once invoked, about $0.0002 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 2026-11-06.

Marketing and SEOResearchad-intelligencead-libraryad-spyai-agentscompetitive-intelligencegoogle-ads
Related

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linkedin-ads-research next to 6 skills from other repositories
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