Linkedin Ads Ctr Signal logo

Linkedin Ads Ctr Signal

Organization
swan-gtm
linkedin-ads-ctr-signal

Use this skill to turn LinkedIn Ads engagement into prioritized, pre-drafted outbound. Runs daily to pull LinkedIn Ads analytics, find the companies clicking your ads above a CTR threshold, suppress customers/open-deals/recently-touched accounts, and draft outreach informed by whichever ad themes are landing right now — all saved as drafts for human review, never sent automatically.

Overview

Publisherswan-gtm
Repositorygtm-skills
Skill namelinkedin-ads-ctr-signal
Stars
216
Forks
103
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by swan-gtm on GitHub. Read the source before you install it.

Installation

Install the Linkedin Ads Ctr Signal AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/swan-gtm/gtm-skills/tree/main/skills/brianne-thomas/linkedin-ads-ctr-signal
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/swan-gtm/gtm-skills.git /tmp/gtm-skills
mkdir -p .claude/skills
cp -r /tmp/gtm-skills/skills/brianne-thomas/linkedin-ads-ctr-signal .claude/skills/linkedin-ads-ctr-signal
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Linkedin Ads Ctr Signal in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Linkedin Ads Ctr Signal on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Linkedin Ads Ctr Signal is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Run this daily to turn LinkedIn Ads engagement into prioritized, pre-drafted outbound. The premise: if a company is clicking your ads at an unusually high rate, they're paying attention right now — reach them while the intent is warm. The skill pulls LinkedIn Ads analytics, finds the high-CTR companies, suppresses anyone you shouldn't touch, and drafts outreach informed by whichever ad themes are actually landing — all saved as drafts for human review, never sent automatically.

When to use it

  • You run LinkedIn Ads and want ad engagement to feed outbound instead of sitting in a dashboard.
  • You want a daily, low-effort signal that surfaces "who's warming up" without a rep watching analytics.
  • You want outreach angles grounded in the messaging your audience is currently responding to.

Skip it if you don't run LinkedIn Ads, or if your motion is purely inbound.

Step 1 — Find the active ad accounts

Pull the LinkedIn ad accounts connected to your org and note the account ID(s). Every analytics query below is scoped to these.

Step 2 — Company-level analytics (the engagement signal)

Query LinkedIn Ads analytics for the last 7 days, pivoted by company, returning impressions and clicks, aggregated across the period, filtered to the active ad accounts. This returns engagement by the LinkedIn company of the member who interacted — the raw intent signal.

Step 3 — Creative-level analytics (ad-theme intelligence)

In parallel, run a second query at the individual-ad (creative) level for the same 7 days — impressions, clicks, and each row's creative reference. Compute CTR per creative and rank by clicks. For the top 5 creatives, fetch the full creative details (post reference, headline, intro text, copy) and build a ranked list: clicks and CTR, headline and intro, and the inferred content theme/angle. If a creative resolves only to a post reference, infer the theme from the campaign name instead — an acceptable fallback. This ranked summary is the ad-theme context passed to every company handled in Step 6.

Step 4 — Calculate CTR and filter

For each company from Step 2: CTR = clicks ÷ impressions × 100. Keep only companies with CTR ≥ 2% and impressions > 0. Everything else drops.

Step 5 — Suppression check

For each qualifying company, look it up in your workspace by name or LinkedIn reference and check its memory for a Last CTR signal processed: YYYY-MM-DD note. Skip if it was processed in the last 30 days. Also stop immediately for any company that is:

  • tagged "suppress alerts"
  • a current customer (funnel stage Customer, or CRM lifecycle stage customer)
  • already in an open deal (open deal count > 0)

Suppression timestamp rule: for every company you skip — for any reason — immediately write Last CTR signal processed: [today] to its memory. This audit trail is what stops the daily run from re-processing the same accounts tomorrow.

Step 6 — Delegate one company at a time

For each company that clears suppression, hand a sub-agent everything it needs: company name, LinkedIn company reference, last-7-day impressions and clicks, CTR, and the ad-theme context from Step 3.

The sub-agent researches the company and drafts outreach end to end, using the top-performing ad themes to inform the angle — the topics and pain points resonating with this audience right now. It must never reference ads, clicks, tracking, or targeting in the outreach itself. That's background intel that shapes the message, not something the recipient should ever see. After it finishes, it writes the CTR-processed timestamp to the company's memory.

All outreach is saved as drafts. Nothing sends automatically — every message goes through manual review.

What good looks like

A great run ends with a short list of genuinely warm accounts — real companies clicking your ads above threshold, none of them existing customers or open deals, none re-surfaced from the last 30 days — each with a review-ready draft whose angle quietly mirrors the ad themes currently working, without ever tipping off the recipient that they were tracked. The suppression ledger is updated for every account touched or skipped, so tomorrow's run starts clean.

Mediocre looks like: alerting on one-click flukes because impressions weren't checked, drafting to customers or live deals, re-drafting the same accounts every day because timestamps weren't written, or outreach that leans on ad data so heavily it reads as creepy surveillance instead of a relevant, timely message.

Frequently asked questions

What does the Linkedin Ads Ctr Signal AI skill do?

Use this skill to turn LinkedIn Ads engagement into prioritized, pre-drafted outbound. Runs daily to pull LinkedIn Ads analytics, find the companies clicking your ads above a CTR threshold, suppress customers/open-deals/recently-touched accounts, and draft outreach informed by whichever ad themes are landing right now — all saved as drafts for human review, never sent automatically.

Why use Linkedin Ads Ctr Signal on TypingMind?

Because you install it once and use it with any model. Linkedin Ads Ctr Signal is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Linkedin Ads Ctr Signal in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/swan-gtm/gtm-skills/tree/main/skills/brianne-thomas/linkedin-ads-ctr-signal. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Linkedin Ads Ctr Signal?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Linkedin Ads Ctr Signal?

As many as you like. As long as a model supports skills, you can use Linkedin Ads Ctr Signal with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Linkedin Ads Ctr Signal AI skill free?

Yes. It is published on GitHub by swan-gtm under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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