Paid Ads Linkedin

OrganizationPopular

nowork-studio

paid-ads-linkedin

Audit, diagnose, plan, and safely operate connected LinkedIn Ads accounts through the NotFair MCP, with an export-based fallback. Use for LinkedIn advertising, sponsored content, lead-generation forms, job-title or company targeting, campaign groups, creatives, conversions, lead quality, budgets, bids, or approved LinkedIn campaign changes.

Overview

Publisher	nowork-studio
Repository	`notfair-plugin`
Skill name	`paid-ads-linkedin`
Stars	3.8K
Forks	488
Bundled files	2
License	MIT
Links	Skill source Repository Homepage

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.
2 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by nowork-studio on GitHub. Read the source before you install it.

Installation

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

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
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.

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/nowork-studio/notfair-plugin.git /tmp/notfair-plugin
mkdir -p .claude/skills
cp -r /tmp/notfair-plugin/paid-ads/paid-ads-linkedin .claude/skills/paid-ads-linkedin

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Paid Ads Linkedin 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 Paid Ads Linkedin 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 Paid Ads Linkedin 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.

LinkedIn Ads

Read ../shared/operating-contract.md and ../shared/measurement-framework.md before acting. Use live account data from the connected platform; use a supplied export when no verified connector is available.

Establish access and qualified-demand context

Follow ../../docs/mcp-connection.md. Resolve ~~linkedin-ads to the live connection. Use its current instructions and capability descriptions to choose tools, and verify the requested platform and account from live data. Do not infer access from another connected platform.
If the connector is missing or unauthorized, request re-authorization or a current export and keep the result plan/review-only.
Define the sales-qualified conversion, CRM feedback loop, account currency, attribution basis, target CPA or pipeline outcome, and complete date window before diagnosing performance.

Keep lead quantity separate from lead quality. Build targeting hypotheses from job function, seniority, company, industry, or account lists only when the business rationale and audience constraints are defensible.

Read and diagnose

Correlate campaign groups, campaigns, creatives, and analytics as needed. Choose available capabilities for individual objects, conversion rules, lead forms, targeting information, or lead responses; retrieve only what the question needs.

Interpret the platform correctly:

Hierarchy is account → campaign group → campaign → creative.
Money is returned as a major-unit object such as { amount: "50", currencyCode: "USD" }, not micros or cents.
Targeting is a whole tree on the campaign. Preserve existing criteria unless the user explicitly approves replacement.
Campaign type and cost type are immutable after creation.
Lead-form responses contain personal data. Retrieve only when necessary, minimize exposure in the response, and never copy raw lead PII into unrelated artifacts.

For reviews, report spend, impressions, link CTR, leads, qualified leads, CPA, and downstream pipeline or revenue by a complete equivalent period. Name the likely driver only when the data supports it.

Execute approved changes safely

Use dedicated write tools, never the read-only script surface. Show the exact object, current and proposed state, currency exposure, expected effect, and rollback first. Use dry-run previews for spend-affecting creates, budgets, bids, and targeting when available.

Prefer pause/activate over hard deletion; conversion rules and matched audiences may not be deletable through the API.
Create campaign groups, campaigns, and creatives in draft, then review targeting, budget, conversion association, and creative before activation.
Use a stable client request ID only to retry the same uncertain create.
Resolve targeting names to LinkedIn URNs before setting the full targeting tree.
Hashing and event-shape enforcement belong to the connector. Do not expose raw customer identifiers in the final report.
Verify the mutation through returned before/after evidence or a fresh read and report any partial failure.

Finish with the confirmed action, quality metric, observation window, and rollback trigger. If operating from an export, mark recommendations ready_for_review, never published.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

agents/openai.yaml1 KB
evals/evals.json2 KB

Frequently asked questions

What does the Paid Ads Linkedin AI skill do?

Why use Paid Ads Linkedin on TypingMind?

Because you install it once and use it with any model. Paid Ads Linkedin 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 Paid Ads Linkedin in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/nowork-studio/notfair-plugin/tree/main/paid-ads/paid-ads-linkedin. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Paid Ads Linkedin?

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 Paid Ads Linkedin?

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

Is the Paid Ads Linkedin AI skill free?

Yes. It is published on GitHub by nowork-studio 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

LinkedIn Ads

Establish access and qualified-demand context

Read and diagnose

Execute approved changes safely

Bundled files

Frequently asked questions

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