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Google Ads

CommunityPopular
kostja94
google-ads

When the user wants to set up, optimize, or manage Google Ads campaigns. Also use when the user mentions "Google Ads," "Google Search Ads," "PPC," "SEM," "PMF testing with ads," "test product-market fit," "Responsive Search Ads," "RSA," "Performance Max," "Quality Score," "keyword bidding," or "Google Display/YouTube ads." For paid mix, use paid-ads-strategy.

Overview

Publisherkostja94
Repositorymarketing-skills
Skill namegoogle-ads
Stars
1K
Forks
139
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 kostja94 on GitHub. Read the source before you install it.

Installation

Install the Google Ads 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/kostja94/marketing-skills/tree/main/skills/paid-ads/platforms/google-ads
  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/kostja94/marketing-skills.git /tmp/marketing-skills
mkdir -p .claude/skills
cp -r /tmp/marketing-skills/skills/paid-ads/platforms/google-ads .claude/skills/google-ads
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Google Ads 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 Google Ads 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 Google Ads 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.

Paid Ads: Google Ads

Guides Google Ads setup, campaign structure, keyword targeting, and optimization. Google Ads excels at high-intent search traffic; use when people actively search for your solution.

When invoking: On first use, if helpful, open with 1–2 sentences on what this skill covers and why it matters, then provide the main output. On subsequent use or when the user asks to skip, go directly to the main output.

Two Modes: PMF Testing vs Conversion-Driven

ModeWhenBudgetLanding pageMetrics
PMF testingPre-PMF; validate idea before building$47–500; start smallSimple LP: headline, benefits, problem solved, CTA ("Join Waitlist," "Get Early Access")CTR, sign-up rate, bounce rate; low CTR/high bounce = messaging/positioning issue
Conversion-drivenPMF validated; commercializationScale; ROAS targetFull funnel; ad-to-page alignmentROAS, CAC, conversion rate

PMF testing: No full product needed. Build landing page with Unbounce, Carrd, or Webflow. Run ads to relevant search terms; measure clicks, engagement, signups. Test messaging (e.g., "Fastest App for Freelancers" vs "Simplest Time Tracker for Teams"), pricing (different price points in ads/LP), and audiences (keyword targeting, in-market). Allow 4–6 weeks for PMax learning phase. Use as learning tool, not just marketing channel.

Reference: Marketing Cactus – Using Google Ads to Test Product-Market Fit

Campaign Structure

Account
├── Campaign: Brand (Search)
├── Campaign: Non-Brand (Search)
├── Campaign: Competitor (Search) — optional; bid on competitor brand + "alternative"/"vs"
├── Campaign: Retargeting (Display)
└── Campaign: Performance Max

Competitor Brand Keywords

When: Bid on "[Competitor] alternative," "[Competitor] vs [You]" to intercept high-intent traffic. Google allows competitor terms as keywords; you cannot use competitor names in ad copy without permission.

Landing page: Use a dedicated landing page (comparison/alternatives page), not a blog article. Users searching competitor brands expect direct alternatives—a blog increases bounce; a comparison page matches intent and converts better. See alternatives-page-generator for structure.

Best practices:

  • Separate campaign; exact/phrase match; add your brand as negative
  • H1 mirrors search intent (e.g., "[Competitor] vs [You]")
  • Feature comparison table; one-line differentiator; strong CTA
  • Expect lower Quality Score, higher CPC than non-brand; optimize LP relevance

Naming: GOOG_[Objective]_[Audience]_[Offer]_[Date] (e.g., GOOG_Search_Brand_Demo_Ongoing)

Campaign Types

TypeBest for
SearchHigh-intent queries; keyword-targeted; landing page critical
DisplayAwareness; retargeting; broader reach
YouTubeVideo; awareness; consideration
Performance MaxAutomated; cross-channel; feed + search + display

Performance Max (PMax) Optimization

Learning period: Run at least 6 weeks for algorithm ramp-up. Works best as complement to Search, not replacement.

Asset groups: Organize by audience intent (e.g., high-intent searchers, cart abandoners, category researchers), not product category alone. Audience signals improve CPA and ROAS vs. no signals.

Asset requirements (per asset group):

  • ≥5 images (include 1200×1200)
  • ≥5 text assets (4 headlines, 5 descriptions)
  • Video when possible
  • Refresh creative regularly to maintain performance

Signals: Add remarketing lists and Customer Match to accelerate learning.

Weekly health check: Flag if brand terms >30% of conversions; unexpected geo conversions; any placement >15% of total spend; asset group performance below "Good."

Keyword Strategy

  • Brand: Protect brand terms; exclude from non-brand campaigns
  • Negative keywords: Build weekly; avoid irrelevant queries. Add support terms (login, forum, pricing, help) from keyword-research—these are existing customers, not prospects.
  • Match types: Broad (discovery) → Phrase → Exact (control)

Keyword sources: Use keyword-research for keyword list, clusters, and intent. Map each cluster to a dedicated landing page; relevance improves Quality Score and lowers CPC.

Quality Score Levers

FactorAction
Expected CTRImprove ad relevance; test headlines
Ad relevanceAlign ad copy to keyword intent
Landing pageAd-to-page alignment; fast load; mobile-friendly

Target: Quality Score ≥6; higher = lower CPC, better ad rank. Benchmark: Improving Quality Score from 5 to 7 can reduce CPC by 30–50%.

Bidding Strategy

Conversions/monthStrategy
<30Manual CPC (smart bidding needs volume to optimize)
30–50Target CPA; minimum for effective smart bidding
50–100Target CPA
100+Target ROAS

Smart bidding: AI-powered bidding (Target CPA, Target ROAS) typically delivers better ROI than manual when conversion volume is sufficient; requires ≥30 conversions in 30 days to work effectively.

Tracking

  • Enhanced Conversions: Server-side signals for better attribution
  • Offline conversion imports: B2B; CRM → Google Ads
  • UTM: Consistent parameters for GA4 cross-check

Paid–Organic Cannibalization

When you rank organically (position 4+) for a keyword and also run PPC, paid ads can absorb clicks that would go to organic. Audit: Cross-reference GSC organic rankings with Search Terms report. If organic ranks well, test pausing PPC on those terms to free budget for higher-impact keywords.

Reference: Backlinko – SEO and PPC: 8 Smart Ways to Align

Pre-Launch Checklist

  • Conversion tracking tested with real conversion
  • Landing page loads <3s; mobile-friendly
  • UTM parameters working
  • Negative keyword list built (include support terms from keyword-research)
  • Budget set; targeting matches audience

Related Skills

  • pmf-strategy: PMF validation framework; when to use PMF testing vs conversion-driven
  • paid-ads-strategy: Channel selection; budget allocation; ad-to-page alignment; competitor brand bidding
  • alternatives-page-generator: Competitor brand keyword ads → dedicated LP (not blog); comparison page structure
  • keyword-research: Keyword list, clusters, intent; support terms for negative keywords; PPC data feeds back SEO priority
  • traffic-analysis: UTM for attribution; paid–organic cannibalization audit
  • landing-page-generator: LP structure for paid traffic; PAA → FAQ
  • analytics-tracking: Conversion tracking; ROAS measurement

Frequently asked questions

What does the Google Ads AI skill do?

When the user wants to set up, optimize, or manage Google Ads campaigns. Also use when the user mentions "Google Ads," "Google Search Ads," "PPC," "SEM," "PMF testing with ads," "test product-market fit," "Responsive Search Ads," "RSA," "Performance Max," "Quality Score," "keyword bidding," or "Google Display/YouTube ads." For paid mix, use paid-ads-strategy.

Why use Google Ads on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/kostja94/marketing-skills/tree/main/skills/paid-ads/platforms/google-ads. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Google Ads?

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 Google Ads?

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

Is the Google Ads AI skill free?

Yes. It is published on GitHub by kostja94 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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