ads
Helps your agent with paid advertising campaigns. It's useful for managing ads on platforms like Google and Facebook.
What it does
Helps create and manage paid ads on platforms like Google, Facebook, and LinkedIn. The goal is to attract customers efficiently.
When to use it
- ✓Need to create an ad campaign from scratch.
- ✓Want to know if paid advertising is worth the investment.
- ✓Need to optimize ads that aren't performing well.
Example of what to ask your AI
“I want to create a Google Ads campaign to sell my online course. What's the ideal budget and who should I target?”
Want more? Open the sections below.
How to use
The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.
Skill link
Ready-to-paste message
How it works
The AI asks about your goals (sales, leads), target audience, what you're promoting, and your budget. With this information, it suggests how to set up and manage your ads.
Installation
Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.
In the terminal (Claude Code, Codex, Cursor), in your project folder:
npx skills add coreyhaines31/marketingskills --skill adsOr download just the file:
curl -L https://raw.githubusercontent.com/coreyhaines31/marketingskills/main/skills/ads/SKILL.md -o SKILL.mdTechnical content (original SKILL.md)
Original text by the author, straight from GitHub.
Paid Ads
You are an expert performance marketer with direct access to ad platform accounts. Your goal is to help create, optimize, and scale paid advertising campaigns that drive efficient customer acquisition.
Before Starting
Check for product marketing context first:
If .agents/product-marketing.md exists (or .claude/product-marketing.md, or the legacy product-marketing-context.md filename, in older setups), read it before asking questions. Use that context and only ask for information not already covered or specific to this task.
Check its Paid Ads section for targets, floors, conversion definitions, and what you may change, and its Learnings section before proposing tests: angles, audiences, and offers already tested there shouldn't come back as new ideas.
Gather this context (ask if not provided):
1. Campaign Goals
- What's the primary objective? (Awareness, traffic, leads, sales, app installs)
- What's the target CPA or ROAS?
- What's the monthly/weekly budget?
- Any constraints? (Brand guidelines, compliance, geographic)
2. Product & Offer
- What are you promoting? (Product, free trial, lead magnet, demo)
- What's the landing page URL?
- What makes this offer compelling?
3. Audience
- Who is the ideal customer?
- What problem does your product solve for them?
- What are they searching for or interested in?
- Do you have existing customer data for lookalikes?
4. Current State
- Have you run ads before? What worked/didn't?
- Do you have existing pixel/conversion data?
- What's your current funnel conversion rate?
Reference Routing
This skill's depth lives in references — load by intent. For any operational decision on a live account (kill/keep/scale/budget), load the relevant playbook before answering; the thresholds live there, not here.
| User intent | Load | Covers |
|---|---|---|
| "Can I afford this channel?", payback math, budgeting per plan, whether LTV:CAC lies | payback-period.md | Why LTV:CAC is useless (4 flaws), Gross-margin payback and cohort recovery (3–12mo planning target), $9-vs-$999 worked examples, OOH+social, narrative momentum |
| B2B strategy, funnel stages, budget splits, kill rules, lead quality, breakeven math | b2b-paid-playbook.md | Demand lifecycle, leading/lagging signals, kill rules, offline conversion loop, U/B/F lead scoring, scaling quadrant |
| Meta operations: when to kill/graduate/scale an ad, fatigue, testing structure, partnership/creator ads, declining reach | meta-decision-system.md | TCPL-anchored decision tree, ad-count ceiling, 80/20 CBO structure, fatigue bands, lead forms, Advantage+ transition, partnership-ads playbook, rolling-reach signal |
| LinkedIn operations: bidding, audience sizing, scaling, benchmarks, TLAs, formats | linkedin-b2b-playbook.md | Bidding progression, penetration scaling, sizing rules, funnel benchmarks, document/conversation ads, audit shortlist |
| Google Search: what to spend on first, structure, match types, negatives, PMax | google-search-playbook.md | Intent ladder, account structure, match-type gates, negatives, bidding by volume, offline conversions, PMax guardrails |
| Named-account targeting, pipeline acceleration, cross-channel retargeting | abm-playbook.md | LinkedIn/Meta ABM, list mechanics, acceleration campaigns, UTM cross-channel remarketing, ABM measurement |
| Generating Google RSAs | rsa-output-spec.md | Mandatory output spec — limits, sidecars, template, self-check |
| Weekly or daily account check, "what changed," stakeholder readout | operating-check.md | Ordered six-step check, HOLD/FIX/TEST/INVESTIGATE calls, facts/hypotheses/missing data, Friday readout under 200 words |
| Auditing a live account, grading account health, quoting benchmarks, recommending changes | audit-guardrails.md | Pass/fail/unknown scoring, evidence coverage, recommendation safety, hard stops, benchmark discipline |
…
FAQ
What ad platforms can the AI help with?
It can help with Google Ads, Meta (Facebook/Instagram), LinkedIn, Twitter/X, and other paid ad platforms.
What are 'paid ads'?
These are advertisements you pay to display on online platforms, like Google and social media, to reach more people interested in your product or service.
What does 'optimize' ads mean?
Optimizing means adjusting ads so they perform better, spending less money to get more results (like sales or leads).
Details
Original description by the author
When the user wants help with paid advertising campaigns on Google Ads, Meta (Facebook/Instagram), LinkedIn, Twitter/X, or other ad platforms. Also use when the user mentions 'PPC,' 'paid media,' 'ROAS,' 'CPA,' 'ad campaign,' 'retargeting,' 'audience targeting,' 'Google Ads,' 'Facebook ads,' 'LinkedIn ads,' 'ad budget,' 'cost per click,' 'ad spend,' 'should I run ads,' 'ABM,' 'account-based market
Popularity
- GitHub stars
- 55k
- Ranking position
- #1754
- License
- MIT
- Updated
- 10/15/2026
Comments
- …
More skills from this repository
ab-testing
Helps your agent plan and run A/B tests. It's useful for comparing different versions of something to see which works better.
coreyhaines31/marketingskills
ad-creative
Has your agent create and improve ads. It's useful for generating more effective ad copy for different platforms.
coreyhaines31/marketingskills
ai-seo
Helps your agent optimize content for AI searches. It's useful for getting your content to appear in AI-generated answers.
coreyhaines31/marketingskills
analytics
Helps your agent set up and improve data tracking. It's useful for understanding how users interact with your site or app.
coreyhaines31/marketingskills
Similar skills
competitive-platform-analysis
Helps understand your competitors. It identifies, categorizes, and filters competing companies before any deeper analysis.
affaan-m/ECC
competitive-report-structure
Creates comprehensive competitor reports. It combines information to show the overall landscape, recommendations, and helps align the team.
affaan-m/ECC
lead-intelligence
Helps your agent find and contact important people. It analyzes leads, suggests approaches, and manages outreach via email and social media.
affaan-m/ECC