paid-ads

Overview of paid-ads skill

What the paid-ads skill does

The paid-ads skill helps an AI agent plan, evaluate, and optimize paid media campaigns across Google Ads, Meta, LinkedIn, X, and similar platforms. It is built for practical ad decisions like channel selection, budget allocation, audience targeting, bidding direction, campaign structure, and performance diagnosis, not just generic marketing brainstorming.

Who should use the paid-ads skill

This paid-ads skill is best for operators who already have a product, offer, and landing page and need help turning that into a paid acquisition plan. It fits founders, growth marketers, demand gen teams, agencies, and in-house performance marketers who want faster first-pass strategy with fewer missing pieces.

The real job-to-be-done

Most users are not looking for a definition of PPC. They want answers to questions like:

• Which platforms should we start with?
• How should we split budget?
• Is our CPA or CPC actually good?
• How should we target audiences by platform?
• What should the first campaign structure look like?
• What should we fix before launch?

The paid-ads skill is useful because it pushes the conversation toward those operational decisions.

What makes this skill different from a normal ad prompt

The main differentiator is structure. The skill explicitly gathers campaign goals, product and offer details, and audience context before recommending tactics. It also points the agent to reusable references for ad copy templates, audience targeting, and platform setup checklists, which is more grounded than asking for “a paid ads strategy” in one sentence.

Best-fit and non-fit use cases

Best fit:

• New campaign planning
• Platform selection
• Budget and targeting recommendations
• Campaign audits based on metrics
• Launch readiness checks
• Performance troubleshooting

Less ideal:

• Bulk creative generation at scale; use a dedicated creative skill for that
• Landing page conversion work; a CRO skill is a better fit
• Deep account-specific analysis when the model cannot access actual campaign data

How to Use paid-ads skill

Install context for paid-ads

Install the skill from the repository with:

npx skills add https://github.com/coreyhaines31/marketingskills --skill paid-ads

This adds the paid-ads skill from the coreyhaines31/marketingskills repo into your local skills setup.

Read these files first

If you want to understand how the paid-ads skill will behave before relying on it, read these in order:

1. skills/paid-ads/SKILL.md
2. skills/paid-ads/references/platform-setup-checklists.md
3. skills/paid-ads/references/audience-targeting.md
4. skills/paid-ads/references/ad-copy-templates.md
5. skills/paid-ads/evals/evals.json

That path gives you the actual workflow first, then the practical references, then example expectations from evals.

The most important input the skill needs

The paid-ads skill gets much better when you provide:

• campaign objective
• target CPA, CPL, CAC, or ROAS
• budget range
• product or offer
• landing page URL
• target audience
• geography
• sales motion and price point
• known constraints such as compliance, creative limits, or brand restrictions

Without those, the output will usually be too broad to be launch-ready.

Check for product marketing context first

A notable workflow detail in the skill: it tells the agent to check for .agents/product-marketing-context.md or .claude/product-marketing-context.md before asking basic questions. That matters because it reduces repetitive discovery and keeps ad strategy aligned with your positioning. If you use shared context files, make sure they are current before invoking the skill.

Turn a rough request into a strong paid-ads prompt

Weak request:
“Help me with paid ads.”

Stronger request:
“Use the paid-ads skill to recommend a launch plan for a B2B HR SaaS at $99 per seat. Goal is demo requests, budget is $15k/month, target CPL is under $200, US only, sales-led motion, existing traffic is low, and we already have a demo landing page. Recommend platforms, budget split, campaign structure, audience targeting, conversion tracking priorities, and what to test first.”

The stronger version works because it gives the skill enough commercial context to choose channels and constraints intelligently.

What paid-ads usage looks like in practice

Typical paid-ads usage follows this sequence:

1. confirm objective and economics
2. clarify product, offer, and audience
3. choose platform mix
4. define campaign structure
5. map targeting by platform
6. set budgets and metrics
7. check tracking and launch readiness
8. propose optimization and scaling steps

That flow is visible in the skill and reinforced by the setup and targeting references.

Use the references instead of asking for everything from scratch

Three support files materially improve output quality:

• references/platform-setup-checklists.md helps catch missing tracking, tagging, billing, audience, and launch prerequisites
• references/audience-targeting.md helps the agent make platform-specific targeting recommendations instead of vague “target decision-makers”
• references/ad-copy-templates.md gives copy formulas and platform-specific patterns when ad messaging is needed

If your prompt asks for strategy, setup, targeting, and ad copy all at once, these references keep the output from becoming generic.

Best prompt pattern for audits and troubleshooting

The paid-ads skill is also useful after launch, especially when you supply real metrics. Include:

• spend
• impressions
• clicks
• CTR
• CPC
• conversions
• CPA or CPL
• conversion rate
• time period
• platform
• campaign type
• what recently changed

Example:
“Use the paid-ads skill to assess our Google Ads lead gen performance. We spent $15k last month, got 80 leads, CPC is $12, CPL is $180, branded and non-branded search are mixed together, and conversion tracking is set at form submit only. Tell me whether performance looks healthy, what to segment first, and which issues are likely due to structure versus targeting versus offer.”

What this skill is especially good at

Based on the repository signals and evals, the paid-ads skill is strongest at:

• recommending plausible channel mix by business type
• connecting audience type to platform choice
• creating a first-pass campaign structure
• framing success metrics and attribution questions
• identifying setup gaps before spend scales

It is more decision-support-oriented than automation-oriented.

Boundaries to know before install

The skill assumes strategic reasoning, not direct platform execution. It can tell you what to set up and why, but it does not include scripts, API tooling, or account-sync automation. If your workflow depends on pulling live campaign data, bulk editing ad entities, or enforcing account-level rules programmatically, this repo does not provide that.

How to evaluate whether the paid-ads install is worth it

Install paid-ads if your main problem is better briefs and fewer missed planning steps. Skip it if you only need one-off ad copy or if you already have a mature internal paid media playbook that covers discovery, targeting, setup, and optimization in detail. The value here is structured guidance that helps an agent ask smarter questions and produce more usable first drafts.

paid-ads skill FAQ

Is paid-ads good for beginners

Yes, if you already understand the business you are advertising. The skill gives a usable framework for goals, audiences, platforms, and setup. It is less helpful for complete beginners who do not yet know their offer, funnel, or success metric.

Is the paid-ads skill only for Google Ads

No. The repository explicitly covers Google Ads, Meta, LinkedIn, X, and similar platforms. It is more useful when platform choice is still open, because the skill can compare channel fit instead of forcing everything into one network.

How is paid-ads different from a generic marketing prompt

A generic prompt often jumps straight to tactics. The paid-ads skill first establishes campaign goals, offer, audience, and constraints, then uses supporting references for setup and targeting. That usually produces a more operational answer with fewer hidden assumptions.

Can I use paid-ads for Ad Optimization

Yes. The paid-ads skill supports ad optimization work such as diagnosing CPA, CPC, targeting quality, campaign segmentation, and scaling logic. To get useful output, provide actual metrics and account structure details rather than asking only “how do I optimize ads?”

When should I not use paid-ads

Do not use paid-ads as your main tool for:

• landing page CRO
• large-scale creative production
• exact platform UI instructions for every edge case
• account analysis without reliable inputs

In those cases, pair it with a CRO workflow, a creative workflow, or direct platform expertise.

Does paid-ads replace platform-specific expertise

No. It improves planning quality and reduces omission risk, but it does not replace experience with bidding models, attribution quirks, policy restrictions, or account history. Think of it as a strong strategy assistant, not a direct media buyer.

How to Improve paid-ads skill

Give the skill economics, not just goals

The fastest way to improve paid-ads output is to include business economics:

• average order value or contract value
• gross margin if relevant
• acceptable payback period
• target CAC, CPA, or ROAS
• lead-to-close rate for lead gen

This changes recommendations materially. A $99 self-serve SaaS and a high-ACV enterprise offer should not get the same platform mix or budget logic.

Provide audience detail at decision level

“HR teams” is weaker than:
“US-based HR managers and directors at 200-2000 employee companies, mostly in healthcare and manufacturing, buying for compliance and onboarding workflows.”

That level of detail helps the paid-ads skill choose between LinkedIn precision, Google intent capture, and Meta or retargeting support.

Include the offer and landing page

The skill asks for product and offer context for a reason. “Promoting our product” is too vague. Better:

• free trial
• demo request
• pricing page visit
• downloadable guide
• webinar registration

Also include the landing page URL or a short summary of what the page promises. The quality of ad recommendations depends on offer clarity.

Use setup checklists before blaming performance

A common failure mode in paid-ads usage is trying to optimize campaigns that were never properly instrumented. Before changing targeting or bids, use references/platform-setup-checklists.md to verify:

• conversion tracking
• analytics integration
• remarketing audiences
• account foundations
• creative readiness
• lead form or event setup

Bad data creates fake optimization problems.

Ask for campaign structure explicitly

If you want something actionable, request structure directly:

• campaign naming conventions
• brand vs non-brand separation
• prospecting vs retargeting split
• audience segmentation
• geo or offer-based breakdowns
• first test matrix

Otherwise the output may stay at recommendation level and stop short of something your team can build.

Improve ad recommendations with copy angles

When you need messaging, point the skill toward the type of angle you want tested:

• pain-point-led
• social proof
• feature-benefit
• direct response
• urgency
• educational

This aligns with references/ad-copy-templates.md and yields more testable output than “write some ads.”

Common failure modes with paid-ads

Watch for these:

• missing target metric, so recommendations become generic
• no budget range, so platform prioritization is weak
• broad audience description, so targeting advice stays obvious
• no funnel or sales context, so lead quality assumptions are wrong
• asking for optimization without recent metrics
• mixing creative, landing page, and media problems into one vague request

Most disappointing outputs are input-quality issues, not skill-quality issues.

Iterate after the first answer

The best paid-ads guide usage is iterative. After the first response, ask follow-ups like:

• “Reallocate this budget assuming LinkedIn CPL comes in 40% above target.”
• “Now segment by branded vs non-branded search.”
• “Turn this into a 30-day launch checklist.”
• “Give me platform-specific audiences for Meta and LinkedIn only.”
• “Rewrite this plan assuming compliance limits aggressive claims.”

That second pass is often where the skill becomes genuinely implementation-ready.

Use the evals to calibrate expectations

Open evals/evals.json if you want to see what good behavior looks like. The examples show that the paid-ads skill is expected to:

• check for shared product marketing context
• recommend platforms based on business model
• define audience targeting by channel
• suggest budget allocation
• propose success metrics
• give a starting structure and scaling logic

That makes the evals a good sanity check when adapting the skill to your own workflow.

Pair paid-ads with adjacent skills carefully

The repo itself separates concerns. Use paid-ads for channel strategy, targeting, bidding direction, and optimization logic. If your real bottleneck is ad volume production, use a creative-focused skill. If conversion rate on the destination page is the issue, use a CRO-focused workflow. Keeping these scopes separate usually improves output quality and reduces muddled recommendations.