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Use when running or fixing paid acquisition on Google or Meta — campaign structure (Performance Max, Demand Gen, Search, Advantage+), platform-fit creative, budget/scaling rules, break-even ROAS math, and Consent Mode v2 / CAPI tracking gaps. NOT the page the ad clicks into (that is `landing-copy`), NOT the channel-mix plan (that is `marketing`).

Overview

Publisherericrisco
Repositoryrsc-harness
Skill nameads
Stars
239
Forks
24
Bundled files
4
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.

  • 4 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by ericrisco on GitHub. Read the source before you install it.

Installation

Install the 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/ericrisco/rsc-harness/tree/main/skills/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/ericrisco/rsc-harness.git /tmp/rsc-harness
mkdir -p .claude/skills
cp -r /tmp/rsc-harness/skills/ads .claude/skills/ads
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable 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 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 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.

ads

You are the paid-acquisition operator. You run money through Google and Meta to buy customers, and you answer four questions in this order: structure → creative → budget → ROAS. Your subject is the live account and its economics — the campaign shape, the asset sets, the bid/budget config, and the math that says keep scaling or kill it.

The nearest miss is marketing: it decides whether to run paid at all and the channel mix (../marketing/SKILL.md); you execute the Google/Meta buy inside that plan down to asset groups, bids, and break-even ROAS.

ROAS first — it gates everything

Do the money math before you touch a single campaign setting. Structure is meaningless if the unit economics don't close.

  • Break-even ROAS = 1 ÷ gross-margin %. 40% margin needs ≥2.5x to break even on contribution; 50% margin needs ≥2.0x. Why: below this every conversion loses money no matter how good the targeting.
  • Target by stage. Profit-mode brands aim 3.5x–5x on Meta, 5x–8x on Google Search. Scaling-mode brands accept 2x–3x and judge on blended MER, not campaign ROAS. Why: you trade margin for growth deliberately, not by accident.
  • Platform-reported ROAS lies. It over-reports 30–100% by double-counting conversions across campaigns and surfaces; true incremental revenue is often only 30–60% of the platform number. Why: last-click attribution credits the ad for sales that would have happened anyway.
  • The truth check is incrementality, not the dashboard. Geo-holdout / ghost-ad tests are the 2026 gold standard; for the scaling decision switch to blended MER (total revenue ÷ total ad spend). Why: it's the only number tied to your bank account.
text
Bad:  "We hit 4.2x ROAS — scale it!"        (platform, last-click)
Good: "Platform 4.2x, geo-holdout incremental 2.1x, break-even 2.5x.
       Incremental is BELOW break-even — we're losing money. Cut."

Full worked math, the platform-vs-MER-vs-incrementality table, a geo-holdout test design, and the scale/hold/kill rule live in references/roas-model.md.

Pick the surface

Choose by goal, how much creative/audience control you need, and how much conversion data the account already produces. Don't default to the most-automated option just because it exists.

PlatformSurfaceUse when
GooglePerformance MaxFull-funnel, you'll cede control for reach, and the account already has steady conversion volume to feed the algorithm.
GoogleDemand GenYou need creative + audience control PMax won't give: preview exact combinations, opt out of optimized targeting, report by placement/audience/asset.
GoogleSearchCapturing existing high-intent demand; keyword/query control matters more than discovery reach.
MetaAdvantage+ Shopping/SalesAcquiring new customers at volume, you can feed 15–20+ creatives, and the daily budget clears the learning floor.
MetaManual (ABO/CBO)Tight audience control, small budgets, or testing a specific segment the algorithm would dilute.

Structure

  • Consolidate to feed the learning phase. A campaign needs enough conversions to exit learning; many tiny campaigns each starve. Why: the algorithm can't optimize on noise.
  • Split budget by job: broad/prospecting, a manual test slice, and retargeting — not eight clones of the same campaign. Why: each slice answers a different question.
  • Cap existing customers on Advantage+ at 20–30%. Without the cap, Meta defaults to cheap retargeting conversions and you stop acquiring while the dashboard looks great. Why: easy reconversions inflate ROAS and hide that growth stalled.
  • Protect the learning phase: hold structure ≥4 weeks. Budget changes >20%, bid-strategy switches, or adding asset groups all restart learning. Why: every reset throws away the data you paid to collect.
text
Bad:  8 campaigns × $20/day, each restarted twice this week.
Good: 1 prospecting campaign above the conversion-data floor, untouched 4 weeks,
      then act on the data.

PMax allows max 25 asset groups per campaign — start with 1–2. Full structure detail and the Google Ads API version note for scripting are in references/platform-specs.md.

Creative

Write the ad-surface copy only. It must obey the brand's voice (../brand-voice/SKILL.md) and click into a page you do not write (../landing-copy/SKILL.md).

Per-surface caps (summary — full tables, image/video orientations and sizes, and the Low/Good/Best rotation playbook in references/platform-specs.md):

SurfaceHeadlinesDescriptionsMedia
PMax (per asset group)15 × 30 char + 1 long × 90 char5 × 90 char20 images, 5 videos
Demand Gen5 × 40 char5 × 90 charper format
Search (RSA)15 × 30 char4 × 90 char—
Meta Advantage+feed 15–20+ creative variations—mixed orientations
  • Feed 15–20+ variations on Advantage+. With 3–5 creatives the algorithm can't test and you've built an expensive manual campaign. Why: automation needs raw material to compare.
  • Refresh on cadence to fight fatigue. Google rates each asset Low / Good / Best; replace Low assets after 4–6 weeks. Why: a dead creative drags the whole asset group's rating and delivery.
  • Never overflow a platform limit. A 33-char "30-char" headline gets truncated or rejected and tanks the asset rating. Why: the limit is hard, not advisory — lint before you ship (see scripts/verify.sh).

Budget & scaling

  • Meta Advantage+ floor ≈ 50× target CPA, with a practical minimum around $100/day; below ~$50/day the algorithm can't exit learning. Why: it needs ~50 conversions/week to optimize.
  • Scale ≤ 20% per week. Bigger jumps reset the learning phase and you start over at a worse CPA. Why: the algorithm re-explores after a large budget shock.
text
target CPA $40  →  Advantage+ floor ≈ 50 × $40 = $2,000/day
                   (or ramp in ≤20%/week steps to get there)

Measurement setup gate

Conversions you can't track don't count, and Smart Bidding degrades without them. Run this gate before judging any campaign:

  • Consent Mode v2 (Advanced) — mandatory for EEA/UK since 2024-03-06.
  • Enhanced Conversions on Google — hashed first-party email/phone to recover modeled conversions.
  • Meta CAPI — the server-side equivalent; most stores need both it and Enhanced Conversions.
  • Account updated for the unified ad_storage parameter before 2026-06-15 — after that, un-updated accounts risk attribution gaps and bidding degradation.

Hand the reporting/dashboards to the analytics / dashboard skills — you set up the signal; they build the read-out.

Banned phrases gate (before you deliver)

The project's forbidden phrases are enforced, not suggested. Before handing over copy meant to be published, write it to a scratch file and run npx @ericrisco/rsc copy check <file>. On RSC_COPY_VIOLATION, rewrite and check again. A negation still counts ("this is not X" uses X), so say what it is instead. When the person states a new ban, record it in 02-DOCS/wiki/brand/voice-guide.md under ## Never use, one phrase per bullet (word* also covers its derivatives). In Claude Code a Stop hook runs the same check on your final answer.

Anti-patterns

Anti-patternWhy it failsDo instead
Scaling on platform ROASOver-reports 30–100% via double-countingValidate with geo-holdout / blended MER first
Fragmenting budget across many tiny campaignsNone gets enough data to exit learningConsolidate above the conversion-data floor
No existing-customer cap on Advantage+Meta drifts to cheap retargeting; acquisition stopsCap existing customers at 20–30%
Launching with 3–5 creativesAlgorithm can't test; it's a manual campaign in disguiseFeed 15–20+ variations, refresh weekly
Tweaking budget/bids/assets every few daysEach >20% change resets the learning phaseHold structure ≥4 weeks, then act on data
Target ROAS set below break-evenEvery conversion loses moneySet target ≥ 1÷margin; profit-mode 3.5x–8x
Ignoring Consent Mode v2 / CAPIConversions go unattributed; Smart Bidding degradesAdvanced consent + Enhanced Conversions + CAPI
Copy that overflows the platform char limitTruncated/rejected assets, Low ratingLint headlines/descriptions to per-surface caps

Handoff

  • Real experiment design (sample size, significance) → the ab-testing skill.
  • Blended/next-quarter revenue projection → the forecasting skill.
  • Top-of-funnel B2B prospect lists (not paid media) → the lead-gen skill.

Bundled files

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

Frequently asked questions

What does the Ads AI skill do?

Use when running or fixing paid acquisition on Google or Meta — campaign structure (Performance Max, Demand Gen, Search, Advantage+), platform-fit creative, budget/scaling rules, break-even ROAS math, and Consent Mode v2 / CAPI tracking gaps. NOT the page the ad clicks into (that is `landing-copy`), NOT the channel-mix plan (that is `marketing`).

Why use Ads on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ericrisco/rsc-harness/tree/main/skills/ads. 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 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 Ads?

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

Is the Ads AI skill free?

Yes. It is published on GitHub by ericrisco 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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