Ad Spend Optimizer

Community

guia-matthieu

ad-spend-optimizer

Analyze paid advertising performance across channels and recommend budget reallocation to maximize ROAS and minimize CAC. Use when: planning quarterly ad budget allocation, diagnosing underperforming ad channels, deciding whether to scale spend on a channel, calculating marginal ROI across Google Ads, Meta, LinkedIn, or TikTok, rebalancing media mix after performance shifts, or setting up a test-and-scale framework for new channels.

Overview

Publisher	guia-matthieu
Repository	`clawfu-skills`
Skill name	`ad-spend-optimizer`
Stars	150
Forks	27
Bundled files	Instructions only
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.
Self-contained
Everything the model needs lives in the instructions — no extra files to sync.
Open source
Published by guia-matthieu on GitHub. Read the source before you install it.

Installation

Install the Ad Spend Optimizer 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.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/guia-matthieu/clawfu-skills/tree/main/skills/acquisition/ad-spend-optimizer

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

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/guia-matthieu/clawfu-skills.git /tmp/clawfu-skills
mkdir -p .claude/skills
cp -r /tmp/clawfu-skills/skills/acquisition/ad-spend-optimizer .claude/skills/ad-spend-optimizer

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

Use it in TypingMind

Enable Ad Spend Optimizer 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 Ad Spend Optimizer 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 Ad Spend Optimizer 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.

Ad Spend Optimizer

Analyze paid advertising performance across channels and recommend budget reallocation to maximize ROAS and minimize CAC.

When to Use This Skill

Quarterly budget planning — reallocate spend based on performance data
Channel mix optimization — find the right balance across platforms
Performance troubleshooting — diagnose why CAC is rising or ROAS declining
Scaling decisions — determine if a channel has headroom to scale
New channel testing — structure test budgets with clear success criteria

Methodology Foundation

Aspect	Details
Source	Marginal ROI optimization + portfolio theory for marketing
Core Principle	Allocate each dollar where the marginal return is highest — shift spend from diminishing-returns channels to underspent ones
Framework	70/20/10 — 70% proven channels, 20% optimization tests, 10% new channel experiments

What Claude Does vs What You Decide

Claude Does	You Decide
Calculates ROAS, CAC, and CPL per channel and campaign	Total budget constraints
Identifies diminishing returns and reallocation opportunities	Risk tolerance for new channels
Models projected outcomes for different allocation scenarios	Business priorities and brand considerations
Creates monitoring dashboards and alert thresholds	Platform selection and creative direction

Instructions

Step 1: Audit Current Performance

Collect these metrics per channel and campaign:

Metric	Formula	Healthy Range
ROAS	Revenue ÷ Ad Spend	>3:1 for most B2B/B2C
CAC	Ad Spend ÷ New Customers	<LTV ÷ 3
CPL	Ad Spend ÷ Leads	Varies by industry
CTR	Clicks ÷ Impressions	>1% search, >0.5% social
Conv Rate	Conversions ÷ Clicks	>2% landing pages

Validation checkpoint: If data is missing for any channel, flag it — incomplete data leads to wrong reallocations.

Step 2: Attribution Analysis

Choose the model that matches the business:

Model	Best For	Trade-off
Last Click	Direct response, short cycles	Ignores awareness
First Click	Awareness campaigns	Ignores conversion assist
Linear	Balanced multi-touch view	Dilutes signal
Time Decay	Shorter sales cycles	Biases toward bottom-funnel
Position-Based	Balanced with emphasis	May miss mid-funnel
Data-Driven	Sophisticated, enough data	Requires volume

Step 3: Calculate Marginal ROI

For each channel, answer: Where does the next $1 produce the most return?

Signal	Meaning	Action
CAC well below target	Headroom to scale	Increase spend 50%, monitor weekly
CAC at target	Optimized	Maintain, test creative
CAC above target	Diminishing returns	Reduce spend, reallocate
Low volume, good CAC	Underinvested	Scale cautiously (2x)
High volume, rising CAC	Hitting ceiling	Cap spend, diversify

Step 4: Model Reallocation Scenarios

Build 3 scenarios (conservative, moderate, aggressive) showing projected leads, CAC, and ROAS at each budget level. Include:

Per-channel breakdowns with expected performance
Warning thresholds — CAC levels that trigger spend cuts
Implementation timeline — weekly changes, not all at once

Step 5: Implement and Monitor

Weekly monitoring checklist:

Spend pacing vs. plan
CAC by channel vs. target
Lead volume vs. forecast
Any channel crossing warning threshold?

Scaling rule: If CAC stays 15%+ below target for 2 consecutive weeks, increase spend by 25%. If CAC exceeds target for 2 weeks, reduce by 25%.

Examples

Example: B2B SaaS Budget Reallocation

Input: $100K/month — Google ($50K), Meta ($30K), LinkedIn ($15K), Other ($5K). Target: $200 CAC, 500 leads/month. Current: 395 leads, $253 CAC.

Diagnosis:

Google Display ($15K → 30 leads, $500 CAC) — cut entirely
Meta Lookalike ($15K → 85 leads, $176 CAC) — star performer, scale
LinkedIn Lead Gen ($5K → 10 leads, $500 CAC) — cut

Proposed reallocation:

Channel	Current	Proposed	Expected CAC
Google Ads	$50K	$35K	$206
Meta	$30K	$50K	$196
LinkedIn	$15K	$8K	$286
Testing	$5K	$7K	Variable

Projected result: 473 leads (+20%), $211 CAC (-17%).

Skill Boundaries

What This Skill Does Well

Analyzing multi-channel ad performance from provided data
Recommending budget shifts based on marginal ROI
Modeling reallocation scenarios with projected outcomes
Creating monitoring frameworks with alert thresholds

What This Skill Cannot Do

Access ad platform accounts or pull live data
Make real-time bid adjustments or campaign changes
Evaluate creative quality (headlines, images, video)
Account for brand lift or offline conversion effects

References

Google Ads Optimization Guide
Meta Business Suite Best Practices
LinkedIn Marketing Solutions
Common Thread Collective — ad spend allocation methodology

Related Skills

google-ads-expert — Google-specific campaign optimization
aarrr-metrics — Full funnel view beyond paid acquisition
growth-loops — Sustainable growth beyond paid channels

Frequently asked questions

What does the Ad Spend Optimizer AI skill do?

Why use Ad Spend Optimizer on TypingMind?

Because you install it once and use it with any model. Ad Spend Optimizer 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 Ad Spend Optimizer in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/guia-matthieu/clawfu-skills/tree/main/skills/acquisition/ad-spend-optimizer. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Ad Spend Optimizer?

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 Ad Spend Optimizer?

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

Is the Ad Spend Optimizer AI skill free?

Yes. It is published on GitHub by guia-matthieu 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

Ad Spend Optimizer

When to Use This Skill

Methodology Foundation

What Claude Does vs What You Decide

Instructions

Step 1: Audit Current Performance

Step 2: Attribution Analysis

Step 3: Calculate Marginal ROI

Step 4: Model Reallocation Scenarios

Step 5: Implement and Monitor

Examples

Example: B2B SaaS Budget Reallocation

Skill Boundaries

What This Skill Does Well

What This Skill Cannot Do

References

Related Skills

Frequently asked questions

Related AI Skills

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