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Ad Campaign Analyzer

Community
superamped
ad-campaign-analyzer

Use this skill to grade running ads as Red (stop), Yellow (hold), or Green (scale), detect creative fatigue, analyze LTV:CAC, and recommend scaling actions. Trigger it when reviewing campaign exports or deciding what to kill, keep, or scale.

Overview

Publishersuperamped
Repositoryai-marketing-skills
Skill namead-campaign-analyzer
Stars
73
Forks
6
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 superamped on GitHub. Read the source before you install it.

Installation

Install the Ad Campaign Analyzer 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/superamped/ai-marketing-skills/tree/main/skills/ads/ad-campaign-analyzer
  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/superamped/ai-marketing-skills.git /tmp/ai-marketing-skills
mkdir -p .claude/skills
cp -r /tmp/ai-marketing-skills/skills/ads/ad-campaign-analyzer .claude/skills/ad-campaign-analyzer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ad Campaign Analyzer 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 Campaign Analyzer 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 Campaign Analyzer 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 Campaign Analyzer

Usage

Use when reviewing a running campaign to decide what to kill, keep, or scale. Works for daily 15-minute ad reviews, weekly creative refresh planning, and monthly performance trend reviews.

Process

Step 1: Gather Inputs

Ask the user for:

  1. Campaign data — one of:
    • CSV or table with columns: ad/ad set name, impressions, clicks, conversions, spend, CPA
    • Pasted text from ads manager
    • Structured list of metrics per ad
  2. Target CPA — the maximum they're willing to pay per acquisition
  3. AOV (Average Order Value) — what they earn per conversion on the front end
  4. Product/pricing info — what they sell, offer details, known conversion benchmarks
  5. Daily budget per ad set (optional) — for scaling calculations
  6. Days running (optional) — for statistical significance judgment
  7. Historical data (optional) — from previous review for trend comparison

Step 2: Parse Campaign Data

Normalize the input into a consistent table structure:

Ad / Ad SetImpressionsClicksCTRConversionsSpendCPADays Running

Calculate any missing derived metrics:

  • CTR = clicks / impressions × 100
  • CPA = spend / conversions (∞ if 0 conversions)
  • Conversion rate = conversions / clicks × 100

Step 3: Grade Each Ad — Red / Yellow / Green

🔴 RED = STOP

Kill this ad. It's burning money.

Criteria (any one triggers Red):

  • Spent 1.5–2x target CPA with zero conversions
  • CPA is 2x+ target CPA with statistically significant spend
  • Consistently worsening metrics over multiple days with no improvement signs
  • CTR below 0.5% after 1,000+ impressions (the creative isn't connecting)

Action: Turn off immediately. Redirect budget to greens.

🟡 YELLOW = LEAVE ALONE

Don't touch it. It needs more data or is borderline.

Criteria:

  • CPA is close to target (within 0.5–1.5x) but not enough data to be confident
  • Fewer than 1,000 impressions or fewer than 20 clicks — too early to judge
  • Spend is under 1x target CPA — hasn't had a fair chance yet
  • Metrics are mixed (good CTR but low conversion, or vice versa)

Action: Do nothing. Check again tomorrow. Resist the urge to tweak.

🟢 GREEN = SCALE

This ad is working. Give it more budget.

Criteria:

  • CPA is consistently at or below target CPA
  • Has statistically significant data (generally 10+ conversions)
  • Metrics are stable or improving over time
  • CTR is healthy for the targeting type

Action: Scale using the 20% Rule — increase daily budget by 20% every 48 hours.

Step 4: Benchmark Comparison

Compare each ad's metrics against industry benchmarks:

CTR Benchmarks (by targeting type):

TargetingExpected CTR
Broad / run-of-network1–3%
Interest-based targeting2–4%
Lookalike / community-targeted3–5%

CPA Targets by Offer Price:

Offer Price RangeExpected CPA Range
$7–27 (low ticket)$20–40
$37–97 (mid ticket)$40–120
$97+ (high ticket)Varies — must model LTV

Step 4b: Funnel Debugging — Find the Leak

Before changing creative, check whether the ad is actually the problem. Work from the surface inward:

  1. Creative — Is the CTR acceptable? Low CTR = the ad isn't connecting. Test new hooks, visuals, or headlines.
  2. Landing page — CTR is fine but conversions are flat? The LP is the problem.
  3. Messaging — LP structure looks okay but still no conversions? The fundamental message may not be resonating.
  4. Product and pricing — Different angles all fail? The offer itself may be the issue.
  5. Market — Everything above looks solid but the right people still aren't converting? The segment may be wrong.

Work from #1 → #5 in order. Most founders jump to #3 or #4 when the actual problem is #1 or #2.

Data thresholds — don't debug on noise:

  • < 1,000 impressions per ad variant: Too early to judge CTR.
  • < 100 LP visitors: Too early to judge landing page conversion.
  • < 10 conversions on a green ad: Scale cautiously — the trend may not hold.

Step 5: Flag Creative Fatigue

Check for fatigue signals across the data:

  • Declining CTR over time (even if still "okay" in absolute terms)
  • Rising CPA despite no changes to targeting or budget
  • Dropping conversion rate with stable traffic quality
  • Frequency above 3 (same people seeing the ad too many times)

If fatigue is detected, flag which ads are affected and recommend:

  • New creative variation (different angle, format, or style)
  • Audience refresh (new targeting or exclusions)
  • Copy refresh (same visual, new headline)

Step 6: Profitability Check

The North Star: Is AOV > CPA?

For each green ad, calculate:

  • Profit per conversion = AOV − CPA
  • ROAS = AOV / CPA (must be > 1.0 to be profitable)
  • Break-even CPA = AOV (you make $0 at this point)
  • Margin at current CPA = (AOV − CPA) / AOV × 100

Step 6b: LTV:CAC Health Check

If LTV data is available, assess the pricing-level health of the campaign:

LTV:CAC Ratio Benchmarks:

Pricing FunctionAverage LTV:CAC
No pricing function1.68
Yearly pricing review3.23
Continuous optimization11.09

Interpret the ratio:

  • < 1:1 — Losing money on every customer. Stop spending until unit economics are fixed.
  • 1:1 – 3:1 — Marginal. Campaigns may appear profitable on front-end ROAS but are destroying value over time.
  • 3:1 – 5:1 — Healthy. Scale greens confidently.
  • > 5:1 — Excellent. May be under-investing in acquisition.

Monetization impact reminder: A 1% improvement in monetization yields a 12.7% increase in bottom-line revenue — roughly 4x the impact of acquisition and 2x the impact of retention. If LTV:CAC is weak, the fix may be pricing, not ads.

Step 6c: Attribution Notes

When analyzing multi-channel campaigns, note attribution limitations:

  • Single-channel campaigns: last-click is sufficient.
  • Multi-channel campaigns: flag that attribution is approximate.
  • Don't over-complicate analytics. The goal is action (red/yellow/green), not perfect measurement.

Step 7: Generate Scaling Recommendations

For each green ad, provide specific scaling numbers:

The 20% Rule:

  • Current daily budget → recommended new budget (current × 1.2)
  • When to apply: 48 hours after last budget change
  • Next check-in date

For the overall campaign:

  • Total daily spend recommendation
  • Budget reallocation from reds to greens
  • When to add new creatives to the mix

Output Format

# Campaign Analysis

**Date:** [current date]
**Campaign:** [campaign name or description]
**Period:** [date range of data]
**Target CPA:** $[amount]
**AOV:** $[amount]

---

## Traffic Light Summary

| Grade | Count | % of Spend |
|-------|-------|-----------|
| 🔴 Red (Stop) | X | X% |
| 🟡 Yellow (Wait) | X | X% |
| 🟢 Green (Scale) | X | X% |

**Campaign Health:** [Healthy / Needs Attention / Critical] — [one sentence summary]

---

## Ad-Level Grades

| Ad / Ad Set | Grade | Spend | CPA | Target CPA | CTR | Conv. | Action |
|-------------|-------|-------|-----|-----------|-----|-------|--------|
| [name] | 🔴 | $X | $X | $X | X% | X | Stop — [reason] |
| [name] | 🟡 | $X | $X | $X | X% | X | Wait — [reason] |
| [name] | 🟢 | $X | $X | $X | X% | X | Scale to $X/day |

---

## Benchmark Comparison

| Metric | Your Average | Benchmark | Status |
|--------|-------------|-----------|--------|
| CTR | X% | X–X% | ✅ On track / ⚠️ Below / 🔥 Above |
| Conversion Rate | X% | X–X% | ✅ / ⚠️ / 🔥 |
| CPA | $X | $X–X | ✅ / ⚠️ / 🔥 |

---

## Creative Fatigue Alerts

[List any ads showing fatigue signals, or "No fatigue signals detected."]

---

## Profitability

| Ad / Ad Set | CPA | AOV | Profit/Conv. | ROAS | Margin |
|-------------|-----|-----|-------------|------|--------|
| [name] | $X | $X | $X | X.Xx | X% |

**Overall ROAS:** X.Xx
**Overall Profit/Conversion:** $X

---

## Action Items

### Immediate (Today)
- [ ] Stop: [list red ads]
- [ ] Scale: [list green ads with specific new budgets]

### This Week
- [ ] [Creative refresh, new tests, etc.]

### Review Cadence
- Next daily check: [tomorrow]
- Next weekly review: [date]
- Next monthly review: [date]

---

## Scaling Plan

| Ad | Current Budget | New Budget | Apply On | Next Increase |
|----|---------------|-----------|----------|---------------|
| [name] | $X/day | $X/day | [date] | $X/day on [date] |

**Total daily spend:** $X → $X (recommended)

Rules

  • The grading method is deliberately binary. Red means stop, not "let's give it one more day." Kill losers fast and feed winners.
  • Yellow is the discipline zone. Don't "optimize" yellows. Either there's enough data to judge or there isn't.
  • The 20% rule exists because ad platforms optimize delivery around your budget. Jumping budgets overnight resets the algorithm's learning.
  • CPA is the North Star, not CTR. A high-CTR ad that doesn't convert is worse than a low-CTR ad with great CPA.
  • These benchmarks are starting points. After 2-4 weeks, your own data becomes the benchmark.
  • If everything is red, the problem isn't the ads — it's the offer or the funnel.
  • Creative fatigue is inevitable. Plan for it. Have your next batch of creatives ready before the current ones die.

Frequently asked questions

What does the Ad Campaign Analyzer AI skill do?

Use this skill to grade running ads as Red (stop), Yellow (hold), or Green (scale), detect creative fatigue, analyze LTV:CAC, and recommend scaling actions. Trigger it when reviewing campaign exports or deciding what to kill, keep, or scale.

Why use Ad Campaign Analyzer on TypingMind?

Because you install it once and use it with any model. Ad Campaign Analyzer 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 Campaign Analyzer in TypingMind?

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

Which AI models can use Ad Campaign Analyzer?

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 Campaign Analyzer?

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

Is the Ad Campaign Analyzer AI skill free?

Yes. It is published on GitHub by superamped 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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