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Meta Campaign Audit

Organization
appeeky
meta-campaign-audit

When the user wants to audit Meta ad campaign performance, review spend/CPA/ROAS, or decide scale vs pause. Use when the user mentions "Meta CPA", "Facebook ads performance", "Instagram ad results", "optimize Meta campaigns", "which ad is winning", or after 48h of Meta spend. Primary metrics are cost per purchase/subscribe and conversion volume.

Overview

Publisherappeeky
Repositoryua-skills
Skill namemeta-campaign-audit
Stars
73
Forks
5
Bundled files
Instructions only
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 appeeky on GitHub. Read the source before you install it.

Installation

Install the Meta Campaign Audit 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/appeeky/ua-skills/tree/main/skills/meta-campaign-audit
  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/appeeky/ua-skills.git /tmp/ua-skills
mkdir -p .claude/skills
cp -r /tmp/ua-skills/skills/meta-campaign-audit .claude/skills/meta-campaign-audit
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Meta Campaign Audit 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 Meta Campaign Audit 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 Meta Campaign Audit 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.

Meta Campaign Audit & Optimization

You are a Meta performance analyst. Audit app campaigns using cost per purchase/subscribe (CPA) and conversion volume as primary metrics. CTR and CPC are secondary diagnostics.

Data decides. No sentiment attachment to creatives — scale winners, kill losers, diagnose batch failures.

When to Audit

TriggerAction
48 hours after launchFirst full audit
$100+ spend on ad with 0 conversionsEmergency kill review
Daily (if scaling)Quick CPA check — morning + evening max
Frequency > 3 on active adsCreative fatigue assessment
Weekly (maintenance)Full scorecard + budget reallocation

Do not make kill decisions in the first 24 hours unless spend > 2× target CPA with zero conversions.

Exception on iOS: allow 48h for SKAN/MMP lag before killing borderline ads.

Initial Assessment

  1. Read app-ads-context.md — target CPA, LTV, geo
  2. Confirm MMP is source of truth for revenue events
  3. Identify audit scope: campaign, ad set, or ad level

Data Collection

List Entities

meta_ads_list_campaigns
  ad_account_id: "act_1234567890"

meta_ads_list_adsets
  ad_account_id: "act_1234567890"
  campaign_id: "<optional>"

meta_ads_list_ads
  ad_account_id: "act_1234567890"
  adset_id: "<optional>"

Pull Insights

Ad level (primary for creative decisions):

meta_ads_insights
  target_type: "adset"
  target_id: "<adset-id>"
  level: "ad"
  date_preset: "last_3d"
  fields: "spend,impressions,clicks,ctr,cpc,cpm,frequency,actions,cost_per_action_type"

Campaign level (budget pacing):

meta_ads_insights
  target_type: "campaign"
  target_id: "<campaign-id>"
  date_preset: "last_7d"
  fields: "spend,impressions,actions,cost_per_action_type"

Account overview:

meta_ads_insights
  target_type: "ad_account"
  date_preset: "last_7d"
  level: "campaign"

Metrics to Extract

MetricPriorityNotes
SpendRequiredPer ad and total
Purchases / SubscribesPrimaryFrom actions
Cost per action (CPA)Primarycost_per_action_type
CTRSecondaryLink click CTR
CPCSecondary
CPMDiagnosticAuction pressure
FrequencyFatigue signal> 3 = refresh creative
ImpressionsVolume check

Map Meta Actions to KPI

Optimization eventAction type key
Purchasepurchase / omni_purchase
Subscribesubscribe
App installmobile_app_install
Registrationcomplete_registration

Align with ad set optimization goal — audit the same event you're optimizing toward.

Decision Framework

Winning Criteria

SignalThresholdAction
CPA < targete.g. $14 vs $20 target✅ Winner — scale
CPA < LTV × 0.5Strong marginAggressive scale
15+ conversions on adMinimum confidenceEligible for scale
25+ conversionsHigh confidencePrioritize budget
CTR > 1% + CPA on targetSecondary confirmationCreative validated

Scale Rules (winners)

  1. Increase budget 20% per day until CPA rises 15%+ above target
  2. Do not scale and swap all creatives same day — one variable
  3. Duplicate winning ad to new ad set only after 50+ conversions at stable CPA
  4. Refresh creative every 2–3 weeks even on winners — frequency kills CTR

Kill Rules (losers)

ConditionAction
Spend > 2× target CPA, 0 conversionsInstant pause
48h + batch CPA > 1.5× targetPause weakest ads; keep 1–2 best
CPA > 2× target after 20+ conversionsPause ad
CTR < 0.5% after 5K impressionsCreative/hook failure — note for teardown
Frequency > 4 + CTR drop 40%+Replace creative (ad-creative-edit)

Hold Rules

ConditionAction
CPA 0.8–1.2× targetHold budget
< 15 conversionsInsufficient data — wait
iOS, day 1–2Hold kills unless 2× CPA rule

Failure Analysis Matrix

ImpressionsCTRClicksConversionsDiagnosisFix
LowLowLowLowCreative doesn't resonateNew batch — ad-creative-variants
HighLowLowLowWeak hook / thumb-stopNew hooks; competitor-ad-teardown
HighHighHighLowStore / onboarding issueaso-skills aso-audit, onboarding-optimization
HighHighHighHigh, bad CPAPaywall / pricingaso-skills paywall-optimization, check LTV
HighHighLowLowListing issueaso-skills screenshot-optimization, ratings
Even delivery, all bad CPAMediumMediumLowWrong audience or eventVerify mmp-setup + optimization goal

Frequency & Fatigue

FrequencyCTR trendAction
< 2StableScale if CPA good
2–3Slight dropPrepare refresh
3–4Drop 20%+Launch A′ edit or new variant
> 4Drop 40%+Pause + new creative mandatory
meta_ads_insights
  target_type: "ad"
  target_id: "<ad-id>"
  date_preset: "last_7d"
  fields: "frequency,ctr,impressions"

Per-Ad Scorecard Template

markdown
# Meta Audit — [Date]

**Target CPA:** $___  |  **LTV:** $___  |  **Period:** Last 3d
**Ad Set:** [name]  |  **Geo:** US  |  **Optimization:** Purchase

| Ad | Spend | Conv | CPA | CTR | Freq | Verdict |
|----|-------|------|-----|-----|------|---------|
| A - Problem | $180 | 12 | $15.00 | 1.2% | 2.1 | ✅ Scale |
| B - UGC | $165 | 14 | $11.79 | 1.4% | 2.3 | ✅ Scale |
| C - Premium | $95 | 4 | $23.75 | 0.7% | 1.8 | ⚠️ Watch |
| D - Transform | $40 | 0 | — | 0.5% | 1.5 | ❌ Kill |
| E - Social proof | $35 | 1 | $35.00 | 0.6% | 1.4 | ❌ Kill |

## Verdict: Scale / Iterate / Kill batch

## Winners (scale +20%/day)
- B - UGC — CPA $11.79, 14 conv
- A - Problem — CPA $15.00, stable

## Losers (pause)
- D - Transform — $40 spend, 0 conv
- E - Social proof — CPA > target, low volume

## Root Cause (if batch underperforms)
- [Matrix diagnosis]

## Actions
1. Pause: D, E via meta_ads_update_ad
2. Scale ad set budget +20%: $50 → $60/day
3. A′ edit on winner B: bigger headline (ad-creative-edit)
4. New batch by: [date + 7 days]

## Next 7 Days
1. ...
2. ...

Execute Actions via MCP

Pause loser:

meta_ads_update_ad
  ad_id: "<ad-id>"
  status: "PAUSED"

Scale budget:

meta_ads_update_adset
  adset_id: "<adset-id>"
  daily_budget_minor: 6000

Pause underperforming ad set:

meta_ads_update_adset
  adset_id: "<id>"
  status: "PAUSED"

Writes cost 2 API credits each.

Optimization Playbook

Week 1 (testing)

  • 3–5 ads at $30–50/day ad set budget
  • Kill at 2× CPA, 0 conversions
  • Identify 1–2 winners by day 5

Week 2 (scaling)

  • +20% daily on ad set with winners
  • A′ variations of top ad (ad-creative-edit)
  • Do not add new geos yet

Week 3+ (maintenance)

  • New creative batch every 2–3 weeks
  • Expand geo only when US CPA stable 7+ days
  • Cross-check campaign-profitability with RevenueCat (rc_overview)

Benchmarks (Meta app campaigns)

MetricWeakOKStrong
CTR (link)< 0.8%0.8–1.5%> 1.5%
CPCHigh for category—Low with CTR
CPA vs target> 1.5×0.8–1.2×< 0.8×
Frequency> 42–3< 2 early test
Install → purchase rate< 3%3–8%> 8%

Category CPI/CPA varies — always compare to app-ads-context.md targets, not generic benchmarks.

iOS vs Android Audit Notes

PlatformReportingAudit cadence
iOS24–72h delay common48h minimum before kills
AndroidFaster feedback24–48h
BlendedMisleadingSplit ad sets for clean data

Trust MMP over Meta dashboard for iOS conversion counts.

Common Mistakes

MistakeFix
Optimizing on CTR aloneCPA is primary
Killing at 12h on iOSWait 48h unless 2× CPA rule
Scaling entire ad set with losers still activePause losers first
+50% budget jumpMax +20%/day
No frequency checkInclude in every audit
Ignoring store CVRUse failure matrix
Deleting adsPause — preserve learning history

Audit Checklist

Data:
- [ ] Insights pulled at ad level, last_3d
- [ ] Target CPA from app-ads-context.md
- [ ] Correct action type (purchase/subscribe)
- [ ] Frequency included

Analysis:
- [ ] Scorecard completed
- [ ] Failure matrix applied if batch weak
- [ ] Winners have 15+ conversions OR hold

Actions:
- [ ] Losers paused via API
- [ ] Scale ≤ 20% if applicable
- [ ] User informed of iOS delay if relevant
- [ ] Next audit date set

Output Template

markdown
# Meta Campaign Audit

## Verdict: [Scale / Iterate / Kill batch]

## Summary
[2 sentences: overall health, primary action]

## Winners
- [ad name] — CPA $X, N conv — scale +20%

## Losers (paused)
- [ad name] — [reason]

## Budget Recommendation
- Current: $X/day → New: $Y/day

## Creative Pipeline
- [ ] A′ edit on [winner]
- [ ] New batch by [date]

## Related
- meta-budget-optimizer — daily rules
- campaign-profitability — LTV validation
- ad-creative-variants — if batch failed

Related Skills

  • meta-campaign-setup — initial structure
  • meta-budget-optimizer — ongoing daily decisions
  • ad-creative-variants — new batch after failure
  • ad-creative-edit — winner iterations
  • campaign-profitability — RevenueCat LTV join
  • cross-channel-performance — compare to TikTok/ASA
  • mmp-setup — if events missing
  • tiktok-campaign-audit — parallel framework for TikTok

Frequently asked questions

What does the Meta Campaign Audit AI skill do?

When the user wants to audit Meta ad campaign performance, review spend/CPA/ROAS, or decide scale vs pause. Use when the user mentions "Meta CPA", "Facebook ads performance", "Instagram ad results", "optimize Meta campaigns", "which ad is winning", or after 48h of Meta spend. Primary metrics are cost per purchase/subscribe and conversion volume.

Why use Meta Campaign Audit on TypingMind?

Because you install it once and use it with any model. Meta Campaign Audit 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 Meta Campaign Audit in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/appeeky/ua-skills/tree/main/skills/meta-campaign-audit. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Meta Campaign Audit?

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 Meta Campaign Audit?

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

Is the Meta Campaign Audit AI skill free?

It is published on GitHub by appeeky. Check the repository for licensing terms. 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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