agentsclimarketplace

Ads meta

Skill kennyolofsson23-netizen/claude-code-config/skills/claude-ads/skills/ads-meta

Meta Ads deep analysis covering Facebook and Instagram advertising. Evaluates 46 checks across Pixel/CAPI health, creative diversity and fatigue, account structure, and audience targeting. Includes Advantage+ assessment. Use when user says "Meta Ads", "Facebook Ads", "Instagram Ads", "Advantage+", or "Meta campaign".From its SKILL.md

Install
npx -y skills add kennyolofsson23-netizen/claude-code-config --skill ads-meta

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

2 things to look at

  • no licenseNo license file was found in the repository. Code published without one is not open source by default, so using it at work is a question for whoever answers licensing questions where you are.
  • 0 stars0 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

SKILL.md

5.2 KB, ~1.3k tokens by cl100k_base, as published. Nobody here has run it

Meta Ads Deep Analysis

Process

  1. Collect Meta Ads data (Ads Manager export, Events Manager screenshot, EMQ scores)
  2. Read ads/references/meta-audit.md for full 46-check audit
  3. Read ads/references/benchmarks.md for Meta-specific benchmarks
  4. Read ads/references/scoring-system.md for weighted scoring
  5. Evaluate all applicable checks as PASS, WARNING, or FAIL
  6. Calculate Meta Ads Health Score (0-100)
  7. Generate findings report with action plan

What to Analyze

Pixel / CAPI Health (30% weight)

  • Meta Pixel installed and firing on all pages
  • Conversions API (CAPI) active (30-40% data loss without it post-iOS 14.5)
  • Event deduplication configured (event_id matching, ≥90% dedup rate)
  • Event Match Quality (EMQ) ≥8.0 for Purchase event
  • All standard events configured (ViewContent, AddToCart, Purchase, Lead)
  • Custom conversions created for non-standard events
  • Aggregated Event Measurement (AEM) configured for iOS
  • Domain verification completed
  • Server-side events include customer_information parameters
  • Pixel fires with correct currency and value parameters

Creative (30% weight)

  • ≥3 creative formats active (image, video, carousel, collection)
  • ≥5 creatives per ad set (Meta recommendation)
  • Creative fatigue detection: CTR drop >20% over 14 days = FAIL
  • Video creative: 15s max for Stories/Reels, 30s max for Feed
  • UGC/testimonial creative tested
  • Dynamic Creative Optimization (DCO) tested
  • Ad copy: headline under 40 chars, primary text under 125 chars
  • Creative refresh cadence: every 2-4 weeks for high-spend

Account Structure (20% weight)

  • Campaign Budget Optimization (CBO) vs Ad Set Budget (ABO) intentional
  • Campaign consolidation: ≤5 active campaigns per objective type
  • Learning phase health: <30% ad sets in "Learning Limited" (FAIL >50%)
  • Budget per ad set: ≥5x target CPA (minimum for learning phase exit)
  • Ad set audience overlap <30% (Audience Overlap tool)
  • Campaign naming conventions consistent and descriptive
  • Advantage+ Shopping Campaigns (ASC) active for e-commerce
  • Simplified campaign structure (fewer, larger ad sets preferred)

Audience & Targeting (20% weight)

  • Prospecting frequency (7-day): <3.0 (WARNING 3-5, FAIL >5)
  • Retargeting frequency (7-day): <8.0 (WARNING 8-12, FAIL >12)
  • Custom Audiences: website visitors, customer lists, engagement
  • Lookalike Audiences: multiple seed sizes tested (1%, 3%, 5%)
  • Advantage+ Audience tested vs manual targeting
  • Interest targeting: broad enough for algorithm optimization
  • Exclusions: purchasers excluded from prospecting, overlap managed
  • Location targeting reviewed for relevance

Advantage+ Assessment

If Advantage+ features are in use:

  • ASC (Shopping Campaigns): catalog connected, existing customer cap set
  • Advantage+ Audience: performance vs manual audience compared
  • Advantage+ Creative: enhancements enabled (text, brightness, music)
  • Advantage+ Placements: enabled (let Meta optimize placement mix)
  • Budget allocation: Advantage+ campaigns getting fair test budget

Special Ad Categories

If ads are in restricted categories:

  • Special Ad Category declared before campaign creation
  • Targeting restrictions verified (no ZIP, age 18-65+ only, no Lookalike)
  • Creative compliance with category-specific policies
  • Read ads/references/compliance.md for full requirements

EMQ Optimization Guide

EMQ ScoreStatusAction
8.0-10.0ExcellentMaintain current setup
6.0-7.9GoodAdd more customer_information parameters
4.0-5.9FairImplement CAPI, improve data quality
<4.0PoorCritical: CAPI + Enhanced Matching required

Key parameters to maximize EMQ:

  • em (email) — highest match rate signal
  • ph (phone) — second highest match signal
  • fn, ln (first/last name) — improves match accuracy
  • ct, st, zp (city, state, zip) — geographic matching
  • external_id — CRM/user ID for cross-device matching

Key Thresholds

MetricPassWarningFail
EMQ (Purchase)≥8.06.0-7.9<6.0
Dedup rate≥90%70-90%<70%
CTR≥1.0%0.5-1.0%<0.5%
Creative formats≥321
Creatives per ad set≥53-4<3
Learning Limited<30%30-50%>50%
Budget per ad set≥5x CPA2-5x CPA<2x CPA

Output

Meta Ads Health Score

Meta Ads Health Score: XX/100 (Grade: X)

Pixel / CAPI Health: XX/100  ████████░░  (30%)
Creative:            XX/100  ██████████  (30%)
Account Structure:   XX/100  ███████░░░  (20%)
Audience:            XX/100  █████░░░░░  (20%)

Deliverables

  • META-ADS-REPORT.md — Full 46-check findings with pass/warning/fail
  • EMQ improvement roadmap
  • Creative fatigue alerts (any creative with CTR declining >20%)
  • Quick Wins sorted by impact
  • Advantage+ adoption recommendations

What ships with it

Read from the repository

Just SKILL.md. No reference files, no scripts.

Gives 0 of the 12 instructions most audit compliance skills give in ~1.3k tokens

Counted across 937 of the 1,487 authors here whose files we hold, read 2026-08-07

  • Fetch latest guidelines before each reviewin 43 of 937, across 3 files
  • Group findings by severityin 43 of 937
  • Check files against all fetched rulesin 42 of 937, across 2 files
  • Output findings in terse file:line formatin 41 of 937, across 3 files
  • Ask user which files to review if none specifiedin 41 of 937, across 3 files
  • Read specified files or prompt user for filesin 39 of 937, across 1 file
  • Generate the audit reportin 33 of 937, across 30 files
  • Assign a severity to every findingin 25 of 937
  • Run automated accessibility scansin 23 of 937, across 13 files
  • Output a markdown audit reportin 22 of 937
  • Map findings to WCAG criteriain 20 of 937, across 10 files
  • Confirm audit scopein 19 of 937, across 9 files

Said here and by no other author read

  • read the benchmarks reference
  • read the scoring system reference
  • generate a findings report with an action plan
  • Collect Meta Ads data and screenshots
  • Read the full 46-check audit reference
  • Calculate a Meta Ads Health Score from zero to 100

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

Keep looking

Skills are one crate of 326,834. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.