ads

name	ads
description	Multi-platform paid advertising audit and optimization skill. Analyzes Google, Meta, YouTube, LinkedIn, TikTok, Microsoft, and Apple Search Ads. 225+ checks with scoring, parallel agents, industry templates, and AI creative generation.
argument-hint	audit | google | meta | youtube | linkedin | tiktok | microsoft | apple | creative | landing | budget | plan <type> | competitor | dna <url> | create | generate | photoshoot
license	MIT

Ads: Multi-Platform Paid Advertising Audit & Optimization

Comprehensive ad account analysis across all major platforms (Google, Meta, LinkedIn, TikTok, Microsoft). Orchestrates 17 specialized sub-skills and 10 agents (6 audit + 4 creative).

Quick Reference

Command	What it does
/ads audit	Full multi-platform audit with parallel subagent delegation
/ads google	Google Ads deep analysis (Search, PMax, YouTube)
/ads meta	Meta Ads deep analysis (FB, IG, Advantage+)
/ads youtube	YouTube Ads specific analysis
/ads linkedin	LinkedIn Ads deep analysis (B2B, Lead Gen)
/ads tiktok	TikTok Ads deep analysis (Creative, Shop, Smart+)
/ads microsoft	Microsoft/Bing Ads deep analysis (Copilot, Import)
/ads creative	Cross-platform creative quality audit
/ads landing	Landing page quality assessment for ad campaigns
/ads budget	Budget allocation and bidding strategy review
/ads plan <business-type>	Strategic ad plan with industry templates
/ads apple	Apple Search Ads (ASA) deep analysis
/ads competitor	Competitor ad intelligence analysis
/ads dna <url>	Extract brand DNA from website, outputs brand-profile.json
/ads create	Generate campaign concepts + copy briefs, outputs campaign-brief.md
/ads generate	Generate AI ad images from brief, outputs to ad-assets/
/ads photoshoot	Product photography in 5 styles (Studio, Floating, Ingredient, In Use, Lifestyle)

Context Intake (Required: Always Do This First)

Before any audit or analysis, collect this context. Without it, benchmarks will be generic and recommendations may be wrong for the user's situation.

Ask these questions upfront (combine into one message):

1. Industry / Business type: Which best describes you? SaaS · E-commerce · Local Service · B2B Enterprise · Info Products · Mobile App · Real Estate · Healthcare · Finance · Agency · Other
2. Monthly ad spend: Total budget and per-platform breakdown (approximate is fine)
3. Primary goal: Sales / Revenue · Leads / Demos · App Installs · Calls · Brand
4. Active platforms: Which platforms are you advertising on?

If the user provides data upfront (e.g. "audit my Google Ads, I spend $5k/mo on SaaS"), extract context from that and proceed without re-asking.

Use the provided context to:

• Select the correct industry benchmarks from references/benchmarks.md
• Apply budget-appropriate recommendations (e.g. Smart Bidding requires 15+ conv/month)
• Calibrate severity scoring (a $500/mo account has different priorities than $50k/mo)

Orchestration Logic

When the user invokes /ads audit, delegate to subagents in parallel:

1. Collect context (see Context Intake above; do this first)
2. Collect account data (exports, screenshots, or pasted metrics)
3. Detect business type and identify active platforms
4. Spawn subagents via Task tool with context: fork: audit-google, audit-meta, audit-creative, audit-tracking, audit-budget, audit-compliance
5. Validate: verify each subagent returned valid JSON scores with required fields before aggregating
6. Collect results and generate unified report with Ads Health Score (0-100)
7. Create prioritized action plan with Quick Wins

For individual commands (/ads google, /ads meta, etc.), load the relevant sub-skill directly. Still collect context first if not already provided.

Creative Workflow

Sequential pipeline (each step is independently runnable):

1. /ads dna <url> → brand-profile.json in current directory
2. /ads create → reads profile + optional audit results → campaign-brief.md
3. /ads generate → reads brief + profile → ad-assets/ directory
4. /ads photoshoot → standalone or reads profile for style injection

Requires GOOGLE_API_KEY (Gemini default) or ADS_IMAGE_PROVIDER + matching key. If API key is missing, /ads generate and /ads photoshoot display setup instructions and exit; they never fail silently.

Industry Detection

Detect business type from ad account signals:

• SaaS: trial_start/demo_request events, pricing page targeting, long attribution windows
• E-commerce: purchase events, product catalog/feed, Shopping/PMax campaigns
• Local Service: call extensions, location targeting, store visits, directions events
• B2B Enterprise: LinkedIn Ads active, ABM lists, high CPA tolerance ($50+), long sales cycle
• Info Products: webinar/course funnels, lead gen forms, low-ticket offers
• Mobile App: app install campaigns, in-app events, deep linking
• Real Estate: listing feeds, property-specific landing pages, geo-heavy targeting
• Healthcare: HIPAA compliance flags, healthcare-specific ad policies
• Finance: Special Ad Categories declared, financial products compliance
• Agency: multiple client accounts, white-label reporting needs

Quality Gates

Hard rules (never violate these):

• Never recommend Broad Match without Smart Bidding (Google)
• 3x Kill Rule: flag any ad group/campaign with CPA >3x target for pause
• Budget sufficiency: Meta ≥5x CPA per ad set, TikTok ≥50x CPA per ad group
• Learning phase: never recommend edits during active learning phase
• Compliance: always check Special Ad Categories for housing/employment/credit/finance
• Creative: never run silent video ads on TikTok (sound-on platform)
• Attribution: default to 7-day click / 1-day view (Meta), data-driven (Google)

Reference Files

Load these on-demand as needed; do NOT load all at startup.

Path resolution: All references are installed at ~/.claude/skills/ads/references/. When sub-skills or agents reference ads/references/*.md, resolve to ~/.claude/skills/ads/references/*.md.

• references/scoring-system.md: Weighted scoring algorithm and grading thresholds
• references/benchmarks.md: Industry benchmarks by platform (CPC, CTR, CVR, ROAS)
• references/bidding-strategies.md: Bidding decision trees per platform
• references/budget-allocation.md: Platform selection matrix, scaling rules, MER
• references/platform-specs.md: Creative specifications across all platforms
• references/conversion-tracking.md: Pixel, CAPI, EMQ, ttclid implementation
• references/compliance.md: Regulatory requirements, ad policies, privacy
• references/google-audit.md: 74-check Google Ads audit checklist
• references/meta-audit.md: 46-check Meta Ads audit checklist
• references/linkedin-audit.md: 25-check LinkedIn Ads audit checklist
• references/tiktok-audit.md: 25-check TikTok Ads audit checklist
• references/microsoft-audit.md: 20-check Microsoft Ads audit checklist
• references/brand-dna-template.md: Brand DNA schema and extraction guide
• references/image-providers.md: Provider config (Gemini/OpenAI/Stability/Replicate)
• references/google-creative-specs.md: PMax/RSA/YouTube generation-ready specs
• references/meta-creative-specs.md: Feed/Reels/Stories specs + safe zones
• references/linkedin-creative-specs.md: Single image/video B2B constraints
• references/tiktok-creative-specs.md: 9:16 only + safe zone overlay
• references/youtube-creative-specs.md: Skippable/Bumper/Shorts/Thumbnail
• references/microsoft-creative-specs.md: Multimedia Ads + RSA subset
• references/gaql-notes.md: GAQL field compatibility, deduplication patterns, filter scope best practices
• references/voice-to-style.md: Brand voice axis to visual attribute mapping for image generation
• references/copy-frameworks.md: 6 ad copy frameworks (AIDA, PAS, BAB, 4P, FAB, Star-Story-Solution)

Scoring Methodology

Ads Health Score (0-100)

Per-platform score using weighted algorithm from references/scoring-system.md. Cross-platform aggregate weighted by budget share:

Aggregate = Sum(Platform_Score x Platform_Budget_Share)

Grading

Grade	Score	Action Required
A	90-100	Minor optimizations only
B	75-89	Some improvement opportunities
C	60-74	Notable issues need attention
D	40-59	Significant problems present
F	<40	Urgent intervention required

Priority Levels

• Critical: Revenue/data loss risk (fix immediately)
• High: Significant performance drag (fix within 7 days)
• Medium: Optimization opportunity (fix within 30 days)
• Low: Best practice, minor impact (backlog)

Sub-Skills

This skill orchestrates 17 specialized sub-skills:

1. ads-audit: Full multi-platform audit with parallel delegation
2. ads-google: Google Ads deep analysis (Search, PMax, YouTube)
3. ads-meta: Meta Ads deep analysis (FB, IG, Advantage+)
4. ads-youtube: YouTube Ads specific analysis
5. ads-linkedin: LinkedIn Ads deep analysis
6. ads-tiktok: TikTok Ads deep analysis
7. ads-microsoft: Microsoft/Bing Ads deep analysis
8. ads-creative: Cross-platform creative quality audit
9. ads-landing: Landing page quality for ad campaigns
10. ads-budget: Budget allocation and bidding strategy
11. ads-plan: Strategic ad planning with industry templates
12. ads-competitor: Competitor ad intelligence
13. ads-apple: Apple Search Ads (ASA) deep analysis
14. ads-dna: Brand DNA extraction from website URL
15. ads-create: Campaign concepts, copy decks, creative briefs
16. ads-generate: AI image generation with pluggable providers
17. ads-photoshoot: Product photography in 5 professional styles

Subagents

For parallel analysis during full audits:

• audit-google: Google Ads checks (G01-G74)
• audit-meta: Meta Ads checks (M01-M46)
• audit-creative: Creative quality for LinkedIn, TikTok, Microsoft
• audit-tracking: Conversion tracking health across all platforms
• audit-budget: Budget, bidding, structure for LinkedIn, TikTok, Microsoft
• audit-compliance: Compliance, settings, performance across all platforms
• creative-strategist: Campaign concepts from brand profile + audit results (Opus, maxTurns: 25)
• visual-designer: Image generation with brand injection via generate_image.py (Sonnet, maxTurns: 30)
• copy-writer: Headlines, CTAs, primary text within platform limits (Sonnet, maxTurns: 20)
• format-adapter: Asset dimension validation and spec compliance reporting (Haiku, maxTurns: 15)