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Comprehensive paid advertising audit & optimization skill for Claude Code. 250+ checks across Google, Meta, YouTube, Lin
A manual audit of a single Google Ads account takes 4-6 hours of senior PPC time. Claude Ads runs the same audit in 10-15 minutes, scores it on a 0-100 weighted scale, and outputs a prioritized action plan across Google, Meta, YouTube, LinkedIn, TikTok, Microsoft, Apple, and Amazon Ads. Built for PPC agencies, in-house marketers, and freelance ad consultants. Local, deterministic, MIT-licensed.
Host support:
Last updated: 2026-05-18 · Version: v1.7.1 · CHANGELOG · Blog: full ad audit breakdown
Two versions of this skill.
- 🌐 Public open-source →
AgriciDaniel/claude-ads: MIT, public releases, no membership. Best if you want stable + downloadable.- 🔒 Community private mirror →
AI-Marketing-Hub/claude-ads: early access to upcoming features and direct collaboration with the AI Marketing Hub Pro community. Requires membership.
/ads amazon (Sponsored Products/Brands/Display, ACOS/TACOS), /ads attribution (AdAttributionKit + GA4 + Consent Mode V2), /ads tracking (sGTM + CAPI Gateway + dedup + hashing).tests/: routing snapshots, bidirectional 209-check catalog coverage, scoring math determinism, SSRF regression. Runs in CI on every commit.install.sh / install.ps1 --target=<host> with whitelist validation for Claude Code, Codex CLI, Cursor, Windsurf, Gemini CLI, Goose./ads google for the AI Max era (ai_max_setting.enable_ai_max, AI Brief, FUE, brand exclusions). /ads meta for the Andromeda + GEM + Lattice era with Entity-ID clustering detection.ads/references/thinking-framework.md.Full release notes: CHANGELOG.md.
What /ads audit actually returns (truncated for brevity):
{
"ads_health_score": 73,
"grade": "C",
"audit_date": "2026-05-18",
"platforms": {
"google_ads": { "score": 78, "grade": "B", "checks_run": 80, "critical": 2, "high": 5 },
"meta_ads": { "score": 64, "grade": "C", "checks_run": 50, "critical": 4, "high": 7 },
"linkedin": { "score": 81, "grade": "B", "checks_run": 27, "critical": 0, "high": 3 }
},
"top_findings": [
{
"id": "M-AND-01",
"severity": "critical",
"platform": "meta",
"title": "Andromeda creative similarity > 60%: retrieval suppression risk",
"impact": "Estimated 20-35% reach loss; 4 ad sets affected",
"action": "Replace 7 near-duplicate creatives with concept-diverse variants",
"owner": "creative",
"eta_days": 7
},
{
"id": "G-AIM-03",
"severity": "high",
"platform": "google",
"title": "AI Max enabled without negative keyword discipline",
"impact": "Wasted spend ~$1,400/mo on irrelevant queries",
"action": "Build negative list from 30d search term report",
"owner": "search",
"eta_days": 2
}
],
"quick_wins": [
"Enable Consent Mode V2 (Privacy Sandbox compatible, ~1 hr)",
"Pause 3 ad groups failing 3x Kill Rule (saves ~$420/mo)"
]
}
Plus a PDF version (/ads report) with health score gauge, platform comparison charts, and zero-overlap layout for client delivery.
ℹ️ Two install paths — pick the one that matches your access.
The commands below default to the community private mirror at
AI-Marketing-Hub/claude-ads(early access for Pro members). To use the public release atAgriciDaniel/claude-ads(MIT, no membership), swapAI-Marketing-Hub/claude-adsforAgriciDaniel/claude-adsand the plugin slugclaude-ads@ai-marketing-hub-claude-adsforclaude-ads@agricidaniel-claude-adsin every command.Pro members: requires
gh auth login(or PAT) with access to theAI-Marketing-Huborg. If/plugin marketplace add404s, DM in the Skool community to get added.
/plugin marketplace add AI-Marketing-Hub/claude-ads
/plugin install claude-ads@ai-marketing-hub-claude-ads
Registers claude-ads as a native plugin with auto-updates, namespace isolation, and version tracking.
curl -fsSL https://raw.githubusercontent.com/AI-Marketing-Hub/claude-ads/main/install.sh | bash
irm https://raw.githubusercontent.com/AI-Marketing-Hub/claude-ads/main/install.ps1 | iex
# Unix/macOS/Linux
bash install.sh --target=codex # OpenAI Codex CLI (experimental)
bash install.sh --target=cursor # Cursor IDE (experimental)
bash install.sh --target=windsurf # Windsurf IDE (experimental)
bash install.sh --target=gemini # Gemini CLI (experimental)
bash install.sh --target=goose # Goose CLI (experimental)
# Windows PowerShell
.\install.ps1 -Target codex
.\install.ps1 -Target cursor
.\install.ps1 -Target windsurf
.\install.ps1 -Target gemini
.\install.ps1 -Target goose
Per-host install path table:
| Target | Skills root | Agents root | Python deps |
|---|---|---|---|
claude | ~/.claude/skills | ~/.claude/agents | ✓ |
codex | ~/.codex/skills | ~/.codex/agents | ✓ |
cursor | ~/.cursor/extensions/claude-ads/skills | ~/.cursor/extensions/claude-ads/agents | skipped |
windsurf | ~/.windsurf/skills | ~/.windsurf/agents | skipped |
gemini | ~/.gemini/extensions/claude-ads/skills | ~/.gemini/extensions/claude-ads/agents | skipped |
goose | ~/.config/goose/skills | ~/.config/goose/agents | skipped |
Path overrides:
bash install.sh --target=claude --skill-dir=~/custom/skills --agent-dir=~/custom/agents
Targets and override paths are strictly whitelist-validated: no shell injection, no flag confusion, no .. segments, no UNC paths.
⚠ Experimental targets: Only Claude Code is verified end-to-end. Other host install paths follow each host's documented convention; skill discovery and sub-skill routing may differ. Open an issue with reproduction details if a target needs adjustment.
git clone https://github.com/AI-Marketing-Hub/claude-ads.git
cd claude-ads
./install.sh # Unix/macOS/Linux, default target=claude
./install.sh --target=codex # any cross-host target
.\install.ps1 # Windows PowerShell, default Target=claude
.\install.ps1 -Target codex
# Start Claude Code
claude
# Run a full multi-platform audit
/ads audit
# Deep analysis for a single platform
/ads google
/ads meta
/ads linkedin
# Strategic planning by business type
/ads plan saas
/ads plan ecommerce
/ads plan local-service
# Cross-platform creative audit
/ads creative
# Budget and bidding strategy review
/ads budget
| Command | Description |
|---|---|
/ads audit | Full multi-platform audit with parallel subagent delegation |
/ads google | Google Ads deep analysis (Search, PMax, AI Max, Display, YouTube, Demand Gen) |
/ads meta | Meta Ads deep analysis (FB, IG, Threads, Advantage+, Andromeda + GEM + Lattice) |
/ads youtube | YouTube Ads specific analysis (Skippable, Shorts, Demand Gen, CTV) |
/ads linkedin | LinkedIn Ads deep analysis (B2B, Lead Gen, TLA, ABM) |
/ads tiktok | TikTok Ads deep analysis (Creative, Shop, Smart+, post-USDS) |
/ads microsoft | Microsoft/Bing Ads deep analysis (Copilot, Import validation) |
/ads apple | Apple Ads deep analysis (CPPs, Maximize Conversions, AdAttributionKit, TAP) |
/ads amazon | Amazon Ads deep analysis (Sponsored Products / Brands / Display, ACOS / TACOS) · Wave 2 |
/ads attribution | Cross-platform attribution audit (AdAttributionKit, GA4, Consent Mode V2, MMP) · Wave 2 |
/ads tracking | Server-side tracking pipeline audit (sGTM, CAPI Gateway, dedup, hashing) · Wave 2 |
/ads creative | Cross-platform creative quality audit and fatigue detection |
/ads landing | Landing page quality assessment for ad campaigns |
/ads budget | Budget allocation and bidding strategy review |
/ads plan <type> | Strategic ad plan with industry templates |
/ads competitor | Competitor ad intelligence across all platforms |
/ads math | PPC financial calculator (CPA, ROAS, break-even, budget forecasting, LTV:CAC) |
/ads test | A/B test design (hypothesis framework, significance, sample size, duration) |
/ads report | Generate PDF audit report for client deliverables |
/ads dna <url> | Extract brand DNA from website → brand-profile.json |
/ads create | Generate campaign concepts + copy briefs → campaign-brief.md |
/ads generate | Generate AI ad images from brief → ad-assets/ |
/ads photoshoot | Product photography in 5 styles (Studio, Floating, Ingredient, In Use, Lifestyle) |
/ads auditFull Multi-Platform Audit
Spawns 6 parallel subagents:
Generates a unified Ads Health Score (0-100) with prioritized action plan.
Wave 2 standalone sub-skills.
/ads auditparallel-delegates the 6 agents above. Amazon, attribution, and server-side tracking are standalone sub-skills (/ads amazon,/ads attribution,/ads tracking); invoke them directly. Wave 3 will add their paired audit agents.
/ads plan <business-type>Strategic Ad Planning
Industry templates with platform mix, campaign architecture, creative strategy, targeting, budget guidelines, and KPI targets.
Supported business types:
saas: Trial/demo focus, Google + LinkedIn primaryecommerce: Shopping/PMax, ROAS-focused, seasonallocal-service: Google Search + LSA, call tracking, geo radiusb2b-enterprise: LinkedIn ABM, long sales cycle, pipeline metricsinfo-products: Meta + YouTube, webinar/VSL funnelsmobile-app: Meta + Google UAC, MMP required, LTV:CPIreal-estate: Special Ad Category (housing), buyer/seller campaignshealthcare: HIPAA compliance, LegitScript, restricted targetingfinance: Special Ad Category (credit), required disclosuresagency: Multi-client management, reporting frameworkgeneric: Universal template with platform selection questionnaire
/ads math and /ads test
/ads reportPDF audit reports for client deliverables: health score gauge, platform comparison charts, pass/fail distribution, formatted tables, zero-overlap layout.
What every check actually does: catches the platform-specific blind spots that cost you spend. Andromeda creative-similarity suppression on Meta. Negative-keyword discipline gaps on Google AI Max. Andromeda-aware creative diversity scoring. AdAttributionKit configurable-window gaps on Apple. ACOS/TACOS targets misaligned with margin on Amazon. Consent Mode V2 missing on the landing page (silent revenue leak). These are the items a manual audit misses because the analyst is mostly checking what used to matter, not what platforms changed in 2026.
| Platform | Checks | Key areas |
|---|---|---|
| Google Ads | 80 | Search, PMax, AI Max (ai_max_setting, AI Brief, FUE), Demand Gen, CTV, YouTube |
| Meta Ads | 50 | Pixel/CAPI, Andromeda + GEM + Lattice, Entity-ID clustering, ASC/AAC, Structure, Audience |
| LinkedIn Ads | 27 | B2B targeting, TLA, Lead Gen, CRM integration |
| TikTok Ads | 28 | Creative-first, Smart+, GMV Max, Search Ads, Events API (post-USDS) |
| Microsoft Ads | 24 | Google import safety, Copilot, CTV, LinkedIn targeting, video |
| Apple Ads | 35+ | Campaign structure, CPPs, Maximize Conversions, AdAttributionKit |
| Amazon Ads | 30+* | Sponsored Products / Brands / Display, ACOS / TACOS, search-term harvesting |
| Cross-platform | 3 | Privacy infrastructure, creative diversity, refresh cadence |
| Attribution + server-side | 25+* | AdAttributionKit, GA4, Consent Mode V2, sGTM, CAPI Gateway, hash quality |
* Verified vs estimated. The 209 checks for Google (80), Meta (50), LinkedIn (27), TikTok (28), and Microsoft (24) are bidirectionally verified against
tests/fixtures/check-catalog.yaml; they can't drift without failing CI. Apple, Amazon, Cross-platform, and Attribution + Server-side counts are inline thresholds in their respective SKILL.md files; corresponding audit reference files + catalog entries land in Wave 3.
Weighted scoring algorithm with severity multipliers:
| 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 |
Auto-detects business type from ad account signals (product feeds, conversion events, platform mix, targeting patterns) and loads industry-specific benchmarks and templates.
Hard rules enforced during every audit:
AI-powered creative generation with 4 specialized agents (/ads dna → /ads create → /ads generate → /ads photoshoot).
26 built-in reference files with 2026-current benchmarks, bidding decision trees, platform specifications, compliance requirements, conversion tracking guides, MCP integration guide, and platform coverage notes.
Every audit, plan, and creative output runs under a shared cognitive discipline: OBSERVE × 2 / LISTEN / THINK / CONNECT × 2 / FEEL / ACCEPT / CREATE / GROW. Maps each principle to concrete ad-work behavior, the anti-pattern that signals you're skipping it, and the workflow stage where it dominates. It's the difference between a list of red flags and a strategic deliverable. Defined in ads/references/thinking-framework.md.
Runs entirely on your local machine via Claude Code. No ad account data is sent to external servers. When using MCP servers for live API access, data flows directly between your machine and the platform APIs. All analysis happens locally.
| Manual audit | Agency engagement | Commercial PPC audit tool | Claude Ads | |
|---|---|---|---|---|
| Time per audit | 4-6 hrs senior PPC time | 1-2 weeks turnaround | 5-30 min | 10-15 min |
| Cost | High (billable hours) | $2k-$10k+ project | $99-$799/mo subscription | Free skill + Claude Code subscription |
| Repeatable | Inconsistent across analysts | Inconsistent across accounts | Yes | Yes, deterministic + scriptable |
| Output format | Wall-of-findings PDF | Branded slide deck | Web dashboard, exports | JSON + PDF, local files |
| Custom benchmarks | Manual | Manual | Vendor-fixed | Edit local SKILL.md |
| Data leaves machine? | No (your spreadsheet) | Yes (sent to agency) | Yes (uploaded to vendor) | No, fully local |
| Lock-in | None | High | High (data exit cost) | None (MIT, your files) |
| Andromeda / AI Max / AdAttributionKit awareness | Depends on analyst | Depends on agency seniority | Lagging (typically 6-12 mo behind) | Andromeda (Oct 2025), AI Max (May 2025), AdAttributionKit + WWDC25 configurable windows, Consent Mode V2 |
Cost benchmarks: manual audit assumes a senior PPC consultant at typical agency billable rates; agency engagement based on common discovery/audit deliverable scopes; commercial-tool subscriptions reflect published mid-tier pricing across the PPC audit category. Your numbers may differ.
Agency lead running 12 client accounts. Replaces the quarterly "deep audit" ritual with a weekly Monday morning /ads audit run per account. Time to deliver a client health-score email drops from 4 hours to 12 minutes; coverage goes from quarterly to weekly without billing more hours.
In-house marketer at a 50-person SaaS company. Runs /ads audit 24 hours before quarterly business reviews. Catches the items the platform UI buries (broken conversion goals, ASC budget starvation, missing Andromeda creative diversity) before the CMO asks "why is CAC up?" in front of the board.
Freelance PPC consultant onboarding a new client. Runs /ads audit on the discovery call. Anchors the engagement scope with a real 0-100 score and 3 prioritized critical findings instead of a vague "I'll take a look and get back to you." Closes more retainers because the proof of value happens before the proposal.
41 tests, 41 passing, CI on every commit. Pytest suite in tests/:
tests/fixtures/check-catalog.yaml (209 IDs) and every audit reference file; no orphan IDs, no untracked rowsRare among Claude Code skills. Makes the project auditable end-to-end and prevents claim-vs-reality drift between releases.
~/.claude/skills/ads/ # Main orchestrator
~/.claude/skills/ads/references/ # 26 RAG reference files
~/.claude/skills/ads-*/ # 22 sub-skills (incl. ads-math, ads-test, ads-amazon, ads-attribution, ads-server-side-tracking)
~/.claude/skills/ads-plan/assets/ # 12 industry templates
~/.claude/agents/ # 10 agents (6 audit + 4 creative)
~/.claude/skills/ads/tests/ # 41-test pytest eval harness (Wave 2)
/ads) routes commands to specialized sub-skillsClaude Ads works with data you provide: exports, screenshots, or pasted metrics from your ad platform dashboards. It does not connect to any ad platform API automatically.
To get accurate, account-specific recommendations:
/ads google, /ads audit, etc.
For direct API access without manual exports, pair Claude Ads with MCP servers. See ads/references/mcp-integration.md for setup:
No. Claude Ads analyzes data you provide (exports, screenshots, or pasted metrics). It doesn't connect to ad platforms automatically. See the Live data integration section for Google Ads API access via MCP.
Both. Your account data drives the audit; industry benchmarks (from research covering thousands of campaigns) provide the comparison floor and ceiling. Benchmarks are averages; results vary by industry, budget level, and account maturity. Always provide your industry and monthly spend up front for the most relevant comparisons.
Yes. Claude Ads is an audit and strategy tool. It finds issues, recommends fixes, and builds campaign plans, but creating, editing, or posting ads remains manual in your ad platform.
Benchmarks and best practices differ significantly between a $500/month account and a $50k/month account. Tell Claude your budget upfront: "I spend $2k/month on Google Ads for a local plumbing business" gives much better results than running /ads google cold.
Currently supported: Google, Meta (Facebook/Instagram), YouTube, LinkedIn, TikTok, Microsoft/Bing, Apple, and Amazon. Additional platforms (Reddit, CTV/OTT, Pinterest, Snapchat) are covered in ads/references/additional-platforms.md for strategic planning.
Use /ads math for the financial calculator (CPA, ROAS, CPL, break-even analysis, impression-share opportunity sizing, budget forecasting, LTV:CAC ratio, MER). The full audit (/ads audit) automatically benchmarks your reported ROAS / CPA / CPL against industry-specific targets loaded from ads/references/benchmarks.md. For Amazon, /ads amazon scores ACOS and TACOS against category benchmarks and flags products where TACOS exceeds your contribution margin.
Single maintainer (see Maintainer). Bug reports and issues filed via GitHub get a response within 48 hours on the public repo; faster for Pro community members via the Skool community. No SLA on feature requests; those go through the public roadmap. CI runs the full eval harness on every commit, so regressions are caught before they ship.
No. The skill runs locally in Claude Code. When you opt into a live MCP integration (e.g. mcp-google-ads), data flows directly from your machine to the platform API, never through claude-ads infrastructure (there is none).
Three things: (1) local-first, no data uploaded anywhere; (2) MIT-licensed and forkable: you can edit the audit checks; (3) it tracks 2026-current platform changes (Andromeda, AI Max, AdAttributionKit) that commercial tools often lag 6-12 months behind on.
Yes. MIT license. White-label the PDF reports via the /ads report template. The /ads plan agency template is built for multi-client management. The community private mirror at AI-Marketing-Hub ships ahead of public releases for Pro members.
/ads report)curl -fsSL https://raw.githubusercontent.com/AI-Marketing-Hub/claude-ads/main/uninstall.sh | bash
irm https://raw.githubusercontent.com/AI-Marketing-Hub/claude-ads/main/uninstall.ps1 | iex
The 12-month delivery cadence from v1.5 stable through Wave 2 (v1.7.x, current) to Wave 3 (v1.8.0+, in active development on the private repo). Full per-release detail in CHANGELOG.md.
Looking for the other version of this skill? See the Two versions of this skill callout at the top of this README.
Built by Agrici Daniel: AI Workflow Architect. Single maintainer, open to community contributions via the Pro Skool community.
If Claude Ads saves you time, a star on the public repo is the easiest way to say thanks (and helps other PPC folks find it).
MIT. See LICENSE for details.
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Self-learning system that captures corrections and syncs them to CLAUDE.md and AGENTS.md
