ads-apple
🤖 AI Summary
This agent ingests Apple Ads account data, then evaluates campaign structure, placement types, and performance metrics to calculate a 0-100 ASA Health Score and generate a findings report with prioritized action items.
How to Install
Claude Code:
git clone --depth 1 https://github.com/AgriciDaniel/claude-ads.git && cp claude-ads/skills/ads-apple ~/.claude/skills/ads-apple -r# Apple Ads (formerly Apple Search Ads) Deep Analysis
## Process
1. Collect Apple Ads account data (exports from Apple Ads dashboard or pasted metrics)
2. Identify active placement types (Search Results, Search Tab, Today Tab, Product Pages)
3. Evaluate all applicable checks as PASS, WARNING, or FAIL
4. Calculate ASA Health Score (0-100)
5. Generate findings report with action plan
## What to Analyze
### Campaign Structure (25% weight)
**BOFU; Bottom of Funnel (Search Results, Exact Match brand)**
- Brand keyword campaign present (own app name + misspellings)
- Competitor campaign present (competitor app names as keywords)
- Category campaigns targeting high-intent generic terms (e.g. "workout app", "budget tracker")
**MOFU; Middle of Funnel (Search Match / broad discovery)**
- Search Match campaigns active in at least one ad group for discovery
- Search Match ad groups isolated from Exact Match (separate ad groups; never mix)
- Search Terms Report reviewed to mine converting queries for Exact Match promotion
**Campaign Architecture Rules:**
- Brand / Category / Competitor should be separate campaigns (different CPT bids, budgets)
- Search Match ad groups isolated from manual keyword ad groups; NEVER mix in same ad group
- Goal: let Search Match discover, then promote winners to Exact Match campaigns
### Bid Health (20% weight)
**CPT (Cost Per Tap) vs Install Rate by Match Type:**
- CPT vs category benchmarks (see Benchmarks section below)
- TTR (Tap-Through Rate): benchmark >2.5% for Search Results, >1.5% for Search Tab
- Conversion Rate (tap → install): benchmark 50-65% for brand terms, 20-40% for category
- CPT/CPG (Cost Per Goal): compare against target CPI/CPA from MMP
**Bid Strategy:**
- Manual CPT bidding appropriate for small/new accounts
- **Maximize Conversions** (GA February 26, 2026): AI-powered auto-bidder using Search Match that sets optimal bids per search query in real time. Target CPA (weekly average target) replaces CPA Cap (being deprecated). Recommended daily budget: at least 5x target CPA. Two-week learning period minimum. **Current limitation**: only optimizes for installs, NOT post-install events (no trial, subscription, or ROAS optimization yet)
- CPA Goals available at campaign level; evaluate if conversion volume supports it (>100 installs/month per campaign)
- Are bids differentiated by match type? (Brand Exact > Category Exact > Search Match)
- Keyword-level CPT bids set, not just ad group default?
**Keyword Health:**
- Irrelevant Search Terms (from Search Match) identified and excluded via negative keywords
- Low-performing keywords paused or bid reduced (TTR <1% + high CPT)
- High-volume generic terms checked for intent quality (avoid "free apps" type queries)
### Custom Product Pages (15% weight)
> **Crea
Details
| Category | AI/ML → ml |
| Source | AgriciDaniel/claude-ads |
| SKILL.md | View on GitHub → |
| Repo Stars | ★ 6.4K |
| Est. per Skill | N/A (shared across 22 skills from this repo) |
| Difficulty | Intermediate |
| Risk Level | N/A |
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