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Trigger for prompts like \"analyze my ad campaigns\", \"where am I wasting ad spend\", \"reallocate my ad budget\", \"which ads are actually working\", or \"ROAS analysis\". Do not trigger for campaign planning or creative generation without performance data.","github\u002Fawesome-copilot","github","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F9919?v=4","01M6AC83DS133TPXFSP1DHNCC6","GitHub","01M6AD3PBDYPK7NVFJNJ66Y8RS",41028,"MIT","https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fad-campaign-analyzer\u002FSKILL.md",[18,23,26,31],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000019","营销推广","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",{"categoryNo":27,"categoryName":28,"taxonomyType":29,"taxonomyTypeDesc":30},"01JBSEEDCAT000000000000035","市场营销","PROFESSION","适用职业",{"categoryNo":32,"categoryName":33,"taxonomyType":29,"taxonomyTypeDesc":30},"01JBSEEDCAT000000000000040","财务",[35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,9],"analyzer","ad","ad campaign analyzer","campaign","Budget","when","api keys","which","paste","external tools","cut","analyze","ads","api","analysis","google","Meta","data","network","csv","https","screenshot","reasoning","planning","test","performance","2026-11-10 10:12:30",false,"---\nname: ad-campaign-analyzer\ndescription: 'Use this skill when the user shares ad campaign performance data and asks what to cut, scale, or test. Trigger for prompts like \"analyze my ad campaigns\", \"where am I wasting ad spend\", \"reallocate my ad budget\", \"which ads are actually working\", or \"ROAS analysis\". Do not trigger for campaign planning or creative generation without performance data.'\nlicense: MIT\ncompatibility: 'Cross-platform. Pure reasoning skill over user-provided campaign exports (CSV, paste, or screenshot from Google, Meta, or LinkedIn) — no external tools, network calls, or API keys.'\nmetadata:\n  version: \"1.0\"\n  author: GooseWorks\n  source: https:\u002F\u002Fgithub.com\u002Fgooseworks-ai\u002Fgoose-skills\n---\n\n# Ad Campaign Analyzer\n\nTake raw campaign performance data and turn it into clear decisions. This skill doesn't just summarize metrics — it diagnoses problems, identifies winners, checks statistical significance, and tells you exactly what to cut, scale, and test next. Then it goes further: it compares channels on equal terms, finds where you're over-spending vs under-spending relative to results, and produces a concrete budget reallocation plan.\n\n**Core principle:** Most startup founders check their ad dashboard, see a ROAS number, and either panic or celebrate. This skill gives you the nuanced analysis a paid media specialist would: what's actually significant, what's noise, and where your next dollar should go. It also solves the allocation problem — most startups either spread budget too thin across channels (no channel gets enough to learn) or dump everything into one channel (missing cheaper opportunities elsewhere).\n\n## When to Use\n\n- \"Analyze my Google Ads performance\"\n- \"Which ads should I kill?\"\n- \"Is this campaign working?\"\n- \"Where am I wasting ad spend?\"\n- \"Optimize my Meta Ads\"\n- \"How should I split my ad budget?\"\n- \"Should I spend more on Google or Meta?\"\n- \"Reallocate my ad spend across channels\"\n- \"Where am I getting the best return?\"\n- \"I have $X\u002Fmonth for ads — how should I distribute it?\"\n\n## Phase 0: Intake\n\n1. **Campaign data** — One of:\n   - CSV export from Google Ads \u002F Meta Ads Manager \u002F LinkedIn Campaign Manager\n   - Pasted performance table\n   - Screenshots of dashboard (we'll extract the data)\n2. **Platform(s)** — Google \u002F Meta \u002F LinkedIn \u002F All\n3. **Time period** — What date range does this cover?\n4. **Monthly budget** — Total ad spend in this period\n5. **Primary goal** — What conversion are you optimizing for? (Demos \u002F Trials \u002F Purchases \u002F Leads)\n6. **Target metrics** — Do you have target CPA or ROAS? (If not, we'll benchmark)\n7. **Any known changes?** — Did you change creative, budget, or targeting during this period?\n8. **Channels currently running** — Google Ads, Meta Ads, LinkedIn Ads, Twitter\u002FX Ads, TikTok Ads, other\n9. **Funnel data** (if available):\n   - Lead → MQL rate\n   - MQL → SQL rate\n   - SQL → Close rate\n   - Average deal size\n10. **Channels you're considering but haven't tried** — Want to test new channels?\n11. **Constraints** — Minimum spend on any channel? Platform you must stay on?\n\n## Phase 1: Data Ingestion & Normalization\n\n### Accepted Data Formats\n\n| Source | Key Columns Expected |\n|--------|---------------------|\n| **Google Ads** | Campaign, Ad Group, Keyword, Impressions, Clicks, CTR, CPC, Conversions, Conv Rate, Cost, Conv Value |\n| **Meta Ads** | Campaign, Ad Set, Ad, Impressions, Reach, Clicks, CTR, CPC, Conversions, Cost Per Result, Amount Spent, ROAS |\n| **LinkedIn Ads** | Campaign, Impressions, Clicks, CTR, CPC, Conversions, Cost, Leads |\n\nNormalize all data into a standard analysis format:\n\n| Dimension | Impressions | Clicks | CTR | CPC | Conversions | Conv Rate | CPA | Spend | Revenue\u002FValue |\n|-----------|------------|--------|-----|-----|-------------|----------|-----|-------|--------------|\n\n### Multi-Channel Normalization\n\nWhen data spans multiple channels, also produce a channel-level rollup:\n\n| Channel | Monthly Spend | Impressions | Clicks | CTR | CPC | Conversions | Conv Rate | CPA | ROAS | CAC* |\n|---------|-------------|------------|--------|-----|-----|-------------|----------|-----|------|------|\n| Google Search | $[X] | [N] | [N] | [X%] | $[X] | [N] | [X%] | $[X] | [X] | $[X] |\n| Google Display | ... | | | | | | | | | |\n| Meta (FB\u002FIG) | ... | | | | | | | | | |\n| LinkedIn | ... | | | | | | | | | |\n| [Other] | ... | | | | | | | | | |\n| **Total** | $[X] | | | | | [N] | | $[X] avg | [X] avg | $[X] avg |\n\n*CAC = Full customer acquisition cost if funnel data provided (CPA × close-rate adjustment)\n\n### Funnel-Adjusted CAC (If Funnel Data Available)\n\n```\nChannel CAC = CPA ÷ (MQL rate × SQL rate × Close rate)\n```\n\nThis reveals which channels produce leads that actually close, not just convert.\n\n## Phase 2: Performance Diagnostics\n\n### 2A: Campaign-Level Health Check\n\nFor each campaign:\n\n| Metric | Value | Benchmark | Status |\n|--------|-------|-----------|--------|\n| CTR | [X%] | [Industry avg] | [Good\u002FOkay\u002FPoor] |\n| CPC | $[X] | [Category avg] | [Good\u002FOkay\u002FPoor] |\n| Conv Rate | [X%] | [Benchmark] | [Good\u002FOkay\u002FPoor] |\n| CPA | $[X] | [Target or benchmark] | [Good\u002FOkay\u002FPoor] |\n| ROAS | [X] | [Target or benchmark] | [Good\u002FOkay\u002FPoor] |\n| Impression Share | [X%] | [>60% ideal] | [Good\u002FOkay\u002FPoor] |\n\n### 2B: Budget Waste Detection\n\nIdentify spend that produced no or negative return:\n\n| Waste Type | Signal | Action |\n|-----------|--------|--------|\n| **Zero-conversion keywords\u002Fads** | Spend > $[X] with 0 conversions | Pause or add negatives |\n| **High CPA outliers** | CPA > 3x target | Pause or restructure |\n| **Low CTR ads** | CTR \u003C 50% of campaign average | Replace creative |\n| **Broad match bleed** | Search terms report showing irrelevant clicks | Add negative keywords |\n| **Audience overlap** | Same users hit by multiple campaigns | Exclude audiences |\n| **Dayparting waste** | Conversions cluster at certain hours; spend is 24\u002F7 | Set ad schedule |\n\n### 2C: Winner Identification\n\nFind what's actually working:\n\n| Winner Type | Signal | Action |\n|------------|--------|--------|\n| **Top-performing keywords** | Lowest CPA, highest conv rate | Increase bid, add variants |\n| **Winning ads** | Highest CTR + conv rate combo | Scale spend, clone for other groups |\n| **Best audiences** | Lowest CPA segment | Increase budget allocation |\n| **Best times** | Peak conversion hours\u002Fdays | Concentrate budget |\n\n### 2D: Statistical Significance Check\n\nFor any A\u002FB test (ad variants, audiences, landing pages):\n\n```\nTest: [Variant A] vs [Variant B]\nMetric: [Conv Rate \u002F CTR \u002F CPA]\nVariant A: [X%] (n=[sample_size])\nVariant B: [Y%] (n=[sample_size])\nConfidence level: [X%]\nVerdict: [Statistically significant \u002F Not enough data \u002F Too close to call]\nRecommended action: [Pick winner \u002F Continue test \u002F Increase budget to reach significance]\n```\n\nMinimum sample: 100 clicks per variant for CTR tests, 30 conversions per variant for CPA tests.\n\n## Phase 3: Funnel Analysis\n\n### Click → Conversion Path\n\n```\nImpressions: [N] (100%)\n     ↓ CTR: [X%]\nClicks: [N] ([X%] of impressions)\n     ↓ Landing page → Conversion: [X%]\nConversions: [N] ([X%] of clicks)\n     ↓ Conversion → Revenue: $[X] avg\nRevenue: $[N]\n```\n\n### Funnel Drop-Off Diagnosis\n\n| Drop-Off Point | Rate | Benchmark | Likely Cause | Fix |\n|----------------|------|-----------|-------------|-----|\n| Impression → Click | [CTR%] | [Benchmark] | [Ad relevance \u002F targeting] | [Copy\u002Ftargeting change] |\n| Click → Conversion | [Conv%] | [Benchmark] | [Landing page \u002F offer \u002F audience mismatch] | [LP optimization] |\n| Conversion → Revenue | [Close%] | [Benchmark] | [Lead quality \u002F sales process] | [Qualification criteria] |\n\n## Phase 4: Budget Reallocation\n\nWhen data spans multiple channels, perform cross-channel budget optimization.\n\n### 4A: Channel Efficiency Ranking\n\n| Rank | Channel | CPA | Funnel-Adj CAC | Share of Spend | Share of Conversions | Efficiency Index |\n|------|---------|-----|---------------|----------------|---------------------|-----------------|\n| 1 | [Channel] | $[X] | $[X] | [X%] | [X%] | [Conv share ÷ Spend share] |\n\n**Efficiency Index:**\n- **> 1.0** = Under-invested (getting more than its share of conversions)\n- **= 1.0** = Proportional (fair share)\n- **\u003C 1.0** = Over-invested (getting less than its share)\n\n### 4B: Marginal Return Analysis\n\nFor each channel, estimate if additional spend would yield proportional returns:\n\n| Channel | Current CPA | Impression Share \u002F Saturation Signal | Marginal Return Estimate |\n|---------|-------------|-------------------------------------|------------------------|\n| Google Search | $[X] | [X%] impression share — room to grow | Likely positive |\n| Meta | $[X] | Frequency [X] — audience may be saturated | Diminishing |\n| LinkedIn | $[X] | Low volume — limited targeting pool | Ceiling soon |\n\n### 4C: Funnel Stage Coverage\n\n| Funnel Stage | Channels Covering It | Current Spend | Gap? |\n|-------------|---------------------|--------------|------|\n| **Awareness** (top) | [Meta Display, YouTube] | $[X] | [Yes\u002FNo] |\n| **Consideration** (mid) | [Google Search, Meta retargeting] | $[X] | [Yes\u002FNo] |\n| **Decision** (bottom) | [Google Brand, Google Search] | $[X] | [Yes\u002FNo] |\n| **Retargeting** | [Meta, Google Display] | $[X] | [Yes\u002FNo] |\n\n### 4D: Budget Shift Recommendations\n\n| Channel | Current Spend | Recommended Spend | Change | Reasoning |\n|---------|-------------|------------------|--------|-----------|\n| Google Search | $[X] | $[Y] | +$[Z] | [Lowest CPA, room to scale] |\n| Meta | $[X] | $[Y] | -$[Z] | [Audience saturation, frequency too high] |\n| LinkedIn | $[X] | $[Y] | $0 | [Maintain — niche but valuable] |\n| [New channel] | $0 | $[Y] | +$[Y] | [Test budget — competitors succeeding here] |\n| **Total** | $[X] | $[X] | $0 | Budget-neutral reallocation |\n\n### 4E: Scenario Modeling\n\n**Scenario 1: Conservative shift (+\u002F- 20%)**\n- Expected conversions: [N] (currently [N]) = [X%] improvement\n- Expected blended CPA: $[X] (currently $[X])\n- Risk: Low\n\n**Scenario 2: Aggressive shift (+\u002F- 40%)**\n- Expected conversions: [N] = [X%] improvement\n- Expected blended CPA: $[X]\n- Risk: Medium — less data on scaled channels\n\n**Scenario 3: Budget increase to $[Y]\u002Fmo**\n- Recommended allocation: [table]\n- Expected conversions: [N]\n- New channels to test: [list]\n\n## Phase 5: Output Format\n\n```markdown\n# Ad Campaign Analysis — [Product\u002FClient] — [DATE]\n\nPeriod: [Date range]\nTotal spend: $[X]\nPlatform(s): [Google \u002F Meta \u002F LinkedIn]\nPrimary goal: [Conversions \u002F Revenue \u002F Leads]\n\n---\n\n## Executive Summary\n\n[3-5 sentences: Overall performance verdict, biggest win, biggest problem, top recommendation including any reallocation moves]\n\n---\n\n## Performance Dashboard\n\n| Campaign | Spend | Impressions | Clicks | CTR | CPC | Conversions | CPA | ROAS | Verdict |\n|----------|-------|------------|--------|-----|-----|-------------|-----|------|---------|\n| [Name] | $[X] | [N] | [N] | [X%] | $[X] | [N] | $[X] | [X] | [Scale\u002FOptimize\u002FPause] |\n\n---\n\n## Budget Waste Report\n\n**Total estimated waste: $[X] ([X%] of total spend)**\n\n### Wasted on zero-conversion items: $[X]\n[List of keywords\u002Fads\u002Faudiences with spend but no conversions]\n\n### Wasted on high-CPA items: $[X]\n[List of items with CPA > 3x target]\n\n### Recommended saves: $[X]\u002Fmonth\n[Specific items to pause]\n\n---\n\n## Winners to Scale\n\n### Top Keywords\u002FAudiences\n| Item | CPA | Conv Rate | Current Spend | Recommended Spend |\n|------|-----|----------|--------------|-------------------|\n\n### Top Ads\n| Ad | CTR | Conv Rate | Why It Works |\n|----|-----|----------|-------------|\n\n---\n\n## A\u002FB Test Results\n\n### [Test Name]\n- Variant A: [Metric] (n=[N])\n- Variant B: [Metric] (n=[N])\n- Confidence: [X%]\n- **Verdict:** [Winner \u002F Continue \u002F Inconclusive]\n\n---\n\n## Budget Reallocation\n\n### Current vs Recommended Allocation\n\n| Channel | Current | Recommended | Change | Why |\n|---------|---------|------------|--------|-----|\n| [Channel] | $[X] | $[Y] | [+\u002F-$Z] | [1-line reason] |\n\n**Projected impact:**\n- Conversions: [N] → [N] (+[X%])\n- Blended CPA: $[X] → $[Y] (-[X%])\n\n### Funnel Stage Coverage\n[Coverage map with gaps identified]\n\n### New Channel Recommendations\n\n#### [Channel Name]\n- **Why test:** [Reasoning]\n- **Recommended test budget:** $[X]\u002Fmo for [X weeks]\n- **Success criteria:** CPA \u003C $[X]\n- **Competitors using it:** [Yes\u002FNo — who]\n\n---\n\n## Action Plan\n\n### Immediate (This Week)\n- [ ] **Pause:** [Specific items — keywords, ads, audiences]\n- [ ] **Scale:** [Specific items — increase budget\u002Fbids]\n- [ ] **Add negatives:** [Specific keywords from search terms]\n- [ ] **Reallocate:** [Specific dollar shifts between channels]\n\n### This Month\n- [ ] **Test:** [New ad angles \u002F audiences \u002F landing pages]\n- [ ] **Restructure:** [Ad groups that need splitting or merging]\n- [ ] **Optimize:** [Bid strategy changes]\n- [ ] **Monitor reallocation:** Track CPA shifts on scaled channels, watch for diminishing returns\n\n### Next Month\n- [ ] **Expand:** [New campaigns \u002F channels to test]\n- [ ] **Re-evaluate:** [Run this analysis again with new data, adjust allocations based on actual results]\n```\n\nSave to `campaign-analysis-[YYYY-MM-DD].md` in the current working directory (or user-specified path).\n\n## Cost\n\n| Component | Cost |\n|-----------|------|\n| Data analysis | Free (LLM reasoning) |\n| Statistical calculations | Free |\n| **Total** | **Free** |\n\n## Tools Required\n\n- No external tools needed — pure reasoning skill\n- User provides campaign data as CSV, paste, or screenshot\n\n## Trigger Phrases\n\n- \"Analyze my ad campaign performance\"\n- \"Which ads should I pause?\"\n- \"Where am I wasting ad budget?\"\n- \"Is my Google Ads campaign working?\"\n- \"Optimize my Meta Ads spend\"\n- \"How should I allocate my ad budget?\"\n- \"Should I spend more on Google or Meta?\"\n- \"Reallocate my ad spend\"\n- \"Where am I getting the best ROAS?\"\n- \"Optimize my multi-channel ad budget\"\n","# Ad Campaign Analyzer\n\nTake raw campaign performance data and turn it into clear decisions. This skill doesn't just summarize metrics — it diagnoses problems, identifies winners, checks statistical significance, and tells you exactly what to cut, scale, and test next. Then it goes further: it compares channels on equal terms, finds where you're over-spending vs under-spending relative to results, and produces a concrete budget reallocation plan.\n\n**Core principle:** Most startup founders check their ad dashboard, see a ROAS number, and either panic or celebrate. This skill gives you the nuanced analysis a paid media specialist would: what's actually significant, what's noise, and where your next dollar should go. It also solves the allocation problem — most startups either spread budget too thin across channels (no channel gets enough to learn) or dump everything into one channel (missing cheaper opportunities elsewhere).\n\n## When to Use\n\n- \"Analyze my Google Ads performance\"\n- \"Which ads should I kill?\"\n- \"Is this campaign working?\"\n- \"Where am I wasting ad spend?\"\n- \"Optimize my Meta Ads\"\n- \"How should I split my ad budget?\"\n- \"Should I spend more on Google or Meta?\"\n- \"Reallocate my ad spend across channels\"\n- \"Where am I getting the best return?\"\n- \"I have $X\u002Fmonth for ads — how should I distribute it?\"\n\n## Phase 0: Intake\n\n1. **Campaign data** — One of:\n   - CSV export from Google Ads \u002F Meta Ads Manager \u002F LinkedIn Campaign Manager\n   - Pasted performance table\n   - Screenshots of dashboard (we'll extract the data)\n2. **Platform(s)** — Google \u002F Meta \u002F LinkedIn \u002F All\n3. **Time period** — What date range does this cover?\n4. **Monthly budget** — Total ad spend in this period\n5. **Primary goal** — What conversion are you optimizing for? (Demos \u002F Trials \u002F Purchases \u002F Leads)\n6. **Target metrics** — Do you have target CPA or ROAS? (If not, we'll benchmark)\n7. **Any known changes?** — Did you change creative, budget, or targeting during this period?\n8. **Channels currently running** — Google Ads, Meta Ads, LinkedIn Ads, Twitter\u002FX Ads, TikTok Ads, other\n9. **Funnel data** (if available):\n   - Lead → MQL rate\n   - MQL → SQL rate\n   - SQL → Close rate\n   - Average deal size\n10. **Channels you're considering but haven't tried** — Want to test new channels?\n11. **Constraints** — Minimum spend on any channel? Platform you must stay on?\n\n## Phase 1: Data Ingestion & Normalization\n\n### Accepted Data Formats\n\n| Source | Key Columns Expected |\n|--------|---------------------|\n| **Google Ads** | Campaign, Ad Group, Keyword, Impressions, Clicks, CTR, CPC, Conversions, Conv Rate, Cost, Conv Value |\n| **Meta Ads** | Campaign, Ad Set, Ad, Impressions, Reach, Clicks, CTR, CPC, Conversions, Cost Per Result, Amount Spent, ROAS |\n| **LinkedIn Ads** | Campaign, Impressions, Clicks, CTR, CPC, Conversions, Cost, Leads |\n\nNormalize all data into a standard analysis format:\n\n| Dimension | Impressions | Clicks | CTR | CPC | Conversions | Conv Rate | CPA | Spend | Revenue\u002FValue |\n|-----------|------------|--------|-----|-----|-------------|----------|-----|-------|--------------|\n\n### Multi-Channel Normalization\n\nWhen data spans multiple channels, also produce a channel-level rollup:\n\n| Channel | Monthly Spend | Impressions | Clicks | CTR | CPC | Conversions | Conv Rate | CPA | ROAS | CAC* |\n|---------|-------------|------------|--------|-----|-----|-------------|----------|-----|------|------|\n| Google Search | $[X] | [N] | [N] | [X%] | $[X] | [N] | [X%] | $[X] | [X] | $[X] |\n| Google Display | ... | | | | | | | | | |\n| Meta (FB\u002FIG) | ... | | | | | | | | | |\n| LinkedIn | ... | | | | | | | | | |\n| [Other] | ... | | | | | | | | | |\n| **Total** | $[X] | | | | | [N] | | $[X] avg | [X] avg | $[X] avg |\n\n*CAC = Full customer acquisition cost if funnel data provided (CPA × close-rate adjustment)\n\n### Funnel-Adjusted CAC (If Funnel Data Available)\n\n```\nChannel CAC = CPA ÷ (MQL rate × SQL rate × Close rate)\n```\n\nThis reveals which channels produce leads that actually close, not just convert.\n\n## Phase 2: Performance Diagnostics\n\n### 2A: Campaign-Level Health Check\n\nFor each campaign:\n\n| Metric | Value | Benchmark | Status |\n|--------|-------|-----------|--------|\n| CTR | [X%] | [Industry avg] | [Good\u002FOkay\u002FPoor] |\n| CPC | $[X] | [Category avg] | [Good\u002FOkay\u002FPoor] |\n| Conv Rate | [X%] | [Benchmark] | [Good\u002FOkay\u002FPoor] |\n| CPA | $[X] | [Target or benchmark] | [Good\u002FOkay\u002FPoor] |\n| ROAS | [X] | [Target or benchmark] | [Good\u002FOkay\u002FPoor] |\n| Impression Share | [X%] | [>60% ideal] | [Good\u002FOkay\u002FPoor] |\n\n### 2B: Budget Waste Detection\n\nIdentify spend that produced no or negative return:\n\n| Waste Type | Signal | Action |\n|-----------|--------|--------|\n| **Zero-conversion keywords\u002Fads** | Spend > $[X] with 0 conversions | Pause or add negatives |\n| **High CPA outliers** | CPA > 3x target | Pause or restructure |\n| **Low CTR ads** | CTR \u003C 50% of campaign average | Replace creative |\n| **Broad match bleed** | Search terms report showing irrelevant clicks | Add negative keywords |\n| **Audience overlap** | Same users hit by multiple campaigns | Exclude audiences |\n| **Dayparting waste** | Conversions cluster at certain hours; spend is 24\u002F7 | Set ad schedule |\n\n### 2C: Winner Identification\n\nFind what's actually working:\n\n| Winner Type | Signal | Action |\n|------------|--------|--------|\n| **Top-performing keywords** | Lowest CPA, highest conv rate | Increase bid, add variants |\n| **Winning ads** | Highest CTR + conv rate combo | Scale spend, clone for other groups |\n| **Best audiences** | Lowest CPA segment | Increase budget allocation |\n| **Best times** | Peak conversion hours\u002Fdays | Concentrate budget |\n\n### 2D: Statistical Significance Check\n\nFor any A\u002FB test (ad variants, audiences, landing pages):\n\n```\nTest: [Variant A] vs [Variant B]\nMetric: [Conv Rate \u002F CTR \u002F CPA]\nVariant A: [X%] (n=[sample_size])\nVariant B: [Y%] (n=[sample_size])\nConfidence level: [X%]\nVerdict: [Statistically significant \u002F Not enough data \u002F Too close to call]\nRecommended action: [Pick winner \u002F Continue test \u002F Increase budget to reach significance]\n```\n\nMinimum sample: 100 clicks per variant for CTR tests, 30 conversions per variant for CPA tests.\n\n## Phase 3: Funnel Analysis\n\n### Click → Conversion Path\n\n```\nImpressions: [N] (100%)\n     ↓ CTR: [X%]\nClicks: [N] ([X%] of impressions)\n     ↓ Landing page → Conversion: [X%]\nConversions: [N] ([X%] of clicks)\n     ↓ Conversion → Revenue: $[X] avg\nRevenue: $[N]\n```\n\n### Funnel Drop-Off Diagnosis\n\n| Drop-Off Point | Rate | Benchmark | Likely Cause | Fix |\n|----------------|------|-----------|-------------|-----|\n| Impression → Click | [CTR%] | [Benchmark] | [Ad relevance \u002F targeting] | [Copy\u002Ftargeting change] |\n| Click → Conversion | [Conv%] | [Benchmark] | [Landing page \u002F offer \u002F audience mismatch] | [LP optimization] |\n| Conversion → Revenue | [Close%] | [Benchmark] | [Lead quality \u002F sales process] | [Qualification criteria] |\n\n## Phase 4: Budget Reallocation\n\nWhen data spans multiple channels, perform cross-channel budget optimization.\n\n### 4A: Channel Efficiency Ranking\n\n| Rank | Channel | CPA | Funnel-Adj CAC | Share of Spend | Share of Conversions | Efficiency Index |\n|------|---------|-----|---------------|----------------|---------------------|-----------------|\n| 1 | [Channel] | $[X] | $[X] | [X%] | [X%] | [Conv share ÷ Spend share] |\n\n**Efficiency Index:**\n- **> 1.0** = Under-invested (getting more than its share of conversions)\n- **= 1.0** = Proportional (fair share)\n- **\u003C 1.0** = Over-invested (getting less than its share)\n\n### 4B: Marginal Return Analysis\n\nFor each channel, estimate if additional spend would yield proportional returns:\n\n| Channel | Current CPA | Impression Share \u002F Saturation Signal | Marginal Return Estimate |\n|---------|-------------|-------------------------------------|------------------------|\n| Google Search | $[X] | [X%] impression share — room to grow | Likely positive |\n| Meta | $[X] | Frequency [X] — audience may be saturated | Diminishing |\n| LinkedIn | $[X] | Low volume — limited targeting pool | Ceiling soon |\n\n### 4C: Funnel Stage Coverage\n\n| Funnel Stage | Channels Covering It | Current Spend | Gap? |\n|-------------|---------------------|--------------|------|\n| **Awareness** (top) | [Meta Display, YouTube] | $[X] | [Yes\u002FNo] |\n| **Consideration** (mid) | [Google Search, Meta retargeting] | $[X] | [Yes\u002FNo] |\n| **Decision** (bottom) | [Google Brand, Google Search] | $[X] | [Yes\u002FNo] |\n| **Retargeting** | [Meta, Google Display] | $[X] | [Yes\u002FNo] |\n\n### 4D: Budget Shift Recommendations\n\n| Channel | Current Spend | Recommended Spend | Change | Reasoning |\n|---------|-------------|------------------|--------|-----------|\n| Google Search | $[X] | $[Y] | +$[Z] | [Lowest CPA, room to scale] |\n| Meta | $[X] | $[Y] | -$[Z] | [Audience saturation, frequency too high] |\n| LinkedIn | $[X] | $[Y] | $0 | [Maintain — niche but valuable] |\n| [New channel] | $0 | $[Y] | +$[Y] | [Test budget — competitors succeeding here] |\n| **Total** | $[X] | $[X] | $0 | Budget-neutral reallocation |\n\n### 4E: Scenario Modeling\n\n**Scenario 1: Conservative shift (+\u002F- 20%)**\n- Expected conversions: [N] (currently [N]) = [X%] improvement\n- Expected blended CPA: $[X] (currently $[X])\n- Risk: Low\n\n**Scenario 2: Aggressive shift (+\u002F- 40%)**\n- Expected conversions: [N] = [X%] improvement\n- Expected blended CPA: $[X]\n- Risk: Medium — less data on scaled channels\n\n**Scenario 3: Budget increase to $[Y]\u002Fmo**\n- Recommended allocation: [table]\n- Expected conversions: [N]\n- New channels to test: [list]\n\n## Phase 5: Output Format\n\n```markdown\n# Ad Campaign Analysis — [Product\u002FClient] — [DATE]\n\nPeriod: [Date range]\nTotal spend: $[X]\nPlatform(s): [Google \u002F Meta \u002F LinkedIn]\nPrimary goal: [Conversions \u002F Revenue \u002F Leads]\n\n---\n\n## Executive Summary\n\n[3-5 sentences: Overall performance verdict, biggest win, biggest problem, top recommendation including any reallocation moves]\n\n---\n\n## Performance Dashboard\n\n| Campaign | Spend | Impressions | Clicks | CTR | CPC | Conversions | CPA | ROAS | Verdict |\n|----------|-------|------------|--------|-----|-----|-------------|-----|------|---------|\n| [Name] | $[X] | [N] | [N] | [X%] | $[X] | [N] | $[X] | [X] | [Scale\u002FOptimize\u002FPause] |\n\n---\n\n## Budget Waste Report\n\n**Total estimated waste: $[X] ([X%] of total spend)**\n\n### Wasted on zero-conversion items: $[X]\n[List of keywords\u002Fads\u002Faudiences with spend but no conversions]\n\n### Wasted on high-CPA items: $[X]\n[List of items with CPA > 3x target]\n\n### Recommended saves: $[X]\u002Fmonth\n[Specific items to pause]\n\n---\n\n## Winners to Scale\n\n### Top Keywords\u002FAudiences\n| Item | CPA | Conv Rate | Current Spend | Recommended Spend |\n|------|-----|----------|--------------|-------------------|\n\n### Top Ads\n| Ad | CTR | Conv Rate | Why It Works |\n|----|-----|----------|-------------|\n\n---\n\n## A\u002FB Test Results\n\n### [Test Name]\n- Variant A: [Metric] (n=[N])\n- Variant B: [Metric] (n=[N])\n- Confidence: [X%]\n- **Verdict:** [Winner \u002F Continue \u002F Inconclusive]\n\n---\n\n## Budget Reallocation\n\n### Current vs Recommended Allocation\n\n| Channel | Current | Recommended | Change | Why |\n|---------|---------|------------|--------|-----|\n| [Channel] | $[X] | $[Y] | [+\u002F-$Z] | [1-line reason] |\n\n**Projected impact:**\n- Conversions: [N] → [N] (+[X%])\n- Blended CPA: $[X] → $[Y] (-[X%])\n\n### Funnel Stage Coverage\n[Coverage map with gaps identified]\n\n### New Channel Recommendations\n\n#### [Channel Name]\n- **Why test:** [Reasoning]\n- **Recommended test budget:** $[X]\u002Fmo for [X weeks]\n- **Success criteria:** CPA \u003C $[X]\n- **Competitors using it:** [Yes\u002FNo — who]\n\n---\n\n## Action Plan\n\n### Immediate (This Week)\n- [ ] **Pause:** [Specific items — keywords, ads, audiences]\n- [ ] **Scale:** [Specific items — increase budget\u002Fbids]\n- [ ] **Add negatives:** [Specific keywords from search terms]\n- [ ] **Reallocate:** [Specific dollar shifts between channels]\n\n### This Month\n- [ ] **Test:** [New ad angles \u002F audiences \u002F landing pages]\n- [ ] **Restructure:** [Ad groups that need splitting or merging]\n- [ ] **Optimize:** [Bid strategy changes]\n- [ ] **Monitor reallocation:** Track CPA shifts on scaled channels, watch for diminishing returns\n\n### Next Month\n- [ ] **Expand:** [New campaigns \u002F channels to test]\n- [ ] **Re-evaluate:** [Run this analysis again with new data, adjust allocations based on actual results]\n```\n\nSave to `campaign-analysis-[YYYY-MM-DD].md` in the current working directory (or user-specified path).\n\n## Cost\n\n| Component | Cost |\n|-----------|------|\n| Data analysis | Free (LLM reasoning) |\n| Statistical calculations | Free |\n| **Total** | **Free** |\n\n## Tools Required\n\n- No external tools needed — pure reasoning skill\n- User provides campaign data as CSV, paste, or screenshot\n\n## Trigger Phrases\n\n- \"Analyze my ad campaign performance\"\n- \"Which ads should I pause?\"\n- \"Where am I wasting ad budget?\"\n- \"Is my Google Ads campaign working?\"\n- \"Optimize my Meta Ads spend\"\n- \"How should I allocate my ad budget?\"\n- \"Should I spend more on Google or Meta?\"\n- \"Reallocate my ad spend\"\n- \"Where am I getting the best ROAS?\"\n- \"Optimize my multi-channel ad budget\"\n",{"name":5,"license":15,"metadata":66,"description":7,"compatibility":70},{"author":67,"source":68,"version":69},"GooseWorks","https:\u002F\u002Fgithub.com\u002Fgooseworks-ai\u002Fgoose-skills","1.0","Cross-platform. Pure reasoning skill over user-provided campaign exports (CSV, paste, or screenshot from Google, Meta, or LinkedIn) — no external tools, network calls, or API keys.","main","5269f7ea2d6c3a9bfda5ed878b6726cf509121fd","skills\u002Fad-campaign-analyzer",5335,"https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot","https:\u002F\u002Fawesome-copilot.github.com\u002F","https:\u002F\u002Fgithub.com\u002Fgithub","https:\u002F\u002Fgithub.com\u002Fabout","How people build software.","2026-11-11 11:59:04",{"source":8,"skillName":5,"commands":82,"prompt":92,"zipAvailable":93},[83,86,89],{"tool":84,"command":85},"npx","npx skills add github\u002Fawesome-copilot --skill ad-campaign-analyzer",{"tool":87,"command":88},"bunx","bunx skills add github\u002Fawesome-copilot --skill ad-campaign-analyzer",{"tool":90,"command":91},"pnpm","pnpm dlx skills add github\u002Fawesome-copilot --skill ad-campaign-analyzer","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fad-campaign-analyzer\u002FSKILL.md",true,[95,101,105],{"path":96,"resourceType":97,"resourceTypeDesc":98,"sizeBytes":99,"sourceHtmlUrl":100},"agents\u002Fopenai.yaml","REFERENCE","参考文档",100,"https:\u002F\u002Fgithub.com\u002Faliyome\u002Fenv\u002Fblob\u002Fmaster\u002F.rulesync\u002Fskills\u002F.curated\u002Fprototype\u002Fagents\u002Fopenai.yaml",{"path":102,"resourceType":97,"resourceTypeDesc":98,"sizeBytes":103,"sourceHtmlUrl":104},"LOGIC.md",6036,"https:\u002F\u002Fgithub.com\u002Faliyome\u002Fenv\u002Fblob\u002Fmaster\u002F.rulesync\u002Fskills\u002F.curated\u002Fprototype\u002FLOGIC.md",{"path":106,"resourceType":97,"resourceTypeDesc":98,"sizeBytes":107,"sourceHtmlUrl":108},"UI.md",6913,"https:\u002F\u002Fgithub.com\u002Faliyome\u002Fenv\u002Fblob\u002Fmaster\u002F.rulesync\u002Fskills\u002F.curated\u002Fprototype\u002FUI.md",[110,140,168,196,229,245],{"skillNo":111,"name":112,"nameZh":6,"description":113,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":114,"categories":115,"keywords":120,"publishedAt":61},"01M70KXZNX9ES38N2A8M58ZZMZ","supabase-rls-policies","Write, review, and test Supabase Row Level Security (RLS) policies so that one user cannot read or change another user's rows. Use when creating or changing tables, policies, views, or security definer functions in a Supabase project, when reviewing a migration that touches RLS, or when asked to prove user isolation with pgTAP tests that sign in as two users.","https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fsupabase-rls-policies\u002FSKILL.md",[116,119],{"categoryNo":117,"categoryName":118,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},[121,122,123,124,125,40,126,127,128,129,130,131,132,133,134,135,136,137,138,139],"supabase","rls","supabase rls","supabase rls policies","project","review","change","read","write","security","isolation","tables","as","tests","prove","use","users","functions","with",{"skillNo":141,"name":142,"nameZh":6,"description":143,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":144,"categories":145,"keywords":147,"publishedAt":61},"01M70KWA5YWXDV6V8DZ19E3T2Y","skill-distribution-audit","Audit a skill collection before release or investigate installer omissions, archived\u002Ftemplate leakage, duplicate identities, and manifest drift. Reconcile the intended public source set, distribution manifest, and actual discovery output for a specified consumer version. Includes a deterministic set-comparison helper.","https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fskill-distribution-audit\u002FSKILL.md",[146],{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},[148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167],"distribution","skill distribution audit","audit","SET","deterministic","installer","for","discovery","comparison","helper","manifest","version","collection","drift","output","investigate","duplicate","template","release","reconcile",{"skillNo":169,"name":170,"nameZh":6,"description":171,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":172,"sourceHtmlUrl":173,"categories":174,"keywords":178,"publishedAt":61},"01M70KCJPFVW7PPQ49CC5R8JGK","logicapps-custom-code-net10-migration","Migrate Azure Logic Apps Standard local-function custom-code projects from .NET 8 to .NET 10. Use for legacy Logic Apps custom-code workspace upgrades, project\u002Fpackage configuration, custom-worker settings, publication, and integrated VS Code debugging. Not for ordinary .NET applications, standalone Azure Functions, Consumption workflows, or cloud deployment.","https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Flogicapps-custom-code-net10-migration\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Flogicapps-custom-code-net10-migration\u002FSKILL.md",[175],{"categoryNo":176,"categoryName":177,"taxonomyType":29,"taxonomyTypeDesc":30},"01JBSEEDCAT000000000000030","开发工程师",[179,180,181,182,183,184,154,125,185,186,187,188,189,190,191,136,192,193,194,195],"custom","code","logicapps custom code net10 migration","migration","projects","standalone","deployment","applications","local","azure","worker","upgrades","package","apps","Legacy","debugging","workflows",{"skillNo":197,"name":198,"nameZh":6,"description":199,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":200,"sourceHtmlUrl":201,"categories":202,"keywords":207,"publishedAt":61},"01M6AEBN37RQMXJ3298PEY7PG1","winmd-api-search","Find and explore Windows desktop APIs. Use when building features that need platform capabilities — camera, file access, notifications, UI controls, AI\u002FML, sensors, networking, etc. Discovers the right API for a task and retrieves full type details (methods, properties, events, enumeration values).","https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fwinmd-api-search\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fwinmd-api-search\u002FSKILL.md",[203,206],{"categoryNo":204,"categoryName":205,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":176,"categoryName":177,"taxonomyType":29,"taxonomyTypeDesc":30},[208,209,48,210,211,212,213,214,215,216,217,218,219,220,221,40,222,223,224,225,226,154,227,228],"winmd api search","search","windows","desktop","file access","enumeration","ui","ml","file","networking","capabilities","notifications","explore","access","features","full","camera","events","platform","methods","controls",{"skillNo":230,"name":231,"nameZh":6,"description":232,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":233,"licenseFileUrl":234,"sourceHtmlUrl":235,"categories":236,"keywords":242,"publishedAt":61},"01M6AE571R3SC1ZGE202B3NB5S","resemble-detect","Deepfake detection and media safety — detect AI-generated audio, images, video, and text, trace synthesis sources, apply watermarks, verify speaker identity, and analyze media intelligence using Resemble AI","Apache-2.0","https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fresemble-detect\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fresemble-detect\u002FSKILL.md",[237,238,241],{"categoryNo":204,"categoryName":205,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":239,"categoryName":240,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000016","图像与多媒体",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},[243,151,216,55,244],"resemble detect","identity",{"skillNo":246,"name":247,"nameZh":6,"description":248,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":233,"licenseFileUrl":249,"sourceHtmlUrl":250,"categories":251,"keywords":256,"publishedAt":61},"01M6AE30BSK2HCY4TQQ22Z8JRD","quality-playbook","Run a complete quality engineering audit on any codebase. Derives behavioral requirements from the code, generates spec-traced functional tests, runs a three-pass code review with regression tests, executes a multi-model spec audit (Council of Three), and produces a consolidated bug report with TDD-verified patches. Finds the 35% of real defects that structural code review alone cannot catch. Works with any language. Trigger on 'quality playbook', 'spec audit', 'Council of Three', 'fitness-to-purpose', or 'coverage theater'.","https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fquality-playbook\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fquality-playbook\u002FSKILL.md",[252,255],{"categoryNo":253,"categoryName":254,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000007","代码审查",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},[257,258,259,260,180,126,9,150,261,262,55,263,264,265,266,267,268,269,270,271,272,273,134,274,275,276,277],"playbook","quality playbook","quality","code review","tdd","run","report","spec","bug report","council","engineering","functional","spec audit","bug","verified","multi-model","three","coverage","model","regression","structural"]