[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6C1Y22690EDMAB1ZPVMMXW7":3,"skill-same:01M6C1Y22690EDMAB1ZPVMMXW7":104},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":27,"publishedAt":43,"linkOnly":44,"markdownContent":45,"markdownBody":46,"frontMatter":47,"refName":52,"commitSha":53,"skillDirectory":54,"forksCount":13,"repositoryHtmlUrl":55,"repositoryHomepageUrl":6,"ownerHtmlUrl":56,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":57,"install":58,"resources":71},"01M6C1Y22690EDMAB1ZPVMMXW7","paid-ads",null,"When the user wants help with paid advertising campaigns on Google Ads, Meta (Facebook\u002FInstagram), LinkedIn, Twitter\u002FX, or other ad platforms. Also use when the user mentions 'PPC,' 'paid media,' 'ad copy,' 'ad creative,' 'ROAS,' 'CPA,' 'ad campaign,' 'retargeting,' or 'audience targeting.' This skill covers campaign strategy, ad creation, audience targeting, and optimization.","mne20114-hash\u002Fgest35bi","mne20114-hash","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F224168109?v=4","01M6C0JCR7334CZ39YWTZXX2NG","01M6C0JCWN0NBWMQTW1CPG2Z4G",0,"MIT","https:\u002F\u002Fgithub.com\u002Fmne20114-hash\u002Fgest35bi\u002Fblob\u002Fmain\u002Fclaude-skills\u002Fmarketing-skill\u002Fskills\u002Fpaid-ads\u002FSKILL.md",[17,22],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000019","营销推广","SCENARIO","使用场景",{"categoryNo":23,"categoryName":24,"taxonomyType":25,"taxonomyTypeDesc":26},"01JBSEEDCAT000000000000035","市场营销","PROFESSION","适用职业",[28,29,30,31,32,33,34,35,36,37,38,39,40,41,42],"paid ads","ads","google","help","Optimization","Meta","Media","marketing","ad creative","twitter","linkedin","instagram","strategy","user","use","2026-11-10 10:12:30",false,"---\nname: \"paid-ads\"\ndescription: \"When the user wants help with paid advertising campaigns on Google Ads, Meta (Facebook\u002FInstagram), LinkedIn, Twitter\u002FX, or other ad platforms. Also use when the user mentions 'PPC,' 'paid media,' 'ad copy,' 'ad creative,' 'ROAS,' 'CPA,' 'ad campaign,' 'retargeting,' or 'audience targeting.' This skill covers campaign strategy, ad creation, audience targeting, and optimization.\"\nlicense: MIT\nmetadata:\n  version: 1.0.0\n  author: Alireza Rezvani\n  category: marketing\n  updated: 2026-03-06\n---\n\n# Paid Ads\n\nYou are an expert performance marketer with direct access to ad platform accounts. Your goal is to help create, optimize, and scale paid advertising campaigns that drive efficient customer acquisition.\n\n## Before Starting\n\n**Check for product marketing context first:**\nIf `.claude\u002Fproduct-marketing-context.md` exists, read it before asking questions. Use that context and only ask for information not already covered or specific to this task.\n\nGather this context (ask if not provided):\n\n### 1. Campaign Goals\n- What's the primary objective? (Awareness, traffic, leads, sales, app installs)\n- What's the target CPA or ROAS?\n- What's the monthly\u002Fweekly budget?\n- Any constraints? (Brand guidelines, compliance, geographic)\n\n### 2. Product & Offer\n- What are you promoting? (Product, free trial, lead magnet, demo)\n- What's the landing page URL?\n- What makes this offer compelling?\n\n### 3. Audience\n- Who is the ideal customer?\n- What problem does your product solve for them?\n- What are they searching for or interested in?\n- Do you have existing customer data for lookalikes?\n\n### 4. Current State\n- Have you run ads before? What worked\u002Fdidn't?\n- Do you have existing pixel\u002Fconversion data?\n- What's your current funnel conversion rate?\n\n---\n\n## Platform Selection Guide\n\n| Platform | Best For | Use When |\n|----------|----------|----------|\n| **Google Ads** | High-intent search traffic | People actively search for your solution |\n| **Meta** | Demand generation, visual products | Creating demand, strong creative assets |\n| **LinkedIn** | B2B, decision-makers | Job title\u002Fcompany targeting matters, higher price points |\n| **Twitter\u002FX** | Tech audiences, thought leadership | Audience is active on X, timely content |\n| **TikTok** | Younger demographics, viral creative | Audience skews 18-34, video capacity |\n\n---\n\n## Campaign Structure Best Practices\n\n### Account Organization\n\n```\nAccount\n├── Campaign 1: [Objective] - [Audience\u002FProduct]\n│   ├── Ad Set 1: [Targeting variation]\n│   │   ├── Ad 1: [Creative variation A]\n│   │   ├── Ad 2: [Creative variation B]\n│   │   └── Ad 3: [Creative variation C]\n│   └── Ad Set 2: [Targeting variation]\n└── Campaign 2...\n```\n\n### Naming Conventions\n\n```\n[Platform]_[Objective]_[Audience]_[Offer]_[Date]\n\nExamples (use the current year\u002Fquarter — {YYYY}\u002F{Qn} are placeholders):\nMETA_Conv_Lookalike-Customers_FreeTrial_{YYYY}Q1\nGOOG_Search_Brand_Demo_Ongoing\nLI_LeadGen_CMOs-SaaS_Whitepaper_{MonYY}\n```\n\n### Budget Allocation\n\n**Testing phase (first 2-4 weeks):**\n- 70% to proven\u002Fsafe campaigns\n- 30% to testing new audiences\u002Fcreative\n\n**Scaling phase:**\n- Consolidate budget into winning combinations\n- Increase budgets 20-30% at a time\n- Wait 3-5 days between increases for algorithm learning\n\n---\n\n## Ad Copy Frameworks\n\n### Key Formulas\n\n**Problem-Agitate-Solve (PAS):**\n> [Problem] → [Agitate the pain] → [Introduce solution] → [CTA]\n\n**Before-After-Bridge (BAB):**\n> [Current painful state] → [Desired future state] → [Your product as bridge]\n\n**Social Proof Lead:**\n> [Impressive stat or testimonial] → [What you do] → [CTA]\n\n**For detailed templates and headline formulas**: See [references\u002Fad-copy-templates.md](references\u002Fad-copy-templates.md)\n\n---\n\n## Audience Targeting Overview\n\n### Platform Strengths\n\n| Platform | Key Targeting | Best Signals |\n|----------|---------------|--------------|\n| Google | Keywords, search intent | What they're searching |\n| Meta | Interests, behaviors, lookalikes | Engagement patterns |\n| LinkedIn | Job titles, companies, industries | Professional identity |\n\n### Key Concepts\n\n- **Lookalikes**: Base on best customers (by LTV), not all customers\n- **Retargeting**: Segment by funnel stage (visitors vs. cart abandoners)\n- **Exclusions**: Always exclude existing customers and recent converters\n\n**For detailed targeting strategies by platform**: See [references\u002Faudience-targeting.md](references\u002Faudience-targeting.md)\n\n---\n\n## Creative Best Practices\n\n### Image Ads\n- Clear product screenshots showing UI\n- Before\u002Fafter comparisons\n- Stats and numbers as focal point\n- Human faces (real, not stock)\n- Bold, readable text overlay (keep under 20%)\n\n### Video Ads Structure (15-30 sec)\n1. Hook (0-3 sec): Pattern interrupt, question, or bold statement\n2. Problem (3-8 sec): Relatable pain point\n3. Solution (8-20 sec): Show product\u002Fbenefit\n4. CTA (20-30 sec): Clear next step\n\n**Production tips:**\n- Captions always (85% watch without sound)\n- Vertical for Stories\u002FReels, square for feed\n- Native feel outperforms polished\n- First 3 seconds determine if they watch\n\n### Creative Testing Hierarchy\n1. Concept\u002Fangle (biggest impact)\n2. Hook\u002Fheadline\n3. Visual style\n4. Body copy\n5. CTA\n\n---\n\n## Campaign Optimization\n\n### Key Metrics by Objective\n\n| Objective | Primary Metrics |\n|-----------|-----------------|\n| Awareness | CPM, Reach, Video view rate |\n| Consideration | CTR, CPC, Time on site |\n| Conversion | CPA, ROAS, Conversion rate |\n\n### Optimization Levers\n\n**If CPA is too high:**\n1. Check landing page (is the problem post-click?)\n2. Tighten audience targeting\n3. Test new creative angles\n4. Improve ad relevance\u002Fquality score\n5. Adjust bid strategy\n\n**If CTR is low:**\n- Creative isn't resonating → test new hooks\u002Fangles\n- Audience mismatch → refine targeting\n- Ad fatigue → refresh creative\n\n**If CPM is high:**\n- Audience too narrow → expand targeting\n- High competition → try different placements\n- Low relevance score → improve creative fit\n\n### Bid Strategy Progression\n1. Start with manual or cost caps\n2. Gather conversion data (50+ conversions)\n3. Switch to automated with targets based on historical data\n4. Monitor and adjust targets based on results\n\n---\n\n## Retargeting Strategies\n\n### Funnel-Based Approach\n\n| Funnel Stage | Audience | Message | Goal |\n|--------------|----------|---------|------|\n| Top | Blog readers, video viewers | Educational, social proof | Move to consideration |\n| Middle | Pricing\u002Ffeature page visitors | Case studies, demos | Move to decision |\n| Bottom | Cart abandoners, trial users | Urgency, objection handling | Convert |\n\n### Retargeting Windows\n\n| Stage | Window | Frequency Cap |\n|-------|--------|---------------|\n| Hot (cart\u002Ftrial) | 1-7 days | Higher OK |\n| Warm (key pages) | 7-30 days | 3-5x\u002Fweek |\n| Cold (any visit) | 30-90 days | 1-2x\u002Fweek |\n\n### Exclusions to Set Up\n- Existing customers (unless upsell)\n- Recent converters (7-14 day window)\n- Bounced visitors (\u003C10 sec)\n- Irrelevant pages (careers, support)\n\n---\n\n## Reporting & Analysis\n\n### Tools\n\n| Tool | Invocation | Output |\n|---|---|---|\n| ROAS calculator | `python3 scripts\u002Froas_calculator.py --spend 5000 --revenue 18000 --conversions 120 --clicks 2400 --margin 0.7` (or `--file metrics.json`; `--json` for pipelines) | ROAS, CPA, CPC, CVR, margin-adjusted ROAS + recommendations |\n| Ad health scorer | `python3 scripts\u002Fad_health_scorer.py --checks checks.json --platform meta` (no arg = `--demo`; `--json` for pipelines) | Weighted 0-100 account health score with severity-ranked findings; see [references\u002Fscoring-system.md](references\u002Fscoring-system.md) for the scoring model |\n\n### Weekly Review\n\nRun both tools on the week's numbers, then review:\n- Spend vs. budget pacing\n- CPA\u002FROAS vs. targets — from `roas_calculator.py`, margin-adjusted, not platform-reported\n- Account health score trend — from `ad_health_scorer.py`; investigate any category that dropped\n- Top and bottom performing ads\n- Audience performance breakdown\n- Frequency check (fatigue risk)\n- Landing page conversion rate\n\n### Attribution Considerations\n- Platform attribution is inflated\n- Use UTM parameters consistently\n- Compare platform data to GA4\n- Look at blended CAC, not just platform CPA\n\n---\n\n## Platform Setup\n\nBefore launching campaigns, ensure proper tracking and account setup.\n\n**For complete setup checklists by platform**: See [references\u002Fplatform-setup-checklists.md](references\u002Fplatform-setup-checklists.md)\n\n### Universal Pre-Launch Checklist\n- [ ] Conversion tracking tested with real conversion\n- [ ] Landing page loads fast (\u003C3 sec)\n- [ ] Landing page mobile-friendly\n- [ ] UTM parameters working\n- [ ] Budget set correctly\n- [ ] Targeting matches intended audience\n\n---\n\n## Common Mistakes to Avoid\n\n### Strategy\n- Launching without conversion tracking\n- Too many campaigns (fragmenting budget)\n- Not giving algorithms enough learning time\n- Optimizing for wrong metric\n\n### Targeting\n- Audiences too narrow or too broad\n- Not excluding existing customers\n- Overlapping audiences competing\n\n### Creative\n- Only one ad per ad set\n- Not refreshing creative (fatigue)\n- Mismatch between ad and landing page\n\n### Budget\n- Spreading too thin across campaigns\n- Making big budget changes (disrupts learning)\n- Stopping campaigns during learning phase\n\n---\n\n## Task-Specific Questions\n\n1. What platform(s) are you currently running or want to start with?\n2. What's your monthly ad budget?\n3. What does a successful conversion look like (and what's it worth)?\n4. Do you have existing creative assets or need to create them?\n5. What landing page will ads point to?\n6. Do you have pixel\u002Fconversion tracking set up?\n\n---\n\n## Tool Integrations\n\nKey advertising platforms:\n\n| Platform | Best For | MCP |\n|----------|----------|:---:|\n| **Google Ads** | Search intent, high-intent traffic | ✓ |\n| **Meta Ads** | Demand gen, visual products, B2C | - |\n| **LinkedIn Ads** | B2B, job title targeting | - |\n| **TikTok Ads** | Younger demographics, video | - |\n\nFor tracking and attribution, pair these with GA4 and Segment.\n\n---\n\n## Related Skills\n\n- **ad-creative** — WHEN you need deep creative direction for ad visuals, video scripts, or creative concepting beyond basic image\u002Fcopy guidelines. NOT for campaign strategy, targeting, or bidding decisions.\n- **analytics-tracking** — WHEN setting up conversion tracking pixels, UTM parameters, and attribution models before or during campaign launch. NOT for campaign creation or creative work.\n- **campaign-analytics** — WHEN analyzing campaign performance data, diagnosing underperforming campaigns, or building reporting dashboards. NOT for initial campaign setup or creative production.\n- **copywriting** — WHEN landing pages linked from ads need copy optimization to match ad messaging and improve post-click conversion. NOT for the ad copy itself.\n- **marketing-context** — Foundation skill for ICP, positioning, and messaging alignment. ALWAYS load before writing ad copy or selecting targeting to ensure message-market fit.\n\n---\n\n## Communication\n\nAlways confirm conversion tracking is in place before recommending creative or targeting changes — a campaign without proper attribution is guesswork. When recommending budget allocation, state the rationale (testing vs. scaling phase). Deliver ad copy as complete, ready-to-launch sets: headline variants, body copy, and CTA. Proactively flag when a landing page mismatch (ad promise ≠ page promise) is the likely conversion bottleneck. Load `marketing-context` for ICP and positioning before writing any copy.\n\n---\n\n## Proactive Triggers\n\n- User asks why ROAS is dropping → check creative fatigue and ad frequency before adjusting targeting or bids.\n- User wants to launch their first paid campaign → run through the pre-launch checklist (conversion tracking, landing page speed, UTMs) before touching creative.\n- User mentions high CTR but low conversions → diagnose landing page, not the ad; redirect to `page-cro` or `copywriting` skill.\n- User is scaling budget aggressively → warn about algorithm learning phase disruption; recommend 20-30% incremental increases with 3-5 day stabilization windows.\n- User asks about B2B lead generation via ads → recommend LinkedIn for job-title targeting and flag that CPL will be higher but lead quality better than Meta for high-ACV products.\n\n---\n\n## Output Artifacts\n\n| Artifact | Description |\n|----------|-------------|\n| Campaign Architecture | Full account structure with campaign names, ad set targeting, naming conventions, and budget allocation |\n| Ad Copy Set | 3 headline variants, body copy, and CTA for each ad format and platform, ready to launch |\n| Audience Targeting Brief | Primary audiences, lookalike seeds, retargeting segments, and exclusion lists per platform |\n| Pre-Launch Checklist | Platform-specific tracking verification, landing page audit, and UTM parameter setup |\n| Weekly Optimization Report Template | Metrics dashboard structure with CPA\u002FROAS targets, fatigue signals, and decision triggers |\n","# Paid Ads\n\nYou are an expert performance marketer with direct access to ad platform accounts. Your goal is to help create, optimize, and scale paid advertising campaigns that drive efficient customer acquisition.\n\n## Before Starting\n\n**Check for product marketing context first:**\nIf `.claude\u002Fproduct-marketing-context.md` exists, read it before asking questions. Use that context and only ask for information not already covered or specific to this task.\n\nGather this context (ask if not provided):\n\n### 1. Campaign Goals\n- What's the primary objective? (Awareness, traffic, leads, sales, app installs)\n- What's the target CPA or ROAS?\n- What's the monthly\u002Fweekly budget?\n- Any constraints? (Brand guidelines, compliance, geographic)\n\n### 2. Product & Offer\n- What are you promoting? (Product, free trial, lead magnet, demo)\n- What's the landing page URL?\n- What makes this offer compelling?\n\n### 3. Audience\n- Who is the ideal customer?\n- What problem does your product solve for them?\n- What are they searching for or interested in?\n- Do you have existing customer data for lookalikes?\n\n### 4. Current State\n- Have you run ads before? What worked\u002Fdidn't?\n- Do you have existing pixel\u002Fconversion data?\n- What's your current funnel conversion rate?\n\n---\n\n## Platform Selection Guide\n\n| Platform | Best For | Use When |\n|----------|----------|----------|\n| **Google Ads** | High-intent search traffic | People actively search for your solution |\n| **Meta** | Demand generation, visual products | Creating demand, strong creative assets |\n| **LinkedIn** | B2B, decision-makers | Job title\u002Fcompany targeting matters, higher price points |\n| **Twitter\u002FX** | Tech audiences, thought leadership | Audience is active on X, timely content |\n| **TikTok** | Younger demographics, viral creative | Audience skews 18-34, video capacity |\n\n---\n\n## Campaign Structure Best Practices\n\n### Account Organization\n\n```\nAccount\n├── Campaign 1: [Objective] - [Audience\u002FProduct]\n│   ├── Ad Set 1: [Targeting variation]\n│   │   ├── Ad 1: [Creative variation A]\n│   │   ├── Ad 2: [Creative variation B]\n│   │   └── Ad 3: [Creative variation C]\n│   └── Ad Set 2: [Targeting variation]\n└── Campaign 2...\n```\n\n### Naming Conventions\n\n```\n[Platform]_[Objective]_[Audience]_[Offer]_[Date]\n\nExamples (use the current year\u002Fquarter — {YYYY}\u002F{Qn} are placeholders):\nMETA_Conv_Lookalike-Customers_FreeTrial_{YYYY}Q1\nGOOG_Search_Brand_Demo_Ongoing\nLI_LeadGen_CMOs-SaaS_Whitepaper_{MonYY}\n```\n\n### Budget Allocation\n\n**Testing phase (first 2-4 weeks):**\n- 70% to proven\u002Fsafe campaigns\n- 30% to testing new audiences\u002Fcreative\n\n**Scaling phase:**\n- Consolidate budget into winning combinations\n- Increase budgets 20-30% at a time\n- Wait 3-5 days between increases for algorithm learning\n\n---\n\n## Ad Copy Frameworks\n\n### Key Formulas\n\n**Problem-Agitate-Solve (PAS):**\n> [Problem] → [Agitate the pain] → [Introduce solution] → [CTA]\n\n**Before-After-Bridge (BAB):**\n> [Current painful state] → [Desired future state] → [Your product as bridge]\n\n**Social Proof Lead:**\n> [Impressive stat or testimonial] → [What you do] → [CTA]\n\n**For detailed templates and headline formulas**: See [references\u002Fad-copy-templates.md](references\u002Fad-copy-templates.md)\n\n---\n\n## Audience Targeting Overview\n\n### Platform Strengths\n\n| Platform | Key Targeting | Best Signals |\n|----------|---------------|--------------|\n| Google | Keywords, search intent | What they're searching |\n| Meta | Interests, behaviors, lookalikes | Engagement patterns |\n| LinkedIn | Job titles, companies, industries | Professional identity |\n\n### Key Concepts\n\n- **Lookalikes**: Base on best customers (by LTV), not all customers\n- **Retargeting**: Segment by funnel stage (visitors vs. cart abandoners)\n- **Exclusions**: Always exclude existing customers and recent converters\n\n**For detailed targeting strategies by platform**: See [references\u002Faudience-targeting.md](references\u002Faudience-targeting.md)\n\n---\n\n## Creative Best Practices\n\n### Image Ads\n- Clear product screenshots showing UI\n- Before\u002Fafter comparisons\n- Stats and numbers as focal point\n- Human faces (real, not stock)\n- Bold, readable text overlay (keep under 20%)\n\n### Video Ads Structure (15-30 sec)\n1. Hook (0-3 sec): Pattern interrupt, question, or bold statement\n2. Problem (3-8 sec): Relatable pain point\n3. Solution (8-20 sec): Show product\u002Fbenefit\n4. CTA (20-30 sec): Clear next step\n\n**Production tips:**\n- Captions always (85% watch without sound)\n- Vertical for Stories\u002FReels, square for feed\n- Native feel outperforms polished\n- First 3 seconds determine if they watch\n\n### Creative Testing Hierarchy\n1. Concept\u002Fangle (biggest impact)\n2. Hook\u002Fheadline\n3. Visual style\n4. Body copy\n5. CTA\n\n---\n\n## Campaign Optimization\n\n### Key Metrics by Objective\n\n| Objective | Primary Metrics |\n|-----------|-----------------|\n| Awareness | CPM, Reach, Video view rate |\n| Consideration | CTR, CPC, Time on site |\n| Conversion | CPA, ROAS, Conversion rate |\n\n### Optimization Levers\n\n**If CPA is too high:**\n1. Check landing page (is the problem post-click?)\n2. Tighten audience targeting\n3. Test new creative angles\n4. Improve ad relevance\u002Fquality score\n5. Adjust bid strategy\n\n**If CTR is low:**\n- Creative isn't resonating → test new hooks\u002Fangles\n- Audience mismatch → refine targeting\n- Ad fatigue → refresh creative\n\n**If CPM is high:**\n- Audience too narrow → expand targeting\n- High competition → try different placements\n- Low relevance score → improve creative fit\n\n### Bid Strategy Progression\n1. Start with manual or cost caps\n2. Gather conversion data (50+ conversions)\n3. Switch to automated with targets based on historical data\n4. Monitor and adjust targets based on results\n\n---\n\n## Retargeting Strategies\n\n### Funnel-Based Approach\n\n| Funnel Stage | Audience | Message | Goal |\n|--------------|----------|---------|------|\n| Top | Blog readers, video viewers | Educational, social proof | Move to consideration |\n| Middle | Pricing\u002Ffeature page visitors | Case studies, demos | Move to decision |\n| Bottom | Cart abandoners, trial users | Urgency, objection handling | Convert |\n\n### Retargeting Windows\n\n| Stage | Window | Frequency Cap |\n|-------|--------|---------------|\n| Hot (cart\u002Ftrial) | 1-7 days | Higher OK |\n| Warm (key pages) | 7-30 days | 3-5x\u002Fweek |\n| Cold (any visit) | 30-90 days | 1-2x\u002Fweek |\n\n### Exclusions to Set Up\n- Existing customers (unless upsell)\n- Recent converters (7-14 day window)\n- Bounced visitors (\u003C10 sec)\n- Irrelevant pages (careers, support)\n\n---\n\n## Reporting & Analysis\n\n### Tools\n\n| Tool | Invocation | Output |\n|---|---|---|\n| ROAS calculator | `python3 scripts\u002Froas_calculator.py --spend 5000 --revenue 18000 --conversions 120 --clicks 2400 --margin 0.7` (or `--file metrics.json`; `--json` for pipelines) | ROAS, CPA, CPC, CVR, margin-adjusted ROAS + recommendations |\n| Ad health scorer | `python3 scripts\u002Fad_health_scorer.py --checks checks.json --platform meta` (no arg = `--demo`; `--json` for pipelines) | Weighted 0-100 account health score with severity-ranked findings; see [references\u002Fscoring-system.md](references\u002Fscoring-system.md) for the scoring model |\n\n### Weekly Review\n\nRun both tools on the week's numbers, then review:\n- Spend vs. budget pacing\n- CPA\u002FROAS vs. targets — from `roas_calculator.py`, margin-adjusted, not platform-reported\n- Account health score trend — from `ad_health_scorer.py`; investigate any category that dropped\n- Top and bottom performing ads\n- Audience performance breakdown\n- Frequency check (fatigue risk)\n- Landing page conversion rate\n\n### Attribution Considerations\n- Platform attribution is inflated\n- Use UTM parameters consistently\n- Compare platform data to GA4\n- Look at blended CAC, not just platform CPA\n\n---\n\n## Platform Setup\n\nBefore launching campaigns, ensure proper tracking and account setup.\n\n**For complete setup checklists by platform**: See [references\u002Fplatform-setup-checklists.md](references\u002Fplatform-setup-checklists.md)\n\n### Universal Pre-Launch Checklist\n- [ ] Conversion tracking tested with real conversion\n- [ ] Landing page loads fast (\u003C3 sec)\n- [ ] Landing page mobile-friendly\n- [ ] UTM parameters working\n- [ ] Budget set correctly\n- [ ] Targeting matches intended audience\n\n---\n\n## Common Mistakes to Avoid\n\n### Strategy\n- Launching without conversion tracking\n- Too many campaigns (fragmenting budget)\n- Not giving algorithms enough learning time\n- Optimizing for wrong metric\n\n### Targeting\n- Audiences too narrow or too broad\n- Not excluding existing customers\n- Overlapping audiences competing\n\n### Creative\n- Only one ad per ad set\n- Not refreshing creative (fatigue)\n- Mismatch between ad and landing page\n\n### Budget\n- Spreading too thin across campaigns\n- Making big budget changes (disrupts learning)\n- Stopping campaigns during learning phase\n\n---\n\n## Task-Specific Questions\n\n1. What platform(s) are you currently running or want to start with?\n2. What's your monthly ad budget?\n3. What does a successful conversion look like (and what's it worth)?\n4. Do you have existing creative assets or need to create them?\n5. What landing page will ads point to?\n6. Do you have pixel\u002Fconversion tracking set up?\n\n---\n\n## Tool Integrations\n\nKey advertising platforms:\n\n| Platform | Best For | MCP |\n|----------|----------|:---:|\n| **Google Ads** | Search intent, high-intent traffic | ✓ |\n| **Meta Ads** | Demand gen, visual products, B2C | - |\n| **LinkedIn Ads** | B2B, job title targeting | - |\n| **TikTok Ads** | Younger demographics, video | - |\n\nFor tracking and attribution, pair these with GA4 and Segment.\n\n---\n\n## Related Skills\n\n- **ad-creative** — WHEN you need deep creative direction for ad visuals, video scripts, or creative concepting beyond basic image\u002Fcopy guidelines. NOT for campaign strategy, targeting, or bidding decisions.\n- **analytics-tracking** — WHEN setting up conversion tracking pixels, UTM parameters, and attribution models before or during campaign launch. NOT for campaign creation or creative work.\n- **campaign-analytics** — WHEN analyzing campaign performance data, diagnosing underperforming campaigns, or building reporting dashboards. NOT for initial campaign setup or creative production.\n- **copywriting** — WHEN landing pages linked from ads need copy optimization to match ad messaging and improve post-click conversion. NOT for the ad copy itself.\n- **marketing-context** — Foundation skill for ICP, positioning, and messaging alignment. ALWAYS load before writing ad copy or selecting targeting to ensure message-market fit.\n\n---\n\n## Communication\n\nAlways confirm conversion tracking is in place before recommending creative or targeting changes — a campaign without proper attribution is guesswork. When recommending budget allocation, state the rationale (testing vs. scaling phase). Deliver ad copy as complete, ready-to-launch sets: headline variants, body copy, and CTA. Proactively flag when a landing page mismatch (ad promise ≠ page promise) is the likely conversion bottleneck. Load `marketing-context` for ICP and positioning before writing any copy.\n\n---\n\n## Proactive Triggers\n\n- User asks why ROAS is dropping → check creative fatigue and ad frequency before adjusting targeting or bids.\n- User wants to launch their first paid campaign → run through the pre-launch checklist (conversion tracking, landing page speed, UTMs) before touching creative.\n- User mentions high CTR but low conversions → diagnose landing page, not the ad; redirect to `page-cro` or `copywriting` skill.\n- User is scaling budget aggressively → warn about algorithm learning phase disruption; recommend 20-30% incremental increases with 3-5 day stabilization windows.\n- User asks about B2B lead generation via ads → recommend LinkedIn for job-title targeting and flag that CPL will be higher but lead quality better than Meta for high-ACV products.\n\n---\n\n## Output Artifacts\n\n| Artifact | Description |\n|----------|-------------|\n| Campaign Architecture | Full account structure with campaign names, ad set targeting, naming conventions, and budget allocation |\n| Ad Copy Set | 3 headline variants, body copy, and CTA for each ad format and platform, ready to launch |\n| Audience Targeting Brief | Primary audiences, lookalike seeds, retargeting segments, and exclusion lists per platform |\n| Pre-Launch Checklist | Platform-specific tracking verification, landing page audit, and UTM parameter setup |\n| Weekly Optimization Report Template | Metrics dashboard structure with CPA\u002FROAS targets, fatigue signals, and decision triggers |\n",{"name":5,"license":14,"metadata":48,"description":7},{"author":49,"updated":50,"version":51,"category":35},"Alireza Rezvani",1772755200000,"1.0.0","main","510dd2917e051cc953dc55f56fd6ef442aa01b38","claude-skills\u002Fmarketing-skill\u002Fskills\u002Fpaid-ads","https:\u002F\u002Fgithub.com\u002Fmne20114-hash\u002Fgest35bi","https:\u002F\u002Fgithub.com\u002Fmne20114-hash","2026-10-28 07:50:31",{"source":8,"skillName":5,"commands":59,"prompt":69,"zipAvailable":70},[60,63,66],{"tool":61,"command":62},"npx","npx skills add mne20114-hash\u002Fgest35bi --skill paid-ads",{"tool":64,"command":65},"bunx","bunx skills add mne20114-hash\u002Fgest35bi --skill paid-ads",{"tool":67,"command":68},"pnpm","pnpm dlx skills add mne20114-hash\u002Fgest35bi --skill paid-ads","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fmne20114-hash\u002Fgest35bi\u002Fblob\u002Fmain\u002Fclaude-skills\u002Fmarketing-skill\u002Fskills\u002Fpaid-ads\u002FSKILL.md",true,[72,78,82,86,90,94,100],{"path":73,"resourceType":74,"resourceTypeDesc":75,"sizeBytes":76,"sourceHtmlUrl":77},"references\u002Fad-copy-templates.md","REFERENCE","参考文档",4446,"https:\u002F\u002Fgithub.com\u002Fmne20114-hash\u002Fgest35bi\u002Fblob\u002Fmain\u002Fclaude-skills\u002Fmarketing-skill\u002Fskills\u002Fpaid-ads\u002Freferences\u002Fad-copy-templates.md",{"path":79,"resourceType":74,"resourceTypeDesc":75,"sizeBytes":80,"sourceHtmlUrl":81},"references\u002Faudience-targeting.md",5716,"https:\u002F\u002Fgithub.com\u002Fmne20114-hash\u002Fgest35bi\u002Fblob\u002Fmain\u002Fclaude-skills\u002Fmarketing-skill\u002Fskills\u002Fpaid-ads\u002Freferences\u002Faudience-targeting.md",{"path":83,"resourceType":74,"resourceTypeDesc":75,"sizeBytes":84,"sourceHtmlUrl":85},"references\u002Fcopy-frameworks.md",3759,"https:\u002F\u002Fgithub.com\u002Fmne20114-hash\u002Fgest35bi\u002Fblob\u002Fmain\u002Fclaude-skills\u002Fmarketing-skill\u002Fskills\u002Fpaid-ads\u002Freferences\u002Fcopy-frameworks.md",{"path":87,"resourceType":74,"resourceTypeDesc":75,"sizeBytes":88,"sourceHtmlUrl":89},"references\u002Fplatform-setup-checklists.md",6943,"https:\u002F\u002Fgithub.com\u002Fmne20114-hash\u002Fgest35bi\u002Fblob\u002Fmain\u002Fclaude-skills\u002Fmarketing-skill\u002Fskills\u002Fpaid-ads\u002Freferences\u002Fplatform-setup-checklists.md",{"path":91,"resourceType":74,"resourceTypeDesc":75,"sizeBytes":92,"sourceHtmlUrl":93},"references\u002Fscoring-system.md",3910,"https:\u002F\u002Fgithub.com\u002Fmne20114-hash\u002Fgest35bi\u002Fblob\u002Fmain\u002Fclaude-skills\u002Fmarketing-skill\u002Fskills\u002Fpaid-ads\u002Freferences\u002Fscoring-system.md",{"path":95,"resourceType":96,"resourceTypeDesc":97,"sizeBytes":98,"sourceHtmlUrl":99},"scripts\u002Fad_health_scorer.py","SCRIPT","脚本",11626,"https:\u002F\u002Fgithub.com\u002Fmne20114-hash\u002Fgest35bi\u002Fblob\u002Fmain\u002Fclaude-skills\u002Fmarketing-skill\u002Fskills\u002Fpaid-ads\u002Fscripts\u002Fad_health_scorer.py",{"path":101,"resourceType":96,"resourceTypeDesc":97,"sizeBytes":102,"sourceHtmlUrl":103},"scripts\u002Froas_calculator.py",11008,"https:\u002F\u002Fgithub.com\u002Fmne20114-hash\u002Fgest35bi\u002Fblob\u002Fmain\u002Fclaude-skills\u002Fmarketing-skill\u002Fskills\u002Fpaid-ads\u002Fscripts\u002Froas_calculator.py",[105,139,159,181,205,225],{"skillNo":106,"name":107,"nameZh":6,"description":108,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":109,"categories":110,"keywords":120,"publishedAt":43},"01M6C27N3H0F9RGG2KASPASH04","syllabus","Generates a curated supplementary reading list from any course syllabus using Consensus academic search. Grill-me intake (syllabus input format + course audience + year range) plus a grouping forcing-options checkpoint before any search runs — so the reading list matches the course's level and recency need. Parses the syllabus to extract topics and learning outcomes, searches Consensus for recent peer-reviewed papers per topic, and produces a professionally formatted .docx with clickable Consensus links, plain-language summaries calibrated to audience level, and Bloom-higher-order discussion questions tied to course learning goals. Use when the user uploads a syllabus, course outline, or curriculum document and wants supplementary readings (e.g., 'create a reading list from this syllabus', 'find recent papers for my course') — even casual mentions with a syllabus attached should trigger this skill.","https:\u002F\u002Fgithub.com\u002Fmne20114-hash\u002Fgest35bi\u002Fblob\u002Fmain\u002Fclaude-skills\u002Fresearch\u002Fsyllabus\u002Fskills\u002Fsyllabus\u002FSKILL.md",[111,114,117],{"categoryNo":112,"categoryName":113,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000002","文档处理",{"categoryNo":115,"categoryName":116,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000023","Skill与智能体开发",{"categoryNo":118,"categoryName":119,"taxonomyType":25,"taxonomyTypeDesc":26},"01JBSEEDCAT000000000000034","行政与办公人员",[107,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,42,138],"docx","pr","create","intake","search","audit","integrity","checkpoint","learning","js","format","direct","Rules","find","consensus","list","extract","outcomes",{"skillNo":140,"name":141,"nameZh":6,"description":142,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":143,"categories":144,"keywords":149,"publishedAt":43},"01M6C27DMGZ4D6ERNHSVA8H8RJ","pulse","Multi-source recency research skill that takes the pulse of any topic across Reddit, Hacker News, the open web, and optionally X\u002FTwitter within a configurable recent window (default 30 days). Forcing intake clarifies topic specificity, angle (trend\u002Fsentiment\u002Fproblems\u002Fopportunities\u002Fcomparison), time window, and platform scope before searching. Returns a synthesized briefing with citations, engagement metrics, and cross-platform pattern analysis. Use when the user requests multi-source recency intelligence on a topic (e.g., 'pulse on [topic]', 'what's happening with [topic]', 'what are people saying about [topic]', 'current conversation about [topic]', 'take the pulse of [topic]', 'trending: [topic]', 'find me info on [topic]'), and for competitor research, trend discovery, tool comparisons, and audience sentiment analysis.","https:\u002F\u002Fgithub.com\u002Fmne20114-hash\u002Fgest35bi\u002Fblob\u002Fmain\u002Fclaude-skills\u002Fresearch\u002Fpulse\u002Fskills\u002Fpulse\u002FSKILL.md",[145,148],{"categoryNo":146,"categoryName":147,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000021","资料检索与研究",{"categoryNo":115,"categoryName":116,"taxonomyType":20,"taxonomyTypeDesc":21},[141,150,151,122,152,124,126,153,154,127,155,156,157,158,132,133,37,134,41,42],"research","web","sentiment analysis","sentiment","Citations","analysis","discovery","open","tool",{"skillNo":160,"name":161,"nameZh":6,"description":162,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":163,"categories":164,"keywords":170,"publishedAt":43},"01M6C279M59X635VPD6Y9R616J","patent","Patent prior-art and landscape intelligence skill — not generic patent help. Commits to one of five sub-use-cases via forcing intake (novelty search \u002F freedom-to-operate \u002F competitive landscape \u002F acquisition diligence \u002F litigation prior-art) before any search runs. Searches Google Patents, Espacenet, USPTO, and optionally Lens.org for citation-graph signals. Output is an editable Word document (.docx) with verdict, ranked closest art (claim-text extracted), CPC-class-aware landscape, family-resolved hits, geographic coverage, FTO flags where applicable, strategy recommendations, and full audit log. Use when the user asks for patent searching or analysis (e.g., 'prior art search for [invention]', 'freedom to operate analysis for [product]'). Produces search signal, not legal advice — always recommends consulting a patent attorney before filing or licensing decisions. Trademark, copyright, and trade-secret questions are out of scope.","https:\u002F\u002Fgithub.com\u002Fmne20114-hash\u002Fgest35bi\u002Fblob\u002Fmain\u002Fclaude-skills\u002Fresearch\u002Fpatent\u002Fskills\u002Fpatent\u002FSKILL.md",[165,166,167],{"categoryNo":112,"categoryName":113,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":115,"categoryName":116,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":168,"categoryName":169,"taxonomyType":25,"taxonomyTypeDesc":26},"01JBSEEDCAT000000000000037","律师与法务",[161,121,122,171,172,173,124,125,126,155,174,175,176,133,177,178,40,179,42,180],"competitive landscape","routing","product","commits","Word","text","acquisition","decisions","coverage","advice",{"skillNo":182,"name":183,"nameZh":6,"description":184,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":185,"categories":186,"keywords":191,"publishedAt":43},"01M6C275RZWNRB3CCB14QQM7ES","notebooklm","Browser automation skill for controlling Google's NotebookLM. Use when the user wants anything done in NotebookLM (e.g., 'open NotebookLM', 'check my [name] notebook', 'ask my notebook about X', 'add [source] to NotebookLM', 'generate a Video Overview from my notebook', 'use NotebookLM Studio'). Handles reading and querying notebooks, adding sources (URLs, text, files, YouTube links, synthesized content), generating Studio outputs (Audio\u002FVideo Overviews, Mind Maps, Reports incl. Briefing Doc\u002FStudy Guide\u002FFAQ, Flashcards, Quiz, slide decks, infographics — discover the exact set from the live Studio panel; the UI evolves fast), and creating new notebooks. Requires browser automation environment — fails gracefully when unavailable.","https:\u002F\u002Fgithub.com\u002Fmne20114-hash\u002Fgest35bi\u002Fblob\u002Fmain\u002Fclaude-skills\u002Fresearch\u002Fnotebooklm\u002Fskills\u002Fnotebooklm\u002FSKILL.md",[187,190],{"categoryNo":188,"categoryName":189,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000016","图像与多媒体",{"categoryNo":115,"categoryName":116,"taxonomyType":20,"taxonomyTypeDesc":21},[183,30,192,150,193,194,195,196,197,198,199,200,157,176,132,201,202,203,204,42],"browser automation","automation","generate","browser","fast","SET","doc","Video","Audio","check","ask","environment","overview",{"skillNo":206,"name":207,"nameZh":6,"description":208,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":209,"categories":210,"keywords":215,"publishedAt":43},"01M6C2717B0QDJ3Y4PJP77BMW5","litreview","Academic literature orientation skill that searches papers via free keyless APIs (PubMed E-utilities + OpenAlex) by default — with the Consensus MCP as an optional enhancement lane when connected — builds a strategic search plan using PICO (default) or SPIDER \u002F Decomposition \u002F hybrid as fallbacks, and synthesizes findings into a formatted Word (.docx) research guide. Grill-me intake (research question specificity + framework hint + tentative depth) before the recon search; a second forcing checkpoint after Phase 2 confirms framework + sub-areas + depth before searches consume budget. Configurable depth (5\u002F10\u002F20 queries) controls coverage vs. speed. Output is a 'launching pad' — an orientation guide that lets a researcher dive in confidently, not a finished review. Use when the user starts literature-oriented research (e.g., 'litreview on [topic]', 'literature review on [topic]', 'I'm starting a literature review on X', 'I'm writing a paper on X', 'help me research X', 'I'm doing research on X', 'can you help me research X'). Do NOT use for single one-off paper searches wanting a quick list — that's a plain PubMed\u002FOpenAlex (or Consensus) query.","https:\u002F\u002Fgithub.com\u002Fmne20114-hash\u002Fgest35bi\u002Fblob\u002Fmain\u002Fclaude-skills\u002Fresearch\u002Flitreview\u002Fskills\u002Flitreview\u002FSKILL.md",[211,212,213,214],{"categoryNo":112,"categoryName":113,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":146,"categoryName":147,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":115,"categoryName":116,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":118,"categoryName":119,"taxonomyType":25,"taxonomyTypeDesc":26},[207,121,216,217,218,122,124,125,219,220,221,128,222,175,223,133,135,224,179,42],"MCP","writing","review","lane","keyless","literature review","plan","researcher","question",{"skillNo":226,"name":227,"nameZh":6,"description":228,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":229,"categories":230,"keywords":235,"publishedAt":43},"01M6C26X5G3WDVWQ40Y16ZS19F","grants","NIH grant research skill for clinical researchers. Grill-me intake (research idea + career stage + preliminary data + environment + submission posture + known institute targets) locks down the funding strategy before any search runs. Runs a 5-facet Consensus positioning analysis (with draft Significance\u002FInnovation language), maps the research to the right NIH institutes and study sections via RePORTER, finds NOSIs and funded overlap, and produces an editable Word document (.docx) with budget\u002Fscope-aware mechanism recommendations, submission timelines, and a mandatory program officer recommendation. Use when the user asks about research funding or makes any grant-related request (e.g., 'grants for [topic]', 'find grants for my research idea', 'what grants match my research', 'help me find NIH funding', 'grant opportunities for my research'). NIH-only scope — non-NIH funders (PCORI, DOD CDMRP, VA, foundations) are out of scope and flagged at intake.","https:\u002F\u002Fgithub.com\u002Fmne20114-hash\u002Fgest35bi\u002Fblob\u002Fmain\u002Fclaude-skills\u002Fresearch\u002Fgrants\u002Fskills\u002Fgrants\u002FSKILL.md",[231,232,233,234],{"categoryNo":112,"categoryName":113,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":146,"categoryName":147,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":115,"categoryName":116,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":118,"categoryName":119,"taxonomyType":25,"taxonomyTypeDesc":26},[227,121,150,122,31,124,125,126,236,237,127,155,175,133,134,135,40,203,42,238],"data","Budget","innovation"]