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Agent Skill · 研究检索

paid-ads(付费广告)

paid-ads 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

paid-ads 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为只读;许可证信息还不明确,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 39,733。

总安装 1,371周安装 56GitHub Stars 103下载 439核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:paid-ads(付费广告)
来源仓库:https://github.com/borghei/claude-skills
仓库路径:skills/paid-ads
安装命令:
npx skills add https://github.com/borghei/claude-skills --skill paid-ads
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/borghei/claude-skills --skill paid-ads

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

paid-ads 提供全平台付费广告策略,覆盖 Google Ads、Meta、LinkedIn 等主流渠道。包含受众定位、预算分配、出价策略与 ROAS/CPA 优化方法论。支持跨平台归因分析与预上线检查清单,助力规模化投放。需结合具体行业与产品特性调整参数,警惕泛化建议带来的预算浪费。

paid-ads 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

来源仓库内路径:skills/paid-ads

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/borghei/claude-skills --skill paid-ads;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它标注为只读,先用只读任务试一次,确认行为符合预期再扩大使用范围。

常见问题

6 条

paid-ads 怎么安装?

在终端执行 npx skills add https://github.com/borghei/claude-skills --skill paid-ads。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

paid-ads 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

paid-ads 需要什么权限?

权限级别是只读。该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

paid-ads 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

paid-ads 用的人多吗?

公开数据是累计安装 1,371 次,近一周安装 56 次,下载 439 次,GitHub 103 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

paid-ads 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 第三方扫描已通过,常见的高风险行为没有命中。
  • 标注为只读权限,试用阶段不容易误改本地文件。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。
  • 只想先试试效果、不希望它改动本地文件的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
paid-ads

付费广告 · 本页

1,371103只读未明确通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

Paid Ads

Campaign strategy, audience targeting, budget optimization, and performance management across all major advertising platforms.


Table of Contents


Keywords

paid ads, PPC, pay-per-click, Google Ads, Meta Ads, Facebook Ads, Instagram Ads, LinkedIn Ads, Twitter Ads, TikTok Ads, paid media, ROAS, CPA, CPC, CPM, audience targeting, retargeting, remarketing, budget optimization, bid strategy, ad campaigns, conversion tracking, lookalike audiences, campaign structure, ad performance, paid search, paid social


Quick Start

Launch a Campaign

  1. Define campaign goal (leads, sales, traffic, awareness)
  2. Select platform based on audience and intent
  3. Set up conversion tracking and verify with test conversion
  4. Build campaign structure with proper naming conventions
  5. Define audience targeting
  6. Set budget and bid strategy
  7. Create ad creative (use Ad Creative skill)
  8. Launch and monitor for 7 days before making changes

Optimize an Existing Campaign

  1. Pull performance data for last 14-30 days
  2. Identify primary issue (high CPA, low CTR, low ROAS)
  3. Use the optimization levers in the Performance Optimization section
  4. Make one change at a time, wait 3-5 days between changes
  5. Document every change and its impact

Platform Selection Guide

Platform Comparison

PlatformBest ForAudience SignalTypical CPCMinimum Budget
Google SearchHigh-intent demand captureSearch keywords (what they want now)$1-8 (B2B: $5-20)$1,500/mo
Google DisplayAwareness, retargetingBrowsing behavior, interests$0.30-1.50$1,000/mo
Google Performance MaxMulti-format automationMixed signals, Google's MLVaries$2,000/mo
Meta (FB/IG)Demand generation, B2C, visual productsInterests, behaviors, lookalikes$0.50-3.00$1,000/mo
LinkedInB2B, decision-maker targetingJob title, company, industry, seniority$5-15$2,000/mo
Twitter/XTech audiences, thought leadershipFollowers, interests, keywords$0.50-3.00$500/mo
TikTok18-34 demographics, brand awarenessInterests, behaviors, creator affinity$0.30-1.50$1,000/mo
RedditNiche communities, tech/gamingSubreddit targeting$0.50-2.00$500/mo

Platform Selection Decision Tree

Is the audience actively searching for your solution?
├── Yes → Google Search Ads
└── No → Do you know their job title or company?
    ├── Yes → LinkedIn Ads (B2B) or Meta Ads (B2C)
    └── No → Is your product visual or lifestyle?
        ├── Yes → Meta Ads (Instagram) or TikTok
        └── No → Is your audience technical?
            ├── Yes → Reddit Ads or Twitter/X
            └── No → Meta Ads (Facebook) or Google Display

Campaign Structure Framework

Account Hierarchy

Account
├── Campaign 1: [Objective] - [Product/Offer]
│   ├── Ad Group/Set 1: [Audience Segment A]
│   │   ├── Ad 1: [Creative Variant 1]
│   │   ├── Ad 2: [Creative Variant 2]
│   │   └── Ad 3: [Creative Variant 3]
│   └── Ad Group/Set 2: [Audience Segment B]
│       ├── Ad 1: [Creative Variant 1]
│       └── Ad 2: [Creative Variant 2]
└── Campaign 2: [Objective] - [Product/Offer]

Naming Conventions

[Platform]_[Objective]_[Audience]_[Offer]_[Date]

Examples:
GOOG_Search_Brand_FreeTrial_2026Q1
META_Conv_Lookalike-Customers_Demo_Mar26
LI_LeadGen_CMOs-SaaS-500_Whitepaper_2026Q1
TIKTOK_Aware_18-34-Tech_BrandVideo_Mar26

Campaign Types by Objective

ObjectiveGoogleMetaLinkedIn
AwarenessDisplay, YouTube, PMaxReach, Video ViewsBrand Awareness
ConsiderationSearch, DisplayTraffic, EngagementWebsite Visits
ConversionSearch, PMaxConversions, LeadsLead Gen Forms
RetargetingDisplay, Search (RLSA)Custom AudiencesMatched Audiences

Audience Targeting by Platform

Google Ads Targeting

Targeting TypeUse WhenHow
Keyword targetingCapturing search intentExact, phrase, and broad match keywords
Audience targetingLayering intent signalsIn-market, affinity, custom intent
RLSARetargeting in searchWebsite visitor lists on search campaigns
Customer MatchTargeting known contactsUpload email lists for matched targeting
Similar audiencesExpanding from known customersGoogle's lookalike from customer lists

Keyword match type strategy:

  • Exact match [keyword]: Highest intent, lowest volume, highest CPC
  • Phrase match "keyword": Medium intent, medium volume
  • Broad match keyword: Lowest intent, highest volume, lowest CPC (use with smart bidding)

Meta Ads Targeting

Targeting TypeUse WhenHow
Interest targetingCold prospectingLayer 2-3 related interests
Lookalike audiencesExpanding from customers1-3% lookalike from best customers (by LTV)
Custom audiencesRetargetingWebsite visitors, email lists, video viewers
Broad targetingTrusting Meta's MLNo targeting restrictions, let the algorithm find converters
Detailed targetingNarrow audience neededCombine demographics + interests + behaviors

Lookalike best practices:

  • Seed with best customers (by LTV), not all customers
  • Start with 1% lookalike (most similar), expand to 3-5% once proven
  • Create separate lookalikes from different seeds (customers, trial users, email subscribers)

LinkedIn Ads Targeting

Targeting TypeUse WhenHow
Job titleTargeting decision-makersSpecific titles (CMO, VP Marketing, Head of Growth)
Job functionBroader role targetingMarketing, Engineering, Finance
Company sizeEnterprise vs. SMBEmployee count ranges
IndustryVertical-specific campaignsLinkedIn's industry categories
SeniorityC-suite vs. individual contributorManager, Director, VP, CXO
SkillsTechnical targetingListed skills on profiles
Company listABM targetingUpload target account lists

LinkedIn targeting rules:

  • Minimum audience size: 50,000 for awareness, 20,000 for conversion
  • Layer 2-3 targeting dimensions maximum (more layers = too narrow)
  • Exclude competitors, agencies, and job seekers if not relevant

Budget Allocation Strategy

Budget by Campaign Phase

Phase 1: Testing (Weeks 1-4)

AllocationPurpose
40%Proven/safe campaigns (brand search, retargeting)
40%Testing new audiences and creative
20%Experimental channels or formats

Phase 2: Optimization (Weeks 5-8)

AllocationPurpose
60%Winning combinations from testing
25%Iterating on promising but unproven
15%New tests

Phase 3: Scaling (Weeks 9+)

AllocationPurpose
70%Proven performers
20%Expansion (new audiences, lookalikes, broader targeting)
10%Ongoing testing

Budget Scaling Rules

  • Increase budget by 20-30% at a time, never more
  • Wait 3-5 days between increases for algorithm learning
  • Monitor CPA for 48 hours after increase — if CPA spikes, hold
  • Never double a budget overnight (disrupts algorithm learning)
  • If CPA increases > 30% after scaling, revert and investigate

Budget Minimums by Platform

PlatformMinimum Viable Monthly BudgetOptimal Monthly Budget
Google Search$1,500$5,000+
Google Display$1,000$3,000+
Meta Ads$1,000$3,000+
LinkedIn Ads$2,000$5,000+
TikTok Ads$1,000$3,000+
Reddit Ads$500$2,000+

Bid Strategy Progression

Strategy Ladder

StageStrategyWhen to UseRequirements
1Manual CPCStarting out, need controlNone
2Max ClicksBuilding traffic dataBudget cap set
3Target CPAOptimizing for conversions30+ conversions/month
4Target ROASOptimizing for revenue50+ conversions/month + revenue data
5Value-basedMaximizing revenueConversion value tracking, 100+ conversions/month

Bid Strategy Rules

  • Start with Manual CPC or Max Clicks until you have conversion data
  • Switch to automated bidding after 30+ conversions in 30 days
  • Set CPA targets 10-20% above your actual target (give the algorithm room)
  • Never change bid strategy and creative at the same time
  • Allow 14 days of learning phase after switching strategies

Retargeting Playbook

Funnel-Based Retargeting

Funnel StageAudienceMessageWindowFrequency
TopBlog readers, video viewersEducational, social proof30-90 days1-2x/week
MiddlePricing/feature page visitorsCase studies, demos, comparisons7-30 days3-5x/week
BottomCart/trial abandonersUrgency, objection handling, offer1-7 daysDaily OK

Retargeting Audience Setup

AudienceSourcePlatformPriority
All website visitors (30 days)PixelAll platformsMedium
Pricing page visitors (14 days)PixelAll platformsHigh
Cart/trial abandoners (7 days)Pixel + EventsAll platformsHighest
Email subscribers (non-customers)Email listMeta, LinkedInMedium
Video viewers (50%+ watched)Platform eventMeta, YouTubeMedium
Blog readers (engaged, 60s+)Pixel + EventsAll platformsLow-Medium

Exclusions (Critical)

Always exclude:

  • Existing paying customers (unless running upsell campaigns)
  • Recent converters (7-14 day exclusion window)
  • Bounced visitors (under 10 seconds on site)
  • Irrelevant page visitors (careers, support, legal)
  • Competitor employees (LinkedIn)

Performance Optimization

Optimization Decision Tree

Is CPA above target?
├── CTR is low (< 1% search, < 0.5% social)
│   ├── Creative fatigue? → Refresh creative
│   ├── Audience mismatch? → Refine targeting
│   └── Ad relevance low? → Improve message match
├── CTR is good, conversion rate low
│   ├── Landing page issue? → Audit page (speed, copy, CTA)
│   ├── Offer mismatch? → Align ad promise with page offer
│   └── Audience too broad? → Narrow targeting
└── CTR and CVR are good, CPA still high
    ├── CPM too high? → Try different placements/platforms
    ├── Competition driving up bids? → Adjust bid strategy
    └── Attribution issue? → Check conversion tracking

Key Metrics by Objective

ObjectivePrimary MetricsBenchmarks (B2B SaaS)
AwarenessCPM, Reach, Video View RateCPM: $5-15, VVR: 15-25%
ConsiderationCTR, CPC, Time on SiteCTR: 1-3%, CPC: $2-8
ConversionCPA, ROAS, Conversion RateCPA: $50-200, CR: 2-5%
RetargetingCPA, ROAS, FrequencyCPA: 30-50% lower than prospecting

Creative Fatigue Detection

SignalThresholdAction
CTR declining week over week20%+ decline over 2 weeksRefresh creative
Frequency above threshold> 3 (display), > 5 (retargeting)Expand audience or refresh
CPA increasing with stable CTR15%+ increase over 2 weeksTest new creative angles
Engagement rate dropping30%+ declineFull creative overhaul

Weekly Optimization Routine

TaskTimeWhat to Check
Budget pacing5 minSpend vs. plan, daily/weekly trends
CPA/ROAS check10 minPerformance vs. targets, by campaign
Top/bottom performers10 minPause worst, scale best
Audience analysis10 minWhich segments are converting?
Creative performance10 minCTR by creative, fatigue signals
Frequency check5 minAny audiences over-exposed?
Landing page CVR5 minPost-click conversion rate
Competitor check5 minNew competitors in auction?

Attribution and Measurement

Attribution Reality Check

What Platforms ReportReality
"This campaign drove 100 conversions"Platform attribution is inflated by 20-50%
"ROAS is 5x"Likely includes assisted conversions that would have converted anyway
Last-click attributionIgnores all touchpoints before the final click
View-through conversionsOften just people who would have converted regardless

Practical Attribution Approach

  1. Use UTM parameters consistently — Tag every campaign, ad, and link
  2. Track in GA4 as source of truth — Compare platform data to GA4
  3. Calculate blended CAC — Total marketing spend / Total new customers
  4. Use incrementality testing — Hold-out tests to measure true lift
  5. Compare platform data vs. CRM data — The gap is your attribution inflation

UTM Standards

utm_source: google | meta | linkedin | twitter | tiktok | reddit
utm_medium: cpc | paid-social | display | video | sponsored
utm_campaign: [campaign-name-lowercase-hyphenated]
utm_content: [ad-variant-identifier]
utm_term: [keyword] (search only)

Pre-Launch Checklist

Tracking

  • Pixel/tag installed and firing correctly
  • Conversion events defined and tested with real test conversion
  • UTM parameters added to all ad destination URLs
  • GA4 goals/events configured to match conversion events
  • Attribution window set appropriately (7 or 28 day)

Landing Page

  • Page loads under 3 seconds on mobile
  • Page is mobile-responsive
  • Headline matches the ad message
  • CTA is above the fold on mobile
  • Form works and submits to CRM/email system
  • Thank you page/event fires conversion tracking

Campaign Setup

  • Budget set correctly (daily or lifetime)
  • Bid strategy selected and configured
  • Audience targeting reviewed (not too broad or narrow)
  • Negative keywords added (Google Search)
  • Exclusions configured (existing customers, competitors)
  • Ad schedule set (if time-specific targeting needed)
  • Geographic targeting verified
  • Device targeting reviewed

Creative

  • 3+ creative variants per ad group/set
  • All creative meets platform specifications
  • Copy validated against platform policies
  • Landing page URL correct for each ad

Best Practices

  1. Tracking first, creative second — Never launch without verified conversion tracking. A campaign without attribution is guesswork.
  2. Start narrow, expand gradually — Begin with your highest-intent, most-defined audience. Expand after proving the funnel works.
  3. One change at a time — Changing audience, creative, and bid strategy simultaneously makes it impossible to know what worked.
  4. Give algorithms time — Do not judge campaign performance before the learning phase completes (typically 50 conversions or 7 days).
  5. Creative is the biggest lever — On most platforms, creative quality matters more than targeting precision. Test creative aggressively.
  6. Match ad to landing page — The #1 conversion killer is mismatched expectations between ad and landing page.
  7. Budget concentration beats distribution — $3,000 on one proven platform outperforms $500 spread across six platforms.
  8. Build retargeting from day one — Install pixels and build audiences even before you spend on retargeting.
  9. Compare platform data to reality — Platform-reported conversions are always higher than actual. Use CRM and GA4 as the source of truth.
  10. Document everything — Every campaign change, test result, and learning should be recorded. Institutional knowledge prevents repeating mistakes.

Integration Points

  • Ad Creative — Use for writing ad copy, generating headlines, and creating creative variations. Paid Ads handles the campaign strategy; Ad Creative handles the copy.
  • Landing Page Generator — Use for building the landing pages ads drive traffic to.
  • Campaign Analytics — Use for measuring campaign performance, attribution analysis, and ROI calculation.
  • Marketing Context — Use as foundation for audience targeting and messaging alignment.
  • Marketing Psychology — Apply psychological principles to improve ad creative and landing page conversion.
  • Copywriting — Use for optimizing landing page copy to improve post-click conversion rates.

Troubleshooting

SymptomLikely CauseFix
CPA above target with low CTRCreative fatigue or audience mismatchRefresh creative. Use ad_copy_scorer.py to validate new copy.
CPA above target with good CTRLanding page conversion issueAudit post-click experience: message match, page speed, form friction.
CTR dropping week over weekCreative fatigue (>3 frequency)Refresh creative every 2-4 weeks. Expand audience to reduce frequency.
Budget not spendingAudience too narrow or bid too lowCheck audience size with audience_sizer.py. Increase bid 10-20%.
Platform reports inflated conversionsAttribution window too wideCompare platform data to GA4/CRM. Use incrementality testing for true lift.
Performance Max underperformingInsufficient conversion dataNeed 30+ conversions in 30 days for PMax to optimize. Start with Search campaigns.
CPA spikes after budget increaseAlgorithm learning disruptedNever increase budget more than 20-30% at a time. Wait 3-5 days between changes.

Success Criteria

  • CPA within target range for campaign objective (B2B SaaS: $50-200 for qualified leads)
  • ROAS above 3x for revenue-focused campaigns
  • CTR above platform benchmarks: 2-5% search, 0.5-2% social
  • Conversion tracking verified with test conversion before launch
  • Budget allocation: 70% proven / 20% expansion / 10% testing (at scale)
  • All campaigns have proper UTM tagging and GA4 attribution configured
  • Weekly optimization routine completed with documented changes

Scope & Limitations

In Scope: Campaign strategy, platform selection, audience targeting, budget allocation, bid strategies, retargeting, performance optimization, attribution, pre-launch checklists.

Out of Scope: Ad copy writing (use ad-creative), landing page design (use landing-page-generator), creative design/production, marketing automation, CRM configuration.

Limitations: Budget minimums and CPC benchmarks are directional estimates. Actual costs vary by industry, geography, and competition. Platform-reported metrics are typically 20-50% inflated versus CRM truth.


Python Automation Tools

1. Ad Copy Scorer (scripts/ad_copy_scorer.py)

Scores ad copy against platform specs, compliance rules, and conversion best practices.

python scripts/ad_copy_scorer.py --headline "Cut churn by 30%" --description "See how 1200 SaaS teams reduced churn" --platform google
python scripts/ad_copy_scorer.py --file ads.json --json

2. CPC / CPA / ROAS Calculator (scripts/cpc_calculator.py)

Calculates key advertising metrics from campaign data with industry benchmarks.

python scripts/cpc_calculator.py --spend 5000 --clicks 1200 --conversions 45 --revenue 12000 --platform meta
python scripts/cpc_calculator.py --file campaign.json --json

3. Audience Sizer (scripts/audience_sizer.py)

Estimates target audience size and recommends budget based on platform and targeting criteria.

python scripts/audience_sizer.py --platform linkedin --targeting "CMOs at SaaS companies 50-500 employees"
python scripts/audience_sizer.py --file targeting.json --json

平台分布

Codex

36.57%
按下载量换算161

Claude

29.41%
按下载量换算129

Cursor

19.15%
按下载量换算84

Gemini CLI

9.91%
按下载量换算44

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

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