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google-ads

作者 Kostja Zhang · GitHub ↗ · v1.4.1 · MIT-0
cross-platform ✓ 安全检测通过
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在 OpenClaw 中安装
/install google-ads-paid-ads
功能描述
When the user wants to set up, optimize, or manage Google Ads campaigns. Also use when the user mentions "Google Ads," "Google Search Ads," "PPC," "SEM," "PM...
使用说明 (SKILL.md)

Paid Ads: Google Ads

Guides Google Ads setup, campaign structure, keyword targeting, and optimization. Google Ads excels at high-intent search traffic; use when people actively search for your solution.

When invoking: On first use, if helpful, open with 1–2 sentences on what this skill covers and why it matters, then provide the main output. On subsequent use or when the user asks to skip, go directly to the main output.

Two Modes: PMF Testing vs Conversion-Driven

Mode When Budget Landing page Metrics
PMF testing Pre-PMF; validate idea before building $47–500; start small Simple LP: headline, benefits, problem solved, CTA ("Join Waitlist," "Get Early Access") CTR, sign-up rate, bounce rate; low CTR/high bounce = messaging/positioning issue
Conversion-driven PMF validated; commercialization Scale; ROAS target Full funnel; ad-to-page alignment ROAS, CAC, conversion rate

PMF testing: No full product needed. Build landing page with Unbounce, Carrd, or Webflow. Run ads to relevant search terms; measure clicks, engagement, signups. Test messaging (e.g., "Fastest App for Freelancers" vs "Simplest Time Tracker for Teams"), pricing (different price points in ads/LP), and audiences (keyword targeting, in-market). Allow 4–6 weeks for PMax learning phase. Use as learning tool, not just marketing channel.

Reference: Marketing Cactus – Using Google Ads to Test Product-Market Fit

Campaign Structure

Account
├── Campaign: Brand (Search)
├── Campaign: Non-Brand (Search)
├── Campaign: Competitor (Search) — optional; bid on competitor brand + "alternative"/"vs"
├── Campaign: Retargeting (Display)
└── Campaign: Performance Max

Competitor Brand Keywords

When: Bid on "[Competitor] alternative," "[Competitor] vs [You]" to intercept high-intent traffic. Google allows competitor terms as keywords; you cannot use competitor names in ad copy without permission.

Landing page: Use a dedicated landing page (comparison/alternatives page), not a blog article. Users searching competitor brands expect direct alternatives—a blog increases bounce; a comparison page matches intent and converts better. See alternatives-page-generator for structure.

Best practices:

  • Separate campaign; exact/phrase match; add your brand as negative
  • H1 mirrors search intent (e.g., "[Competitor] vs [You]")
  • Feature comparison table; one-line differentiator; strong CTA
  • Expect lower Quality Score, higher CPC than non-brand; optimize LP relevance

Naming: GOOG_[Objective]_[Audience]_[Offer]_[Date] (e.g., GOOG_Search_Brand_Demo_Ongoing)

Campaign Types

Type Best for
Search High-intent queries; keyword-targeted; landing page critical
Display Awareness; retargeting; broader reach
YouTube Video; awareness; consideration
Performance Max Automated; cross-channel; feed + search + display

Performance Max (PMax) Optimization

Learning period: Run at least 6 weeks for algorithm ramp-up. Works best as complement to Search, not replacement.

Asset groups: Organize by audience intent (e.g., high-intent searchers, cart abandoners, category researchers), not product category alone. Audience signals improve CPA and ROAS vs. no signals.

Asset requirements (per asset group):

  • ≥5 images (include 1200×1200)
  • ≥5 text assets (4 headlines, 5 descriptions)
  • Video when possible
  • Refresh creative regularly to maintain performance

Signals: Add remarketing lists and Customer Match to accelerate learning.

Weekly health check: Flag if brand terms >30% of conversions; unexpected geo conversions; any placement >15% of total spend; asset group performance below "Good."

Keyword Strategy

  • Brand: Protect brand terms; exclude from non-brand campaigns
  • Negative keywords: Build weekly; avoid irrelevant queries. Add support terms (login, forum, pricing, help) from keyword-research—these are existing customers, not prospects.
  • Match types: Broad (discovery) → Phrase → Exact (control)

Keyword sources: Use keyword-research for keyword list, clusters, and intent. Map each cluster to a dedicated landing page; relevance improves Quality Score and lowers CPC.

Quality Score Levers

Factor Action
Expected CTR Improve ad relevance; test headlines
Ad relevance Align ad copy to keyword intent
Landing page Ad-to-page alignment; fast load; mobile-friendly

Target: Quality Score ≥6; higher = lower CPC, better ad rank. Benchmark: Improving Quality Score from 5 to 7 can reduce CPC by 30–50%.

Bidding Strategy

Conversions/month Strategy
<30 Manual CPC (smart bidding needs volume to optimize)
30–50 Target CPA; minimum for effective smart bidding
50–100 Target CPA
100+ Target ROAS

Smart bidding: AI-powered bidding (Target CPA, Target ROAS) typically delivers better ROI than manual when conversion volume is sufficient; requires ≥30 conversions in 30 days to work effectively.

Tracking

  • Enhanced Conversions: Server-side signals for better attribution
  • Offline conversion imports: B2B; CRM → Google Ads
  • UTM: Consistent parameters for GA4 cross-check

Paid–Organic Cannibalization

When you rank organically (position 4+) for a keyword and also run PPC, paid ads can absorb clicks that would go to organic. Audit: Cross-reference GSC organic rankings with Search Terms report. If organic ranks well, test pausing PPC on those terms to free budget for higher-impact keywords.

Reference: Backlinko – SEO and PPC: 8 Smart Ways to Align

Pre-Launch Checklist

  • Conversion tracking tested with real conversion
  • Landing page loads <3s; mobile-friendly
  • UTM parameters working
  • Negative keyword list built (include support terms from keyword-research)
  • Budget set; targeting matches audience

Related Skills

  • pmf-strategy: PMF validation framework; when to use PMF testing vs conversion-driven
  • paid-ads-strategy: Channel selection; budget allocation; ad-to-page alignment; competitor brand bidding
  • alternatives-page-generator: Competitor brand keyword ads → dedicated LP (not blog); comparison page structure
  • keyword-research: Keyword list, clusters, intent; support terms for negative keywords; PPC data feeds back SEO priority
  • traffic-analysis: UTM for attribution; paid–organic cannibalization audit
  • landing-page-generator: LP structure for paid traffic; PAA → FAQ
  • analytics-tracking: Conversion tracking; ROAS measurement
安全使用建议
This is an instruction-only guide for managing Google Ads and is internally consistent. Before using: (1) be aware some recommended tactics (Customer Match, enhanced/server-side conversions) require uploading customer data to Google Ads—ensure you have legal/consent authority and follow your privacy policy; (2) the skill does not request API keys or credentials, so never paste your Google Ads credentials into messages for the skill; (3) treat the guidance as best-practice advice and cross-check critical technical steps with Google Ads' official docs or your ad/account admin; (4) if the skill suggests using other named helper skills (e.g., keyword-research), verify those skills separately for safety and data handling.
功能分析
Type: OpenClaw Skill Name: google-ads-paid-ads Version: 1.4.1 The skill bundle contains marketing guidelines and strategic instructions for managing Google Ads campaigns. There is no executable code, no evidence of data exfiltration, and no malicious prompt injection; the content is entirely focused on campaign optimization and product-market fit testing (SKILL.md).
能力评估
Purpose & Capability
Name and description (Google Ads campaign setup/optimization) match the SKILL.md content. No unrelated env vars, binaries, or installs are required.
Instruction Scope
SKILL.md contains best-practice guidance (campaign structure, bidding, PMax, tracking). It does not instruct the agent to read local files, access system configs, or exfiltrate secrets. It does reference actions the user may take in Google Ads (e.g., Customer Match, server-side enhanced conversions) which involve uploading user data to Google Ads—this is expected for the domain but is a privacy consideration rather than a technical inconsistency.
Install Mechanism
No install specification, no code files, and therefore nothing is written to disk or downloaded. This lowers installation risk.
Credentials
The skill declares no required environment variables or credentials. The references to uploading customer lists and server-side signals are appropriate for Google Ads workflows but involve user data handling — the skill itself does not request secrets or tokens.
Persistence & Privilege
always:false and normal agent invocation settings. The skill does not request permanent/system-wide privileges or to modify other skills' configs.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install google-ads-paid-ads
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /google-ads-paid-ads 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.4.1
Batch: ctv through google-ads
v1.4.0
Automated batch sync
元数据
Slug google-ads-paid-ads
版本 1.4.1
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 2
常见问题

google-ads 是什么?

When the user wants to set up, optimize, or manage Google Ads campaigns. Also use when the user mentions "Google Ads," "Google Search Ads," "PPC," "SEM," "PM... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 242 次。

如何安装 google-ads?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install google-ads-paid-ads」即可一键安装,无需额外配置。

google-ads 是免费的吗?

是的,google-ads 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

google-ads 支持哪些平台?

google-ads 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 google-ads?

由 Kostja Zhang(@kostja94)开发并维护,当前版本 v1.4.1。

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