google-ads-pmax

GitHub

分析Google Ads Performance Max广告表现,通过意图污染和流量蚕食视角评估效果。支持MCP实时拉取或手动导出数据,对比搜索广告以识别品牌保护问题并优化资产组与排除策略。

skills/google-ads-pmax/SKILL.md TheMattBerman/google-ads-copilot

Trigger Scenarios

需要分析PMax广告活动性能 检查PMax是否蚕食品牌搜索流量 评估广告资产组质量 获取PMax转化与成本报告

Install

npx skills add TheMattBerman/google-ads-copilot --skill google-ads-pmax -g -y
More Options

Use without installing

npx skills use TheMattBerman/google-ads-copilot@google-ads-pmax

指定 Agent (Claude Code)

npx skills add TheMattBerman/google-ads-copilot --skill google-ads-pmax -a claude-code -g -y

安装 repo 全部 skill

npx skills add TheMattBerman/google-ads-copilot --all -g -y

预览 repo 内 skill

npx skills add TheMattBerman/google-ads-copilot --list

SKILL.md

Frontmatter
{
    "name": "google-ads-pmax",
    "description": "Analyze Performance Max through the lens of intent contamination, cannibalization, weak control surfaces, and what can still be learned despite PMax opacity. Pulls live data via MCP or works with manual exports."
}

Google Ads PMax

Read first:

  • google-ads/references/operator-thesis.md
  • google-ads/references/intent-map.md
  • google-ads/references/query-patterns.md
  • google-ads/references/structure-playbook.md
  • google-ads/references/benchmarks.md
  • google-ads/references/deliverable-templates.md

Read workspace if available:

  • workspace/ads/account.md
  • workspace/ads/goals.md
  • workspace/ads/intent-map.md
  • workspace/ads/findings.md
  • workspace/ads/change-log.md
  • workspace/ads/learnings.md

Data Acquisition

Connected Mode (MCP available)

Pull via the search tool on google-ads-mcp:

Primary: PMax campaign performance:

SELECT
  campaign.name,
  campaign.status,
  metrics.impressions,
  metrics.clicks,
  metrics.cost_micros,
  metrics.conversions,
  metrics.conversions_value,
  metrics.cost_per_conversion
FROM campaign
WHERE campaign.advertising_channel_type = 'PERFORMANCE_MAX'
  AND campaign.status = 'ENABLED'
  AND segments.date DURING LAST_30_DAYS

Primary: Asset group performance:

SELECT
  campaign.name,
  asset_group.name,
  asset_group.status,
  metrics.impressions,
  metrics.clicks,
  metrics.cost_micros,
  metrics.conversions
FROM asset_group
WHERE campaign.advertising_channel_type = 'PERFORMANCE_MAX'
  AND segments.date DURING LAST_30_DAYS

Supplementary: Compare with Search campaign performance (cannibalization check):

SELECT
  campaign.name,
  campaign.advertising_channel_type,
  metrics.impressions,
  metrics.clicks,
  metrics.cost_micros,
  metrics.conversions,
  metrics.cost_per_conversion
FROM campaign
WHERE campaign.status = 'ENABLED'
  AND segments.date DURING LAST_30_DAYS
ORDER BY metrics.cost_micros DESC

See data/gaql-recipes.md for additional queries.

Export Mode (no MCP)

Ask the user for:

  • PMax campaign metrics
  • Asset group list with performance
  • Search campaign performance (for comparison)
  • Any listing group or audience signal notes

Core Questions

  • Is PMax cannibalizing branded or clean high-intent traffic?
  • Is it helping discover net-new signal, or just absorbing existing demand?
  • What can we infer about intent quality despite limited visibility?
  • What should be protected outside PMax?
  • What should be fixed in feeds, exclusions, or surrounding structure?

Process

  1. Announce mode (connected/export).
  2. Pull PMax and Search campaign data.
  3. Compare PMax vs. Search on shared conversion actions.
  4. Look for branded cannibalization signals.
  5. Assess asset group quality.
  6. Attempt PMax query visibility recovery using Steps 5/5b from the shared retrieval ladder (data/search-term-retrieval.md).
  7. Separate direct evidence from inference. Query rows from PMax are useful, but they do not automatically carry the same term-level cost / CPA detail as classic Search reports.
  8. Identify what should be protected in dedicated Search campaigns.

Draft Output

PMax analysis typically does not produce its own draft type — its findings feed into:

  • Structure drafts (if PMax is cannibalizing, recommend brand exclusions or campaign restructure)
  • Negative drafts (if account-level negatives can help contain PMax)
  • Budget drafts (if PMax is absorbing budget that should go to proven Search campaigns)

When PMax-specific actions are needed, use the structure draft template and note the PMax context.

Always update workspace memory:

  • workspace/ads/findings.md — PMax observations (inference vs. direct evidence)
  • workspace/ads/learnings.md — what we learned about PMax behavior in this account

Output Format

Use operator summary, then add:

  • Cannibalization risks (with evidence level)
  • Useful signal PMax may still be surfacing
  • What should be protected in Search or other buckets
  • What is direct evidence vs inference

Rules

  • Treat PMax as useful but not self-explanatory.
  • Look for contamination before celebrating efficiency.
  • Protect clean branded and high-intent buckets when needed.
  • Be explicit about what is inference versus direct evidence.
  • If visibility is weak, say that clearly instead of pretending certainty.

Version History

  • 2c253ee Current 2026-07-25 07:33

Same Skill Collection

google-ads/SKILL.md
skills/google-ads-apply/SKILL.md
skills/google-ads-audit/SKILL.md
skills/google-ads-budget/SKILL.md
skills/google-ads-connect/SKILL.md
skills/google-ads-daily/SKILL.md
skills/google-ads-draft-summary/SKILL.md
skills/google-ads-intent-map/SKILL.md
skills/google-ads-landing-review/SKILL.md
skills/google-ads-negatives/SKILL.md
skills/google-ads-plan/SKILL.md
skills/google-ads-rsas/SKILL.md
skills/google-ads-search-terms/SKILL.md
skills/google-ads-structure/SKILL.md
skills/google-ads-tracking/SKILL.md

Metadata

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Version
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Hash
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Indexed
2026-07-25 07:33

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