Individual skill
google-ads-query
Query Google Ads data and save to CSV. AUTO-ACTIVATE for: search terms, campaigns, keywords, ad groups, conversions, budgets, assets, geo performance. Also triggered by account names or "get/pull/show me" + Google Ads resource.
google-ads-query/SKILL.md
Source commit 90f0e419f2d820789d5e50eee1237ab9fd14dfc6
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- Matches the observed repository HEAD
- Yes
- Scanner rules are current
- No
- Repository scan complete
- No
- Skill scan complete
- No
- Observed behavior
- Not checked yet
- Last checked
- Nov 11, 2026
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Repository context
- Repository stars (not skill ratings)
- 80
- Repository last updated
- Aug 25, 2026
- License
- MIT
Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.
- ai-agents Repository topic
- ai-skills Repository topic
- claude-code Repository topic
- google-ads Repository topic
- google-ads-api Repository topic
- ppc Repository topic
- ppc-automation Repository topic
- engineering Derived category
Files and excerpt
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Browse files
README.mddoc · 3,739 bytesSKILL.mdcore · 7,378 bytesSKILL.mdcore · 7,378 bytesSKILL.mdcore · 7,378 bytesaccounts.example.jsonasset · 949 bytesexamples.mddoc · 4,371 bytesreferences/ad-groups.gaqlasset · 522 bytesreferences/assets.gaqlasset · 539 bytesreferences/budgets.gaqlasset · 437 bytesreferences/campaigns.gaqlasset · 653 bytesreferences/conversions.gaqlasset · 478 bytesreferences/geo.gaqlasset · 489 bytesreferences/keywords.gaqlasset · 733 bytesreferences/query-contract.mddoc · 8,105 bytesreferences/resources.mddoc · 1,725 bytesreferences/search-terms.gaqlasset · 433 bytesrules.mddoc · 6,759 bytesscripts/query.pyruns · 10,852 bytes
Read excerpt
# Google Ads Query Skill
Owns one workflow: turn a natural-language data request into a template-driven
GAQL pull whose results land in a CSV file — returning only the file path and
row count to the conversation. Read-only — SELECT queries, never mutations.
## Purpose
This skill implements the **CSV-first pattern** for context-efficient analysis:
1. Query API data
2. Save to CSV file (data stays outside context)
3. Return only file path + row count
4. Analyze the CSV as a separate step, only when asked (read the file then, not before)