Pmax Performance Pack

C 61 completed
Other
unknown / sql · tiny
45
Files
2,645
LOC
0
Frameworks
4
Languages

Pipeline State

completed
Run ID
#949818
Phase
done
Progress
0%
Started
2026-04-15 08:22:11
Finished
2026-04-15 08:22:11
LLM tokens
0

Pipeline Metadata

Stage
Skipped
Decision
skip_scaffold_dup
Novelty
33.21
Framework unique
Isolation
Last stage change
2026-04-16 18:15:42
Deduplication group #47876
Member of a group with 57 similar repo(s) — canonical #186778 view group →
All rows above produced by Repobility · https://repobility.com

AI Prompt

Create a data pipeline project focused on analyzing Google Performance Max campaigns. I need to build out the logic using SQL queries, specifically for BigQuery. The goal is to track per-asset performance metrics, including clicks, impressions, cost, and conversions, segmented by date and ad network. Please ensure the architecture accounts for Google Ads API v23+ compatibility fixes and integrates with Looker Studio for visualization. I also need to structure the configuration using YAML files and include documentation in Markdown.
sql bigquery google-ads-api data-pipeline analytics pmax looker-studio yaml google-cloud
Generated by gemma4:latest

Catalog Information

Create a data pipeline project focused on analyzing Google Performance Max campaigns. I need to build out the logic using SQL queries, specifically for BigQuery. The goal is to track per-asset performance metrics, including clicks, impressions, cost, and conversions, segmented by date and ad network. Please ensure the architecture accounts for Google Ads API v23+ compatibility fixes and integrates with Looker Studio for visualization. I also need to structure the configuration using YAML files a

Tags

sql bigquery google-ads-api data-pipeline analytics pmax looker-studio yaml google-cloud

Quality Score

C
60.8/100
Structure
49
Code Quality
90
Documentation
70
Testing
0
Practices
70
Security
76
Dependencies
50

Strengths

  • Consistent naming conventions (snake_case)
  • Low average code complexity — well-structured code

Weaknesses

  • No LICENSE file — legal ambiguity for contributors
  • No tests found — high risk of regressions
  • No CI/CD configuration — manual testing and deployment
  • 228 duplicate lines detected — consider DRY refactoring

Recommendations

  • Add a test suite — start with critical path integration tests
  • Set up CI/CD (GitHub Actions recommended) to automate testing and deployment
  • Add a linter configuration to enforce code style consistency
  • Add a LICENSE file (MIT recommended for open source)

Languages

sql
59.6%
yaml
27.6%
markdown
12.8%
text
0.0%

Frameworks

None detected

Quality Timeline

1 quality score recorded.

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