bigquery-pipeline-audit
Analyticsgithub/awesome-copilotskills.sh ↗
Installs
8,830
deduplicated, at the last sync
Since we started
+0.2%
16 readings, about 4 hours apart. Not a live curve.
Our category
Analytics
ours
Last read
Sep 3, 2026
from the directory
Our brief
oursThis skill audits Python + BigQuery pipelines to assess their cost safety, idempotency, and overall production readiness. By analyzing the codebase, it helps identify potential runaway costs before execution, ensures that data reruns do not corrupt existing data, and verifies adherence to best practices for robust pipeline design.
- Structured report with exact patch locations
- PASS / FAIL status with specific reasons per section
- Patch list ordered by risk
- Top 3 cost risks with worst-case estimates
- Python + BigQuery pipeline codebase
not detected
The evidence describes the audit criteria but does not mention any specific requirements for paid plans or APIs to perform the analysis.
not detected
The evidence provides no information regarding required user registration.
The skill analyzes code and suggests fixes, but it cannot execute the pipeline itself. It also cannot guarantee real-time runtime behavior or verify if suggested patches will resolve all issues in a live environment.
Evidenceskills/bigquery-pipeline-audit/SKILL.md:1-130
--- name: bigquery-pipeline-audit description: 'Audits Python + BigQuery pipelines for cost safety, idempotency, and production readiness. Returns a structured report with exact patch locations.' --- # BigQuery Pipeline Audit: Cost, Safety and Production Readiness You are a senior data engineer reviewing a Python + BigQuery pipeline script. Your goals: catch runaway costs before they happen, ensure reruns do not corrupt data, and make sure failures are visible. Analyze the codebase and respond in the structure below (A to F + Final). Reference exact function names and line locations. Suggest minimal fixes, not rewrites. --- ## A) COST EXPOSURE: What will actually get billed? Locate every BigQuery job trigger (`client.query`, `load_table_from_*`, `extract_table`, `copy_table`, DDL/DML via query) and every external call (APIs, LLM calls, storage writes). For each, answer: - Is this inside a loop, retry block, or async gather? - What is the realistic worst-case call count? - For each `client.query`, is `QueryJobConfig.maximum_bytes_billed` set? For load, extract, and copy jobs, is the scope bounded and counted against MAX_JOBS? - Is the same SQL and params being executed more
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Installfrom the directory
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What the auditors foundfrom the directory
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Installs, reading by readingours
Axis starts at 8.8k, not zero — the range is 8.8k to 8.8k.
Asked out loudspoken, not typed
The same skill in the words people use speaking to an assistant rather than typing into a box. Each one carries the situation it came from, and each answer says only what the skill's own files support.
The evidence describes the audit criteria but does not mention any specific requirements for paid plans or APIs to perform the analysis.
It suggests minimal fixes and provides a patch list, but it cannot execute the pipeline or apply suggested patches itself.