Guide · Agent skill
How to use the BigQuery Pipeline Audit agent skill
Updated · written with AI from the project's own documentation and checked against our catalogue data
The BigQuery Pipeline Audit skill is a set of instructions that tells an AI agent to review a Python script that runs BigQuery jobs the way a senior data engineer would. It checks for runaway query costs, unsafe reruns, missing dry-run and prod safeguards, and weak logging, then returns a graded report. This guide shows you how to install the skill, ask for an audit in plain language, and read the results.
BigQuery Pipeline Auditgithub/awesome-copilot · MIT Download the skill Quick start
- 1Download the ZIP. Download the skill ZIP from this page and unzip it. It contains SKILL.md.
- 2Place the skill folder. Copy the bigquery-pipeline-audit folder into ~/.claude/skills/ for personal use, or into your project's .claude/skills/ folder.
- 3Or upload it in the apps. In the Claude apps, go to Settings → Capabilities → Skills and upload the skill instead.
- 4Make your pipeline readable. Ensure the agent can access the Python pipeline files, including helper modules and configuration.
- 5Ask for an audit. Describe the pipeline and your goal in plain language, such as checking cost safety before a production deploy.
- 6Review the report. Read the PASS or FAIL results per section and work through the patch list ordered by risk.
What the skill makes the agent do
The skill is a single instruction file, SKILL.md. It does not contain scripts and does not run anything itself. When you point an agent at your pipeline code, the agent reads the files and checks them against a fixed structure.
The review covers six areas, labelled A to F: cost exposure, dry-run and execution modes, backfill and loop design, query safety and scan size, safe writes and idempotency, and observability. It finishes with a PASS or FAIL per section, an ordered patch list, and, if it fails, the top three cost risks with a rough worst-case estimate.
- Flags BigQuery job triggers such as client.query, load, extract and copy jobs, and whether they sit inside loops or retry blocks.
- Checks whether maximum_bytes_billed is set on each client.query call.
- Looks for loops that run one query per date or per entity.
- Looks for plain INSERT or append writes with no deduplication logic.
- Checks that failures raise exceptions and that run_id is consistent across log lines.
- References exact function names and suggests minimal fixes rather than rewrites.
Install the skill from the ZIP
Download the ZIP for this skill from this page and unzip it. The archive contains SKILL.md. Place the unzipped bigquery-pipeline-audit folder in one of two locations, depending on how you want to use it.
For a personal setup available across your projects, put it in ~/.claude/skills/. For a single project, put it in that project's .claude/skills/ folder so it travels with the repository.
If you use the Claude apps instead, you can upload the skill under Settings → Capabilities → Skills rather than copying files.
- Personal: ~/.claude/skills/bigquery-pipeline-audit/
- Project only: .claude/skills/bigquery-pipeline-audit/ inside your repository
- Claude apps: Settings → Capabilities → Skills
Trigger the skill with a natural request
You do not need a special command. Describe what you want in plain language and mention the pipeline, so the agent knows which code to read. Naming BigQuery and the cost or rerun concern usually makes the match clear.
Good requests name the files or functions and state the goal. For example, you can ask the agent to check whether a backfill runs one query per day, or to review whether the pipeline is safe to rerun after a crash.
- Audit this Python BigQuery pipeline for cost safety and rerun safety before we deploy to prod.
- Review pipeline/backfill.py for queries that run once per date.
- I inherited this pipeline. Estimate where the BigQuery billing exposure is.
- Check whether the writes in load_daily_scores could create duplicate rows on rerun.
Read the report
The report follows the skill's A to F structure. Each section gets a PASS or FAIL with reasons that cite functions and line locations. Treat the patch list as your to-do list, ordered by risk.
The cost estimates are reasoning from the code, not measurements. The skill reads code and does not run queries, so a worst-case figure such as 90 dates times 3 retries equalling 270 jobs shows the shape of the risk, not the bill.
- PASS or FAIL per section A to F
- Patch list ordered by risk, with the functions to change
- If FAIL: top three cost risks with a rough worst-case estimate
Safety notes: scripts and network
The skill itself runs no scripts and uses no network access, according to its listing. It works by reading the repository files the agent can access, so the agent needs permission to read your pipeline code.
The audit is limited in scope. It focuses on cost and data correctness, not access control. It does not replace a formal security or compliance review, and it should not be used on non-BigQuery warehouses because its checks are BigQuery-specific.
- Runs scripts: no
- Uses network: no
- Requires an agent that can read repository files and a Python + BigQuery codebase
- Does not check IAM, access control or compliance
Tips for better audits
Give the agent the entry point and any scripts it calls, since it can only judge code it can see. If your pipeline is split across modules, say so in the request.
Mention your intended mode and environment. The skill checks whether a --mode flag with dry_run and execute options exists, whether execute needs --env=prod --confirm, and whether prod is the default.
- Point the agent at the whole pipeline, including helper modules and config.
- Ask for the patch list first, then apply the fixes one at a time.
- Re-run the audit after fixes to confirm the section results change.
What people use it for
- Reviewing a daily BigQuery load script before it is promoted to production.
- Checking a backfill job for loops that issue one query per date and suggesting a set-based rewrite.
- Assessing whether a pipeline can be safely rerun after a mid-run crash without double-writing.
- Understanding the billing exposure of a pipeline inherited from another team.
- Adding a dry_run mode and prod confirmation to an existing script.
Frequently asked questions
Is the BigQuery Pipeline Audit skill free to use?
The skill is published under the MIT licence, as stated on this page. Check the licence terms for the original repository if you need the exact wording. Your agent platform may have its own costs, which are separate from the skill.
How do I install a Claude agent skill?
Download the ZIP, unzip it, and place the folder in ~/.claude/skills/ for personal use or in a project's .claude/skills/ folder. Alternatively, upload it in the Claude apps under Settings → Capabilities → Skills.
What does the skill need to run?
It needs an AI agent that can read your repository files, plus a Python and BigQuery codebase to audit. The skill itself contains no scripts and uses no network access.
Does the skill actually measure my BigQuery costs?
No. It reads your code and reasons about where costs could come from. Its estimates are rough worst-case figures, so use a real dry run or your BigQuery job history to measure actual spend.
Can I use it for a non-BigQuery warehouse like Snowflake?
No. The checks are BigQuery-specific, such as maximum_bytes_billed and BigQuery SQL features, so results for other warehouses would be unreliable.
Does this replace a security review?
No. The skill focuses on cost and data correctness, not access control or compliance. Use a formal review for those concerns.
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Our verdict
Use this skill if you are a data engineer or team lead who wants a structured, checklist-style review of a Python and BigQuery pipeline's query cost and rerun safety before production, or when you have inherited one and need to understand its billing exposure. Pick something else if your pipeline runs on a different warehouse, if you need measured costs rather than code-based estimates, or if you need a security or compliance review.
Download the skill