bigquery-pipeline-audit

bigquery-pipeline-audit is a skill for Claude Code, Codex from boshi-xixixi/TraeSkill. It costs 35 tokens per session (1,226 once invoked), scanned A, original, MIT.

A review workflow for Python programs that move data through BigQuery, Google's cloud data warehouse. It examines spending risks, safe reruns, and whether failures are visible, then returns a structured report with precise fix locations.

In plain words
What is it for?
Use it to audit Python and BigQuery pipelines, identify expensive or unsafe operations, and suggest small targeted fixes.
Why use it?
It can reveal repeated queries, unbounded work, duplicate processing, and hidden failures before a data pipeline reaches production or creates unexpected costs.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: installed under .agents/ (shared by several agents).

Good fit Use it to audit Python and BigQuery pipelines, identify expensive or unsafe operations, and suggest small targeted fixes.

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Install with agentmods
npx agentmods add skills/boshi-xixixi/traeskill/bigquery-pipeline-audit
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add boshi-xixixi/TraeSkill --skill bigquery-pipeline-audit
Clone the repo
git clone --depth 1 https://github.com/boshi-xixixi/TraeSkill

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

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README.md
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Your own site · 80×15
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Per session 35 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,226 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00035 $0.01226
Opus 5 $0.00017 $0.00613
Sonnet 5 $0.00007 $0.00245
Haiku 4.5 $0.00003 $0.00123

Measured 10d ago against content hash 9dca6a922c1f, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

bigquery-pipeline-audit scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.trae/Skills/.agents/skills/bigquery-pipeline-audit/SKILL.md · 130 lines

How it starts

The opening of the file, as written. The whole thing — 130 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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 than once in a single run? Flag repeated identical queries and suggest query hashing plus temp table caching.

Flag immediately if:

  • Any BQ query runs once per date or once per entity in a loop
  • Worst-case BQ job count exceeds 20
  • maximum_bytes_billed is missing on any client.query call

B) DRY RUN AND EXECUTION MODES

Verify a --mode flag exists with at least dry_run and execute options.

  • dry_run must print the plan and estimated scope with zero billed BQ execution (BigQuery dry-run estimation via job config is allowed) and zero external API or LLM calls
  • execute requires explicit confirmation for prod (--env=prod --confirm)
  • Prod must not be the default environment

If missing, propose a minimal argparse patch with safe defaults.


C) BACKFILL AND LOOP DESIGN

Hard fail if: the script runs one BQ query per date or per entity in a loop.

Check that date-range backfills use one of:

  1. A single set-based query with GENERATE_DATE_ARRAY
  2. A staging table loaded with all dates then one join query
  3. Explicit chunks with a hard MAX_CHUNKS cap

Read the full file on GitHub · 130 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 10d ago First seen · 130 lines · 35 tokens per session scan A 9dca6a922c1f

Subscribe to this mod's changes

bigquery-pipeline-audit is a skill published in the GitHub repository boshi-xixixi/TraeSkill (262 stars, last pushed 4mo ago), licensed MIT. It adds 35 tokens to every session and 1,226 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.