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.
npx skills add vaquarkhan/data-engineering-agent-skills --skill bigquery-and-dataform-platform-engineeringgit clone --depth 1 https://github.com/vaquarkhan/data-engineering-agent-skillsWrote 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.
[](https://agentmods.dev/skills/vaquarkhan/data-engineering-agent-skills/bigquery-and-dataform-platform-engineering)<a href="https://agentmods.dev/skills/vaquarkhan/data-engineering-agent-skills/bigquery-and-dataform-platform-engineering"><img src="https://agentmods.dev/badge/skills/vaquarkhan/data-engineering-agent-skills/bigquery-and-dataform-platform-engineering.svg" alt="Measured on agentmods" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00066 | $0.00716 |
| Opus 5 | $0.00033 | $0.00358 |
| Sonnet 5 | $0.00013 | $0.00143 |
| Haiku 4.5 | $0.00007 | $0.00072 |
Grade A, and why
bigquery-and-dataform-platform-engineering 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 8d 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.
How it starts
The opening of the file, as written. The whole thing — 83 lines — stays where its author put it; the contents beside it link to each section on GitHub.
BigQuery And Dataform Platform Engineering
Overview
Use this skill when BigQuery is the center of gravity for analytics engineering and data delivery on GCP. It helps agents decide what should run in BigQuery, what belongs in Dataform, and when the workflow should move to Dataflow, Dataproc, or external orchestration.
When to Use
- designing or reviewing
BigQueryphysical models - deciding between
Dataform,dbt,Dataflow, orDataprocresponsibilities - tuning partitioning, clustering, slots, and cost behavior
- defining ingestion and transformation boundaries on
GCP - building platform-native analytics workflows around
BigQuery
Do not treat BigQuery as a universal default for every preprocessing and orchestration need.
Workflow
-
Define the workload boundary. Clarify:
- landing pattern
- transformation complexity
- latency requirements
- governance and regional constraints
- cost sensitivity
-
Design
BigQueryphysical layout intentionally. Cover:- partitioning
- clustering
- dataset boundaries
- publish layers
- data retention and serving expectations
-
Choose the transformation surface. Consider:
Dataformfor warehouse-native SQL transformation workflowsdbtwhen the team already standardizes thereDatafloworDataprocwhen preprocessing or runtime requirements exceed warehouse-native fit
-
Define orchestration and operations. Include:
- where orchestration runs
- slot and concurrency behavior
- failure and rerun expectations
- validation gates before publish
-
Validate cost and governance behavior. Require:
- slot or query cost awareness
- service-account and secret controls
- policy tags or governance metadata where needed
- publish-readiness evidence
Common Rationalizations
| Rationalization | Reality |
|---|---|
| "Everything on GCP should run in BigQuery." | Some preprocessing, streaming, or protocol-heavy work belongs in Dataflow, Dataproc, or upstream services. |
| "Dataform is just a SQL wrapper." | It changes how transformation workflows, dependencies, testing, and deployment are managed. |
| "Partitioning and clustering can be tuned later." | Poor physical design often becomes a long-term cost and performance tax. |
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.
- 8d ago First seen · 83 lines · 66 tokens per session scan A 15a4f1a64cd4
bigquery-and-dataform-platform-engineering is a skill published in the GitHub repository vaquarkhan/data-engineering-agent-skills (43 stars, last pushed 2mo ago), licensed MIT. It adds 66 tokens to every session and 716 once invoked, about $0.0003 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.
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