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gcp-data-engineering

gcp-data-engineering is a skill for Claude Code, Codex from jggomez/expert-ai-developer-skills. It costs 86 tokens per session (1,344 once invoked), scanned A, original, Apache-2.0.

Guides architecture decisions for data pipelines on Google Cloud — lake/warehouse/lakehouse design, batch vs. streaming, orchestration tool selection (Dataform, Cloud Composer/Airflow, Dataflow/Beam), and BigQuery cost/performance tuning (partitioning, clustering, slots). Use when designing a new pipeline, choosing a…

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: mentions Claude Code; mentions AGENTS.md.

not rated 30repo yesterday A scan 86 tokens original Apache-2.0
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Install with agentmods
npx agentmods add skills/jggomez/expert-ai-developer-skills/gcp-data-engineering
View source ↗ jggomez/expert-ai-developer-skills
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 jggomez/expert-ai-developer-skills --skill gcp-data-engineering
Clone the repo
git clone --depth 1 https://github.com/jggomez/expert-ai-developer-skills

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.

agentmods badge for gcp-data-engineering

README.md
[![agentmods](https://agentmods.dev/badge/skills/jggomez/expert-ai-developer-skills/gcp-data-engineering/github.svg)](https://agentmods.dev/skills/jggomez/expert-ai-developer-skills/gcp-data-engineering)
Your own site
<a href="https://agentmods.dev/skills/jggomez/expert-ai-developer-skills/gcp-data-engineering"><img src="https://agentmods.dev/badge/skills/jggomez/expert-ai-developer-skills/gcp-data-engineering/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for gcp-data-engineering

Your own site · 80×15
<a href="https://agentmods.dev/skills/jggomez/expert-ai-developer-skills/gcp-data-engineering"><img src="https://agentmods.dev/badge/skills/jggomez/expert-ai-developer-skills/gcp-data-engineering.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 86 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,344 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 $0.00086 $0.01344
Opus 5.5 $0.00034 $0.00538
Sonnet 5.5 $0.00017 $0.00269
Haiku 4.5 $0.00009 $0.00134

Measured today against content hash e1b03773b693, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

gcp-data-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 today.

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.

plugins/antigravity/senior-data-engineer/skills/gcp-data-engineering/SKILL.md · 65 lines

How it starts

The opening of the file, as written. The whole thing — 65 lines — stays where its author put it; the contents beside it link to each section on GitHub.

GCP Data Engineering Skill

Overview

Guides architectural decisions for data pipelines, Medallion lakehouses, and analytical storage on Google Cloud Platform (GCP). Balances data volume, SLA freshness, operational simplicity, and cloud cost—prioritizing warehouse-native ELT (Dataform, BigQuery) over custom streaming (Dataflow/Beam) unless sub-minute SLAs apply, while enforcing capability-gated artifacts and fail-honest CLI verification.

When to Use

Trigger Scenarios

  • Designing GCP lakehouse platforms (Cloud Storage, BigQuery, Datastream, Pub/Sub, Dataform, Composer).
  • Selecting batch vs. log-based CDC vs. streaming ingestion and Dataform vs. Composer vs. Dataflow orchestration.
  • Designing BigQuery partitioning, clustering, and Medallion layers (Bronze → Silver → Gold).

When NOT to Use

  • CDC & SCD Type 2 merge patterns: Route to cdc-scd-patterns.
  • Slow/expensive BigQuery SQL plan tuning: Route to bigquery-query-optimization.
  • Single-node RDBMS SQL tuning: Route to sql-query-optimization.
  • Application backend code: Route to python-expert.

Process

Phase 1: Capability-Gated Planning & Target AGENTS.md

  1. Maintain root AGENTS.md (<= 90 lines) linking dataform/bq commands and context/memory/MEMORY.md.
  2. Author PLAN.md (<= 50 lines) gating artifacts individually:
    • docs/<feature-name>/data-contracts.md (<= 60 lines): ONLY IF new/modified schemas or SLAs are introduced; SKIP if schemas exist.
    • docs/<feature-name>/pipeline-lineage.md (<= 45 lines): ONLY IF multi-layer Bronze/Silver/Gold flows change; SKIP for single-mart edits.
    • Delegate sql-query-optimizer ONLY IF large partitioned scans or multi-table JOIN/window shuffle risks exist; invoke Tier 3 Data Challenger ONLY IF CDC/SCD-2 merges, late-arriving dedup, or destructive/backfill logic changes.

Phase 2: Medallion Storage, Ingestion & Orchestration

  1. Storage Layers: GCS immutable raw landing zone + BigQuery Medallion tables: Bronze (append-only raw/CDC), Silver (deduplicated, typed, SCD-2), Gold (BI star marts).
  2. Ingestion (Default Batch): Scheduled loads (GCS/Dataform/Composer); Datastream for operational DB CDC; Pub/Sub → BigQuery (or Dataflow only when windowed stream joins/non-SQL code are required).
  3. Orchestration: Dataform for warehouse-native SQLX & assertions; Cloud Composer only for cross-service DAGs.

Read the full file on GitHub · 65 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

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. today First seen · 65 lines · 86 tokens per session scan A e1b03773b693

Subscribe to this mod's changes

gcp-data-engineering is a skill published in the GitHub repository jggomez/expert-ai-developer-skills (30 stars, last pushed yesterday), licensed Apache-2.0. It adds 86 tokens to every session and 1,344 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-11-06.

DevOps and cloudPlanning
Related

gcp-data-engineering compared with skills from other repositories

gcp-data-engineering next to 6 skills from other repositories
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