data-engineering

data-engineering is a skill for Claude Code, Codex from Global-mindee/WAY. It costs 23 tokens per session (1,509 once invoked), scanned A, original, MIT.

A guide to moving data from source systems into a prepared store for analysis. ETL means extracting data, changing it into a useful format, and loading it into a warehouse; it also covers Spark and data checks.

In plain words
What is it for?
Use it to build ETL pipelines, load data warehouses, process data with Apache Spark, and validate data quality.
Why use it?
It helps organise repeatable data pipelines and catch incorrect or incomplete data before it reaches reports or analysis.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to build ETL pipelines, load data warehouses, process data with Apache Spark, and validate data quality.

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Install with agentmods
npx agentmods add skills/global-mindee/way/data-engineering
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 Global-mindee/WAY --skill data-engineering
Clone the repo
git clone --depth 1 https://github.com/Global-mindee/WAY

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 data-engineering

README.md
[![agentmods](https://agentmods.dev/badge/skills/global-mindee/way/data-engineering/github.svg)](https://agentmods.dev/skills/global-mindee/way/data-engineering)
Your own site
<a href="https://agentmods.dev/skills/global-mindee/way/data-engineering"><img src="https://agentmods.dev/badge/skills/global-mindee/way/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 data-engineering

Your own site · 80×15
<a href="https://agentmods.dev/skills/global-mindee/way/data-engineering"><img src="https://agentmods.dev/badge/skills/global-mindee/way/data-engineering.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 23 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,509 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 $0.00023 $0.01509
Opus 5 $0.00012 $0.00754
Sonnet 5 $0.00005 $0.00302
Haiku 4.5 $0.00002 $0.00151

Measured 6d ago against content hash d904f2942b09, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

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 6d 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.

skills/04_infra-platform/data-engineering/SKILL.md · 225 lines

How it starts

The opening of the file, as written. The whole thing — 225 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Data Engineering

ETL Pipeline Pattern

from datetime import datetime
from dataclasses import dataclass

@dataclass
class PipelineResult:
    records_extracted: int
    records_transformed: int
    records_loaded: int
    errors: list[str]
    duration_seconds: float

class OrderPipeline:
    def __init__(self, source_db, warehouse_db):
        self.source = source_db
        self.warehouse = warehouse_db

    def extract(self, since: datetime) -> list[dict]:
        query = """
            SELECT o.*, c.name as customer_name, c.segment
            FROM orders o
            JOIN customers c ON o.customer_id = c.id
            WHERE o.updated_at > %s
        """
        return self.source.fetch_all(query, [since])

    def transform(self, records: list[dict]) -> list[dict]:
        transformed = []
        for record in records:
            transformed.append({
                "order_id": record["id"],
                "customer_name": record["customer_name"],
                "segment": record["segment"].upper(),
                "total_amount": float(record["total"]),
                "order_date": record["created_at"].date(),
                "fiscal_quarter": get_fiscal_quarter(record["created_at"]),
                "is_high_value": float(record["total"]) > 1000,
                "loaded_at": datetime.utcnow(),
            })
        return transformed

    def load(self, records: list[dict]) -> int:
        return self.warehouse.upsert_batch(
            table="fact_orders",
            records=records,
            conflict_keys=["order_id"],
            batch_size=5000,
        )

    def run(self, since: datetime) -> PipelineResult:
        start = datetime.utcnow()
        raw = self.extract(since)
        clean = self.transform(raw)
        loaded = self.load(clean)
        return PipelineResult(
            records_extracted=len(raw),
            records_transformed=len(clean),
            records_loaded=loaded,
            errors=[],
            duration_seconds=(datetime.utcnow() - start).total_seconds(),
        )

Read the full file on GitHub · 225 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. 6d ago First seen · 225 lines · 23 tokens per session scan A d904f2942b09

Subscribe to this mod's changes

data-engineering is a skill published in the GitHub repository Global-mindee/WAY (11 stars, last pushed 2d ago), licensed MIT. It adds 23 tokens to every session and 1,509 once invoked, about $0.0001 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 2026-09-03.

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