data-engineer

data-engineer is a skill for Claude Code, Codex from daffy0208/ai-dev-standards. It costs 16 tokens per session (4,563 once invoked), scanned A, original, MIT.

A guide for building data pipelines, which move and transform information between systems, and related data infrastructure. It covers ETL—extracting, transforming, and loading data—along with databases, warehouses, caching, replication, analytics, and exports.

In plain words
What is it for?
Use it for API-to-database syncs, batch or real-time ingestion, schema design, data warehousing, caching, replication, metric calculation, reporting, and data export.
Why use it?
It provides patterns for turning raw source data into structured, usable data while checking quality and calculating useful results. This reduces the need to design ingestion and transformation workflows from scratch.

Skill for Claude CodeCodex

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.

agentmods
npx agentmods add skills/daffy0208/ai-dev-standards/data-engineer
Any agent
npx skills add daffy0208/ai-dev-standards --skill data-engineer
Clone the repo
git clone --depth 1 https://github.com/daffy0208/ai-dev-standards

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-engineer

README.md
[![agentmods](https://agentmods.dev/badge/skills/daffy0208/ai-dev-standards/data-engineer.svg)](https://agentmods.dev/skills/daffy0208/ai-dev-standards/data-engineer)
Your own site
<a href="https://agentmods.dev/skills/daffy0208/ai-dev-standards/data-engineer"><img src="https://agentmods.dev/badge/skills/daffy0208/ai-dev-standards/data-engineer.svg" alt="Measured on agentmods" height="20"></a>
Per session 16 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 4,563 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00016 $0.04563
Opus 5 $0.00008 $0.02282
Sonnet 5 $0.00003 $0.00913
Haiku 4.5 $0.00002 $0.00456

Measured 4d ago against content hash 873316a2549d, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

data-engineer 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 4d 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/data-engineer/SKILL.md · 870 lines

How it starts

The opening of the file, as written. The whole thing — 870 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Data Engineer Skill

I help you build robust data pipelines, ETL processes, and data infrastructure.

What I Do

Data Pipelines:

  • Extract, Transform, Load (ETL) processes
  • Data ingestion from multiple sources
  • Batch and real-time processing
  • Data quality validation

Data Infrastructure:

  • Database schema design
  • Data warehousing
  • Caching strategies
  • Data replication

Analytics:

  • Data aggregation
  • Metrics calculation
  • Report generation
  • Data export

ETL Patterns

Pattern 1: Simple ETL Pipeline

Use case: Daily sync from external API to database

// lib/etl/daily-sync.ts

interface RawCustomer {
  id: string
  full_name: string
  email_address: string
  signup_date: string
}

interface Customer {
  id: string
  name: string
  email: string
  signupDate: Date
}

export async function syncCustomers() {
  console.log('Starting customer sync...')

  // EXTRACT: Fetch data from external API
  const response = await fetch('https://api.example.com/customers', {
    headers: {
      Authorization: `Bearer ${process.env.API_KEY}`
    }
  })

  const rawCustomers: RawCustomer[] = await response.json()
  console.log(`Extracted ${rawCustomers.length} customers`)

  // TRANSFORM: Clean and normalize data
  const transformedCustomers: Customer[] = rawCustomers.map(raw => ({
    id: raw.id,
    name: raw.full_name.trim(),
    email: raw.email_address.toLowerCase(),
    signupDate: new Date(raw.signup_date)
  }))

  // LOAD: Insert into database
  let inserted = 0
  let updated = 0

  for (const customer of transformedCustomers) {
    const existing = await db.customers.findUnique({
      where: { id: customer.id }
    })

    if (existing) {
      await db.customers.update({
        where: { id: customer.id },
        data: customer
      })
      updated++
    } else {
      await db.customers.create({
        data: customer
      })
      inserted++
    }
  }

  console.log(`Sync complete: ${inserted} inserted, ${updated} updated`)

  return { inserted, updated, total: transformedCustomers.length }
}

Read the full file on GitHub · 870 lines

Files

What ships with it

2 files 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. 4d ago First seen · 870 lines · 16 tokens per session scan A 873316a2549d

Subscribe to this mod's changes

data-engineer is a skill published in the GitHub repository daffy0208/ai-dev-standards (36 stars, last pushed 8mo ago), licensed MIT. It adds 16 tokens to every session and 4,563 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 2026-08-30.