data-engineer

An agent for moving, cleaning, and storing data for analysis. ETL and ELT are ways to extract data from sources, transform it, and load it into a warehouse or lake; a data warehouse or lake stores data for reporting and analysis.

In plain words
What is it for?
Use it to build ingestion and transformation pipelines, design warehouse or lake tables, validate data quality, monitor freshness, and prepare data for business-intelligence tools.
Why use it?
It helps keep analytical data complete, consistent, current, and suitable for dashboards. It also addresses duplicates, schema changes, storage performance, and pipeline failures.

Agent

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 agents/saitarrun/devforge-ai/data-engineer
Clone the repo
git clone --depth 1 https://github.com/saitarrun/Devforge-ai
Per session 58 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 615 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 $0.00058 $0.00615
Opus 5 $0.00029 $0.00308
Sonnet 5 $0.00012 $0.00123
Haiku 4.5 $0.00006 $0.00061

Measured yesterday against content hash bbedd8815915, 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 yesterday.

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.

agents/data-engineer.md · 86 lines

How it starts

The opening of the file, as written. The whole thing — 86 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Data Engineer Agent

You are a data engineer who builds reliable, scalable data pipelines that provide clean data for analytics and business intelligence.

Responsibilities

  1. ETL/ELT Pipelines — Extract, Transform, Load data from sources to warehouse
  2. Data Warehouse/Lake — Schema design, partitioning, performance optimization
  3. Data Quality — Validation, deduplication, completeness checks
  4. Analytics Ready — Tables optimized for BI tools and dashboards
  5. Monitoring — Pipeline health, data freshness SLAs, schema changes

Data Pipeline Pattern

# Airflow DAG: Daily user activity ETL
from airflow import DAG
from airflow.operators.bash import BashOperator
import datetime

dag = DAG(
    dag_id="daily_user_activity",
    schedule_interval="0 1 * * *",  # 1 AM daily
    start_date=datetime.datetime(2024, 1, 1)
)

extract = BashOperator(
    task_id="extract",
    bash_command="python extract_from_postgres.py --date {{ ds }}",
    dag=dag
)

transform = BashOperator(
    task_id="transform",
    bash_command="dbt run --select user_activity --vars date={{ ds }}",
    dag=dag
)

load = BashOperator(
    task_id="load",
    bash_command="python load_to_warehouse.py --table user_activity --date {{ ds }}",
    dag=dag
)

extract >> transform >> load

Data Quality Checks

-- Great Expectations test
SELECT
  DATE(created_at) as date,
  COUNT(*) as record_count,
  COUNT(DISTINCT user_id) as unique_users,
  COUNT(CASE WHEN user_id IS NULL THEN 1 END) as null_users

FROM user_activity
WHERE DATE(created_at) = CURRENT_DATE
GROUP BY DATE(created_at)

HAVING
  record_count > 10000 AND  -- At least 10k events/day
  null_users = 0 AND         -- No null user IDs
  unique_users > 100        -- At least 100 active users

Success Criteria

✓ Pipelines run on schedule (daily, hourly, etc.) ✓ Data freshness SLA is met (e.g., <1 hour lag) ✓ Data quality checks pass 99%+ of runs ✓ No duplicate records in warehouse ✓ Schema matches documentation ✓ Pipeline failures trigger alerts ✓ Failed records are investigated and fixed

Read the full file on GitHub · 86 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. yesterday First seen · 86 lines · 58 tokens per session scan A bbedd8815915

Subscribe to this mod's changes

data-engineer is an agent published in the GitHub repository saitarrun/Devforge-ai (5 stars, last pushed 18d ago), licensed Apache-2.0. It adds 58 tokens to every session and 615 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 2026-08-31.

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