data-engineering SKILL.md skillA
data-engineering from travisjneuman/.claude is an agent skill: a SKILL.md folder an AI agent loads when a task calls for it.
mdr add travisjneuman/.claude/data-engineering@git:20260328.75c803aPins git:20260328.75c803a in mdr.lock. Pin the fingerprint instead.
- Audit grade
- A, 17 of 17 checks passed
- Kind
- SKILL.md skill
- Repository
- travisjneuman/.claude
- Path
skills/data-engineering/SKILL.md- Versions recorded
- 1
- Latest version
- git:20260328.75c803a, committed Mar 28, 2026
- Size
- 3,370 bytes
- Repository stars
- 105
- License
- MIT
ETL/ELT pipelines, data warehousing (BigQuery, Snowflake, Redshift), stream processing (Kafka, Spark Streaming), orchestration (Airflow, Dagster, Prefect), dbt transformations, and data lake architecture. Use when building data pipelines, designing warehouse schemas, or implementing real-time data processing.
Collected from public GitHub. Every version is saved exactly as it was and never changes. Install it at an exact version with the mdr tool, and every file is checked before it reaches your agent. The main file is checked against the SHA-256 fingerprint recorded here, and the others against the fingerprints GitHub keeps for its source commit. The automatic checks below grade the latest version, and the same checks always give the same file the same grade.
What the file says
# Data Engineering
## Pipeline Architecture
### ETL vs ELT
| Pattern | When to Use | Tools |
|---------|-------------|-------|
| **ETL** | Transform before loading, data quality critical | Airflow + custom, Spark |
| **ELT** | Raw → warehouse → transform in-place | Fivetran + dbt, Airbyte + dbt |
### Orchestration
**Apache Airflow:**
```python
from airflow.decorators import dag, task
from datetime import datetime
@dag(schedule="@daily", start_date=datetime(2024, 1, 1), catchup=False)
def my_pipeline():
@task()
def extract() -> dict:
return {"data": "extracted"}
@task()
def transform(data: dict) -> dict:
return {"transformed": True}
@task()
def load(data: dict):
# Load to warehouse
pass
raw = extract()
transformed = transform(raw)
load(transformed)
my_pipeline()
```
**Dagster (recommended for new projects):**
```python
from dagster import asset, Definitions
@asset
def raw_users():
return extract_from_source()
@asset
def cleaned_users(raw_users):
return clean_and_validate(raw_users)
```
## dbt Transformations
```sql
-- models/marts/dim_customers.sql
…Read the whole file at its exact version.
How to install
mdr add travisjneuman/.claude/data-engineering@sha256:a9a66a0045778e57The line at the top of this page pins the label git:20260328.75c803a, which is what most people want. This one pins the sha256 fingerprint of the contents instead, so it survives a label being moved. Either way the exact version is written to mdr.lock, and mdr install downloads and checks those contents again, so it installs them exactly or stops with an error.
[](https://markdownregistry.com/a/art_tfc5bujlnfj6kcn3)
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Versions
The registry has recorded 1 version of this file, committed on Mar 28, 2026.
Audit of the latest version
- pass: Frontmatter block present
- pass: Frontmatter declares a name
- pass: Frontmatter declares a description
- pass: Size between 200 bytes and 200 KB (3370 bytes)
- pass: No zero-width or bidi control characters
- pass: No instruction hidden inside an HTML comment
- pass: No link to an exfiltration or paste host
- pass: No credential-shaped string
- pass: No instruction to send local credentials anywhere
- pass: No text hidden with inline styles
- pass: No prompt-injection phrasing
- pass: No curl or wget piped into a shell
- pass: No recursive delete of root, home or parent
- pass: No instruction to read or print local credentials
- pass: No base64 blob over 200 characters
- pass: No link to a raw IP address
- pass: No script tag
Source
travisjneuman/.claude · 105 stars · license MIT · pushed 2026-10-19 · branch master
API
GET https://markdownregistry.com/api/v1/artifacts/art_tfc5bujlnfj6kcn3 GET https://markdownregistry.com/api/v1/resolve?ref=travisjneuman/.claude/data-engineering GET https://markdownregistry.com/api/v1/blob/a9a66a0045778e57c957119b0a2859b802ccd9466e3d1843a1d25de30cb1e837
Your AI assistant does the legwork. You hear about the deals worth your word. Hand yours the standing instructions at modelranch.com and it joins the network that reads files like this one.
Guides: the SKILL.md format and its required fields · what to check before you install a skill · how to pin a skill to an exact version · where to find agent skills · keeping a team on the same skills · which agent file does what.
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