Data Pipeline Patterns logo

Data Pipeline Patterns

Community
vibeeval
data-pipeline-patterns

ETL/ELT patterns, batch vs streaming, idempotency, data quality framework, and pipeline orchestration

Overview

Publishervibeeval
Repositoryvibecosystem
Skill namedata-pipeline-patterns
Stars
533
Forks
44
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by vibeeval on GitHub. Read the source before you install it.

Installation

Install the Data Pipeline Patterns AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/vibeeval/vibecosystem/tree/main/skills/data-pipeline-patterns
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/vibeeval/vibecosystem.git /tmp/vibecosystem
mkdir -p .claude/skills
cp -r /tmp/vibecosystem/skills/data-pipeline-patterns .claude/skills/data-pipeline-patterns
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Data Pipeline Patterns in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Data Pipeline Patterns on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Data Pipeline Patterns is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Data Pipeline Patterns

ETL vs ELT Decision

KriterETLELT
Transform locationPipeline'daData warehouse'da
Data volumeKüçük-ortaBüyük
FlexibilityDüşükYüksek
CostCompute-heavyStorage-heavy
Use caseLegacy, complianceModern analytics

Batch vs Streaming

KriterBatchStreaming
LatencyDakika-saatSaniye-milisaniye
ComplexityDüşükYüksek
CostDüşükYüksek
Use caseReporting, ETLReal-time alerts, dashboards
ToolAirflow, dbtKafka Streams, Flink

Idempotency Patterns

python
# Pattern 1: Upsert
INSERT INTO target (id, name, updated_at)
VALUES (%(id)s, %(name)s, %(ts)s)
ON CONFLICT (id) DO UPDATE SET
  name = EXCLUDED.name,
  updated_at = EXCLUDED.updated_at

# Pattern 2: Partition overwrite
DELETE FROM target WHERE partition_date = '2026-03-14';
INSERT INTO target SELECT * FROM staging WHERE partition_date = '2026-03-14';

# Pattern 3: Checkpoint
last_checkpoint = get_checkpoint('pipeline_x')
new_data = source.query(f"WHERE updated_at > '{last_checkpoint}'")
process(new_data)
save_checkpoint('pipeline_x', max(new_data.updated_at))

Data Quality Framework

python
import pandera as pa

schema = pa.DataFrameSchema({
    "user_id": pa.Column(int, pa.Check.gt(0), nullable=False),
    "email": pa.Column(str, pa.Check.str_matches(r'^.+@.+\..+$')),
    "age": pa.Column(int, pa.Check.in_range(0, 150), nullable=True),
    "created_at": pa.Column(pa.DateTime, pa.Check.less_than_or_equal_to(pd.Timestamp.now()))
})

validated_df = schema.validate(df)  # Fail on invalid data

Quality Dimensions

DimensionKontrolTool
CompletenessNULL ratio < thresholdGreat Expectations
AccuracyValue range checkspandera
FreshnessLast update < SLAAirflow sensor
UniquenessDuplicate checkSQL DISTINCT
ConsistencyCross-table referential integritydbt test

Pipeline Orchestration

python
# Airflow DAG
from airflow import DAG
from airflow.operators.python import PythonOperator

with DAG('daily_etl', schedule='0 6 * * *', catchup=False) as dag:
    extract = PythonOperator(task_id='extract', python_callable=extract_fn)
    transform = PythonOperator(task_id='transform', python_callable=transform_fn)
    load = PythonOperator(task_id='load', python_callable=load_fn)
    validate = PythonOperator(task_id='validate', python_callable=validate_fn)

    extract >> transform >> load >> validate

Checklist

  • Pipeline idempotent (rerun safe)
  • Data quality checks her adımda
  • Dead letter queue (failed records)
  • Monitoring + alerting aktif
  • Schema evolution handled
  • Backfill mekanizması var
  • Retry logic (exponential backoff)
  • Data lineage tracked

Anti-Patterns

  • Pipeline'da hardcoded credentials
  • Idempotent olmayan transform
  • Data quality check'siz load
  • Monolithic pipeline (parçala)
  • Silent failure (error swallowing)

Frequently asked questions

What does the Data Pipeline Patterns AI skill do?

ETL/ELT patterns, batch vs streaming, idempotency, data quality framework, and pipeline orchestration

Why use Data Pipeline Patterns on TypingMind?

Because you install it once and use it with any model. Data Pipeline Patterns is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Data Pipeline Patterns in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/vibeeval/vibecosystem/tree/main/skills/data-pipeline-patterns. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Data Pipeline Patterns?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Data Pipeline Patterns?

As many as you like. As long as a model supports skills, you can use Data Pipeline Patterns with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Data Pipeline Patterns AI skill free?

Yes. It is published on GitHub by vibeeval under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇