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.
npx agentmods add skills/vaquarkhan/data-engineering-agent-skills/python-data-engineering-and-pipeline-packagingnpx skills add vaquarkhan/data-engineering-agent-skills --skill python-data-engineering-and-pipeline-packaginggit clone --depth 1 https://github.com/vaquarkhan/data-engineering-agent-skillsWrote 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.
[](https://agentmods.dev/skills/vaquarkhan/data-engineering-agent-skills/python-data-engineering-and-pipeline-packaging)<a href="https://agentmods.dev/skills/vaquarkhan/data-engineering-agent-skills/python-data-engineering-and-pipeline-packaging"><img src="https://agentmods.dev/badge/skills/vaquarkhan/data-engineering-agent-skills/python-data-engineering-and-pipeline-packaging.svg" alt="Measured on agentmods" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00051 | $0.00679 |
| Opus 5 | $0.00026 | $0.00340 |
| Sonnet 5 | $0.00010 | $0.00136 |
| Haiku 4.5 | $0.00005 | $0.00068 |
Grade A, and why
python-data-engineering-and-pipeline-packaging 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.
How it starts
The opening of the file, as written. The whole thing — 83 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Python Data Engineering And Pipeline Packaging
Overview
Use this skill when Python is the main implementation language for data pipelines or operational data tooling. It helps agents structure jobs as maintainable packages instead of loose scripts, choose the right execution boundary, manage dependencies explicitly, and keep runtime behavior testable and production-safe.
When to Use
- building or modifying
Pythondata pipelines - packaging
PySpark, ingestion, validation, or orchestration helper code - moving from notebooks or scripts into production-ready modules
- managing dependency, environment, and runtime issues in
Python - adding CLI entry points, test harnesses, or local development workflows
Do not treat a working script as a production design just because it runs once.
Workflow
-
Define the role of the Python code. Clarify whether it is:
- a single-node transform
- a
PySparkjob entry point - an orchestration helper
- a validation or reconciliation tool
- an integration or extraction service
-
Package logic into explicit modules. Prefer:
- versioned packages
- reusable modules
- clear CLI or job entry points
- isolated configuration
- minimal hidden global state
-
Make dependency management real. Define:
- environment model
- pinned dependency strategy
- native or system dependency assumptions
- compatibility with runtime platforms such as
Airflow,Spark, or container images
-
Keep runtime boundaries explicit. Decide:
- what runs locally versus distributed
- what belongs in orchestration versus the job package
- how configuration, secrets, and environment values are supplied
- how logs, retries, and exits behave operationally
-
Prove the package is maintainable. Require:
- targeted tests
- representative input cases
- type or interface clarity where useful
- reproducible local execution
Common Rationalizations
| Rationalization | Reality |
|---|---|
| "It is only a small Python script." | Small scripts often become critical pipeline entry points with no packaging or test discipline. |
| "We can keep the business logic in the DAG or notebook." | Hidden logic in orchestration or notebook state becomes hard to test, reuse, and debug. |
| "Requirements are enough documentation." | Dependency files do not explain runtime assumptions, entry points, or platform compatibility. |
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.
- yesterday First seen · 83 lines · 51 tokens per session scan A f73d7cf7ece4
python-data-engineering-and-pipeline-packaging is a skill published in the GitHub repository vaquarkhan/data-engineering-agent-skills (40 stars, last pushed 2mo ago), licensed MIT. It adds 51 tokens to every session and 679 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 2026-09-03.
Other skills, from other repositories
kafka-python-client
Scaffold a production-ready Python Kafka producer and consumer using confluent-kafka-python, with Schema Registry, graceful shutdown, idempotent producer, tests and a complete project layout. Discovers the target topic, partition count and registered JSON Schema directly from the live cluster via any attached Kafka…
temporal-python-testing
Test Temporal workflows with pytest, time-skipping, and mocking strategies. Covers unit testing, integration testing, replay testing, and local development setup. Use when implementing Temporal workflow tests or debugging test failures.
manimgl-best-practices
Trigger when: (1) User mentions "manimgl" or "ManimGL" or "3b1b manim", (2) Code contains from manimlib import , (3) User runs manimgl CLI commands, (4) Working with InteractiveScene, self.frame, self.embed(), ShowCreation(), or ManimGL-specific patterns. Best practices for ManimGL (Grant Sanderson's 3Blue1Brown…
authoring-dags
Workflow and best practices for writing Apache Airflow DAGs. Use when creating a new DAG, write pipeline code, handling questions about DAG patterns and conventions or extending an existing DAG with a follow-up/downstream task. ANY request shaped like 'add a DAG named X', 'write a pipeline', 'add a task that runs…
telnyx-numbers-python
Search, order, and manage phone numbers by location, features, and coverage.
telnyx-ai-outbound-voice-python
End-to-end setup for making a Telnyx AI assistant call a phone number. Covers provisioning a phone number, creating a TeXML application, assigning the number, configuring telephony settings, whitelisting destination countries, and triggering outbound calls via scheduled events. Use this skill (not…