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Python Development

Checked
apache·vlatest·backend·Apache-2.0

Develops, debugs, and deploys Apache projects using Python, ensuring code quality and adherence to best practices.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add apache/beam --skill python-development -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

apache

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned apache/beam at 6f0047c13a.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
6f0047c13a
Manifest
.agent/skills/python-development/SKILL.md
Scanned
1 file, 164 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

What it does

This skill enables AI agents to work with Apache Beam, a unified programming model for batch and stream data processing. It allows agents to write Python code that can be executed on various distributed processing backends like Apache Flink, Apache Spark, and Google Cloud Dataflow. The skill facilitates building complex data pipelines and performing transformations at scale.

When to use it

  • Data Pipeline Development: Automate the creation of ETL (Extract, Transform, Load) pipelines for moving and transforming large datasets.
  • Stream Processing Applications: Build real-time applications that analyze streaming data from sources like Kafka or Pub/Sub.
  • Batch Data Analysis: Process historical data stored in systems like Hadoop or cloud storage to generate insights.
  • Data Transformation at Scale: Perform complex data cleaning, enrichment, and aggregation tasks on massive datasets.

Key capabilities

  • Python programming language support
  • Apache Beam API integration
  • Support for distributed processing backends (Flink, Spark, Dataflow)
  • Pipeline construction and execution

Example prompts

  • "Write a Beam pipeline in Python to read CSV data from GCS, filter rows where the 'value' column is greater than 100, and write the results to BigQuery."
  • "Generate a Python script using Apache Beam that reads messages from Kafka, transforms them to uppercase, and writes them back to another Kafka topic."
  • “Create a Beam pipeline in python to calculate the average of a large dataset stored on Hadoop.”

Tips & gotchas

  • Familiarity with Python programming is required to effectively utilize this skill.
  • Understanding Apache Beam concepts (PCollections, transforms, pipelines) will improve prompt clarity and results.