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Xp Practices

Checked
proffesor-for-testing·vlatest·testing·MIT

Analyzes existing test code to suggest improvements aligning with established XP practices like refactoring and TDD.

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 proffesor-for-testing/agentic-qe --skill xp-practices -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

proffesor-for-testing

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 proffesor-for-testing/agentic-qe at 124d8c735e.

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

Commit
124d8c735e
Manifest
assets/skills/xp-practices/SKILL.md
Scanned
2 files, 234 lines
Copies in repo
2 — all of them scanned
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 provides a curated collection of practical experiences and methodologies for AI agents, specifically tailored for agentic quality engineering (Agentic QE). It enables agents to access structured knowledge on testing strategies, validation techniques, and operational best practices within complex software ecosystems.

When to use it

  • Designing Agentic Workflows: When building autonomous agents that require rigorous self-validation or peer-review mechanisms before executing critical tasks.
  • Quality Assurance Automation: For scenarios where an AI agent needs to generate test cases, simulate user behavior, or analyze system stability without human intervention.
  • Skill Gap Analysis: When evaluating whether an existing agent architecture lacks specific testing competencies needed for production deployment.
  • Educational Contexts: To teach new AI agents the fundamentals of software reliability and error handling through established industry practices.

Key capabilities

  • Agentic Quality Engineering (Agentic QE) frameworks
  • Practical experience libraries for autonomous testing
  • Methodologies for validating AI-driven decision processes
  • Integration patterns for quality assurance in agent loops

Example prompts

  • "Retrieve the standard operating procedures for an AI agent to validate its own output before submitting a code commit."
  • "List the top three practical experiences required for an autonomous agent to perform regression testing on a web application."
  • "Explain how agentic quality engineering principles apply to a multi-agent system coordinating a deployment pipeline."

Tips & gotchas

Ensure your AI agent has access to the specific version of these practices relevant to its target environment, as testing methodologies evolve rapidly with new model capabilities. This skill is most effective when combined with real-time feedback loops from the agent's actual execution environment.