Risk Based Testing

Name: Risk Based Testing
Author: proffesor-for-testing

Checked

proffesor-for-testing·vlatest·testing·MIT

Prioritizes test cases based on potential impact and likelihood of failure, optimizing resource allocation for maximum risk mitigation.

Repository

Install on your platform

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

Run this in your project

terminal

$ npx skills add proffesor-for-testing/agentic-qe --skill risk-based-testing -a claude-code

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

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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 11 Oct 2026

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
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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/risk-based-testing/SKILL.md
Scanned: 12 files, 2,417 lines
Copies in repo: 3 — all of them scanned

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About this skill

What it does

This skill enables AI agents to dynamically adjust testing strategies based on the calculated risk level of specific application areas. It prioritizes critical functionality and high-impact features while reducing effort on low-risk components, optimizing resource allocation for Quality Assurance workflows.

When to use it

Prioritizing regression suites after major feature releases to focus on core business logic.
Allocating automated test budgets across a large codebase where full coverage is cost-prohibitive.
Determining release readiness by correlating defect density in high-risk modules with deployment timelines.
Optimizing CI/CD pipelines to run intensive tests only on branches affecting sensitive data or payment flows.

Key capabilities

Dynamic test selection based on impact analysis.
Automated risk scoring for different application modules.
Strategic resource allocation for QA efforts.
Context-aware prioritization of test cases.

Example prompts

"Analyze the current codebase and generate a risk-based test plan, highlighting which modules require full regression coverage versus smoke testing."
"Identify high-risk areas in our payment processing flow and suggest specific automated tests to prioritize for the upcoming release."
"Adjust our nightly build test suite to focus exclusively on features marked as 'critical' based on recent user feedback data."

Tips & gotchas

Ensure your AI agent has access to accurate impact analysis data or defect history, as risk calculations rely heavily on this context. Avoid applying high-risk protocols to experimental features where the cost of failure is low and learning is the primary goal.