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quality-assurance

Perform evidence-first end-to-end quality assurance for software repositories.

Updated Feb 13, 2026
Search Tags:#quality assurance#code review#debugging#test strategy#security review#flaky tests#ai evals
One-click install
npx skills add https://github.com/alvarovillalbaa/plugins --skill quality-assurance-alvarovillalbaa
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: quality-assurance
Source: https://github.com/alvarovillalbaa/plugins/tree/main/engineering/skills/quality-assurance
Command: npx skills add https://github.com/alvarovillalbaa/plugins --skill quality-assurance-alvarovillalbaa

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

Quality Assurance resolves the risk of shipping broken, insecure, or unverified changes by turning debugging and verification into repeatable, evidence-first workflows.

Core Features & Use Cases

  • Evidence-first QA for PRs: Guides review, debugging, and repair using concrete artifacts (stack traces, failure logs, screenshots, traces) instead of opinions.
  • Deterministic test strategy: Enforces error-path coverage, boundary testing, contract/component testing, and minimal-but-strong regression suites.
  • Security and compliance-oriented validation: Supports passive security review, threat modeling, and secure engineering checks aligned to release confidence.
  • LLM/AI evals alongside traditional tests: Adds evals when prompts, tool calls, RAG, or nondeterministic outputs are part of the behavior under test.
  • Browser trace observability: Enables agent-driven QA with full observability into network, DOM, screenshots, and console logs via the browser-trace workflow.

Quick Start

Run the QA scan on your repository by using the installed skill directory’s scanner script against the target repository root.

Frequently Asked Questions about quality-assurance

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I perform evidence-first code review and debugging for pull requests?▼

Evidence-first code review and debugging for pull requests requires collecting concrete failure artifacts like stack traces, logs, and screenshots to guide verification workflows. This approach replaces opinion-based reviews with repeatable, artifact-driven validation for deterministic code and component boundaries.

What is the best way to fix flaky tests and build a deterministic test strategy?▼

Fixing flaky tests and building a deterministic test strategy involves enforcing error-path coverage, boundary testing, and contract testing. This ensures minimal-but-strong regression suites by applying disciplined layer selection and coverage-aware operating rules to your test suites.

How do I design AI evals for testing nondeterministic LLM outputs and RAG pipelines?▼

Designing AI evals for nondeterministic LLM outputs, prompts, tool calls, and RAG pipelines requires adding structured evaluation alongside traditional tests. This validates AI-enabled systems by applying evidence-first QA operating rules to assess behavior under test.

Does this QA workflow support security review and threat modeling for release validation?▼

This QA workflow supports security review and threat modeling for release validation through passive security checks and secure engineering validation. It aligns compliance-oriented validation with CI quality gates to ensure safe engineering practices before shipping software.

How do I get browser trace observability for agent-driven QA testing?▼

Browser trace observability for agent-driven QA testing enables full visibility into network requests, DOM state, screenshots, and console logs. This workflow captures comprehensive failure artifacts to debug end-to-end user interactions and component boundaries.

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