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e2e-testing

Design end-to-end validation journeys with evidence naming and flaky diagnosis.

2|Updated Apr 8, 2026
Search Tags:#e2e#flaky-tests#test-strategy#evidence-management#journey-design#validation-plan
One-click install
npx skills add https://github.com/krzemienski/validationforge --skill e2e-testing-krzemienski
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: e2e-testing
Source: https://github.com/krzemienski/validationforge/tree/main/skill-audit-workspace/e2e-testing/skill-snapshot
Command: npx skills add https://github.com/krzemienski/validationforge --skill e2e-testing-krzemienski

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill provides a structured approach to designing end-to-end validation journeys, managing evidence artifacts, and diagnosing flaky flows so that validation outcomes are deterministic, auditable, and actionable.

Core Features & Use Cases

  • Journey design principles: One journey equals one user goal, clear PRECONDITION → ACTION → ASSERTION structure, independence between journeys, and prioritization (P0–P3).
  • Evidence management: Step-NN naming conventions, per-journey evidence directories, automated evidence inventory generation, and retention/archiving guidance.
  • Flaky flow diagnosis: Three-run diagnosis protocol, delta comparison between runs, quarantine pattern with FLAKY.md, and recommended fixes for race conditions, stale data, and network timing issues.
  • Integration examples: Feed journey definitions into create-validation-plan, execute with playwright-validation or ios-validation, and consume evidence with the verdict-writer agent.

Quick Start

Ask the AI to create a prioritized end-to-end validation plan for the "New User Signup" journey with step-NN evidence naming, a three-run flaky diagnosis protocol, and an evidence inventory.

Frequently Asked Questions about e2e-testing

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

FAQPage Schema
How do I design end-to-end validation journeys that avoid ambiguous test results?▼

End-to-end validation journeys require a strict PRECONDITION, ACTION, and ASSERTION structure to eliminate ambiguous test results. This approach enforces journey independence and step-NN evidence naming to ensure deterministic and auditable outcomes.

What is the best way to diagnose flaky end-to-end tests?▼

Diagnosing flaky end-to-end tests uses a three-run diagnosis protocol with delta comparison between runs. This identifies race conditions, stale data, and network timing issues, utilizing a quarantine pattern with FLAKY.md to isolate inconsistent flows.

How do I manage test evidence for web, mobile, and API validations?▼

Managing test evidence for web, mobile, and API validations involves per-journey evidence directories and automated evidence inventory generation. This enforces step-NN naming conventions and provides retention and archiving guidance for deterministic validation artifacts.

Does this end-to-end test strategy work with playwright-validation and ios-validation?▼

Yes, this end-to-end test strategy integrates directly with playwright-validation and ios-validation for execution. It feeds journey definitions into these tools and allows a verdict-writer agent to consume the generated evidence.

When do I need a validation plan for end-to-end testing?▼

A validation plan for end-to-end testing is needed when real-system interactions require deterministic and auditable outcomes. It applies to journey planning, flaky flow diagnosis, and evidence inventory generation for web, mobile, and API validations.

Why should end-to-end test journeys be independent?▼

End-to-end test journeys must be independent to ensure one journey equals one user goal and prevent cascading failures. This journey design principle prioritizes validations from P0 to P3, guaranteeing actionable and reliable end-to-end validation outcomes.

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