ValidTec Projects & GitHub Repositories

How the current projects work together

The two repositories demonstrate a practical separation between AI-assisted test design and deterministic automated execution.

Track 1Analyze and approveRequirements, risks, scenarios, and human approval evidence.

HandoffAutomation specificationAn approved, reviewable contract for implementation.

Track 5Execute and reportDeterministic API checks, cleanup, CI validation, and release evidence.

Track 1 • Agentic QA

ValidTec Track 1 — Agentic QA Workflow

Feature intake → requirement analysis → risk-based test design → human approval → automation handoff → QA reporting

This Python project demonstrates a controlled agentic QA workflow with explicit human oversight, traceable JSON evidence, automated tests, and GitHub Actions validation.

What this demonstrates

Structured feature and requirement intake
Risk-based scenario generation
Explicit human approval before automation
Traceable JSON artifacts and QA evidence
Unit tests and GitHub Actions validation

Portfolio value

Use this project to explain how you apply traditional QA controls—review, traceability, approval, and repeatability—to AI-assisted workflows.

Python 3.11+Human approval gate7 scenariosUnit testsMIT License

Workflow

Step 1Feature intake

Step 2Requirement analysis

Step 3Risk-based test design

Step 4Human approval

Step 5Automation handoff

Step 6QA reporting

View Repository →Download ZIP ↓Explore Track 1

Clone the repositorygit clone https://github.com/WaikiSin2025/ValidTec-track-1-agentic-qa.git

Repository purposeDesign, review, approve, and document AI-assisted QA scenarios before deterministic implementation.

Visitor access: Anyone can view, download, or clone this public repository. Only authorized collaborators can push changes.

Track 5 • New Automation Repo

ValidTec Track 5 — Automation

Health check → organization creation → user invitation → negative and authorization checks → license assignment → state verification → cleanup

This portfolio-ready regression repository converts realistic production-risk scenarios into deterministic API tests that run locally and in GitHub Actions. A built-in Node.js API keeps the demonstration self-contained.

What this demonstrates

API regression design and Postman collection structure
Newman execution and native Node.js smoke testing
Positive, negative, authorization, and entitlement checks
Test-data creation, state verification, and cleanup
GitHub Actions CI with JUnit report artifacts
Release-gate and defect-to-regression thinking

Project requirements

Node.js 20+ is required. The repository includes a small local API, Postman collection, Newman runner, smoke test, CI workflow, and synthetic test data only.

Node.js 20+PostmanNewmanNative fetchGitHub ActionsMIT License

Enterprise-style onboarding regression flow

Step 1Health check

Step 2Create organization

Step 3Invite user

Step 4Reject duplicate invitation

Step 5Reject unauthorized invitation

Step 6Assign active license

Step 7Verify onboarding state

Step 8Cleanup synthetic data

View Repository →Download ZIP ↓Explore Track 5

Clone the repositorygit clone https://github.com/WaikiSin2025/ValidTec-track-5-automation.git

Run locally

npm install
npm run server

# In another terminal
npm run smoke
npm run newman

Release gate: All onboarding P0 checks must pass. Authorization and duplicate-protection failures block release, and cleanup must succeed so synthetic data does not affect later runs.

A structure that will scale beyond two repositories

Keep the home page limited to compact summary cards. Add every future repository to this page using the same project-card format, grouped by track when the collection grows. This avoids turning the home page into a technical catalog.

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