CHIT THWAY
Windows Support Diagnostic Toolkit demonstration32 second demonstration
Projects

Support engineering case study

Windows Support Diagnostic Toolkit

A local, read-only diagnostic workflow that collects bounded Windows evidence, validates a structured report and turns it into clear support findings in a Python dashboard.

View repository
Environment
Windows / local only
Repository
Public
Focus
Safe diagnostic evidence

The process

How this toolkit grew

  1. Initial Problem

    Make Windows evidence understandable

    Windows troubleshooting needed a repeatable way to gather useful evidence and explain it clearly without uploading private machine data, changing system settings or claiming an automatic diagnosis.

  2. First idea

    Collect, validate, explain

    A read-only PowerShell collector creates structured JSON. A separate local Flask dashboard validates it, applies deterministic support rules and presents plain-English explanations with safe manual actions.

  3. Result

    A local support workflow

    The completed toolkit collects system, resource, network, service and recent event evidence, then produces readable findings while handling partial, unavailable, malformed and unsupported reports safely.

Technology architecture

Windows Support Diagnostic Toolkit

Read-only Windows evidence pipeline

Collection

  • PowerShell
  • Windows

Evaluation

  • Python
  • JSON

Quality

  • Pytest
  • Safety validation

Delivery

  • GitHub

A bounded PowerShell collector feeds a validated JSON contract into deterministic Python checks, keeping system evidence safe, testable and easy to explain.

Overview / 01

What the project demonstrates

The project separates data collection, report validation, deterministic evaluation and presentation into distinct stages. That makes the workflow easier to test and prevents the dashboard from silently changing a report or running system commands.

Synthetic fixtures cover healthy, warning, problem, partial and malformed states. Real reports remain local because they can contain machine-specific information, while the public repository uses fictional data for demonstrations and automated checks.

Try it / 02

A simple first run

Use the included fictional sample first. You only need Windows, Python 3.10 or later and a modern browser.

You will need
  • Windows PowerShell 5.1
  • Python 3.10+
  • Git or a downloaded repository ZIP
  1. 01

    Open the project folder

    Download or clone the repository, then open PowerShell inside the folder.

  2. 02

    Create the local Python environment

    Create and activate a virtual environment so the project dependencies stay isolated.

    python -m venv .venv
.\.venv\Scripts\Activate.ps1
  3. 03

    Install the requirements

    Install the small set of packages used by the dashboard and its tests.

    python -m pip install -r requirements-dev.txt
  4. 04

    Start the dashboard

    The default command opens the fictional sample report. Visit the local address shown below and stop it with Ctrl+C.

    python -m dashboard
http://127.0.0.1:5000

Thinking / 03

Decisions behind the work

01

Read-only by design

The PowerShell collector gathers a deliberately limited evidence set. It does not repair Windows, retrieve secrets or send reports anywhere.

02

Deterministic support findings

Documented rules produce Healthy, Warning, Problem or Unavailable states, with evidence and plain-English next actions rather than an opaque diagnosis.

03

Failure-aware presentation

Missing files, malformed JSON, unsupported versions and partial collection produce useful error states instead of raw tracebacks or misleading success screens.

Evidence / 04

Skills and capabilities

  • Read-only collection by design
  • Healthy, malformed, partial and problem fixtures
  • Privacy and failure hardening
  • PowerShell
  • Python
  • JSON
  • Schema validation
  • Safety testing