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Automation Interview Prep

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PramodDutta
Automation Interview Prep

Prepare for SDET and automation interviews round by round, covering coding screens, framework design, API testing tasks, scenario questions, and STAR stories built from real testing work.

Overview

PublisherPramodDutta
Repositoryqaskills
Skill nameAutomation Interview Prep
Stars
239
Forks
31
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by PramodDutta on GitHub. Read the source before you install it.

Installation

Install the Automation Interview Prep AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/PramodDutta/qaskills/tree/main/seed-skills/automation-interview-prep
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/PramodDutta/qaskills.git /tmp/qaskills
mkdir -p .claude/skills
cp -r /tmp/qaskills/seed-skills/automation-interview-prep .claude/skills/pramoddutta-automation-interview-prep
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Automation Interview Prep in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Automation Interview Prep on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Automation Interview Prep is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Automation Interview Prep

When to Use This Skill

Use this skill when the user:

  • Has an SDET, QA Automation, or Test Engineer interview scheduled
  • Mentions: "SDET interview", "automation interview", "QA interview questions", "framework design round"
  • Wants STAR stories built from their testing experience
  • Needs a preparation plan matched to a specific company's loop

Core Capabilities

  • Map the standard SDET loop and prepare each round separately
  • Generate STAR stories from defect catches, flake hunts, and framework work
  • Drill framework-design narration with trade-off vocabulary
  • Prepare API-testing tasks and test-the-function exercises
  • Build answers for the scenario classics without sounding scripted
  • Produce a question list to ask interviewers that signals seniority

The Standard SDET Loop

RoundWhat actually happensPreparation focus
Recruiter screenStack verification, salary bands, notice period90-second experience summary, exact tool years
Coding screenEasy/medium algorithms or string/array work in your language20-30 problems, narrate while coding
Framework design"Design test automation for X" on a whiteboard/docArchitecture narration with trade-offs
API/practicalTest this endpoint, review this test code, find the bugsPostman/code fluency, boundary thinking out loud
Scenario"How would you test a login page / payment flow / search"Structured decomposition, not feature listing
BehavioralSTAR stories, conflict, quality advocacy6 prepared stories with numbers

Coding Screen Reality

SDET coding bars sit below SWE bars at most companies but are rising. Cover:

  • Strings and arrays: reversal, deduplication, frequency counts, two pointers
  • Maps and sets: first non-repeating character, anagram grouping
  • Simple recursion and iteration conversions
  • Language fluency: collections, string methods, error handling in YOUR primary language

Narrate constantly. SDET interviewers weight communication above optimal complexity; a clean O(n log n) explained well beats a silent O(n).

Framework Design Round

The question is some form of: "You join us; there is no automation. Design it."

Structure the answer in this order, and name a trade-off at every step:

  1. What to automate first: risk-ranked smoke path, not everything ("I start with the 10 flows that block release, because ROI concentrates there")
  2. Layer split: API tests for logic breadth, UI for critical journeys ("pushing checks down the pyramid keeps runtime sane")
  3. Structure: Page objects/fixtures/factories, test data strategy, config per environment
  4. Execution: parallelization, CI trigger points (PR vs nightly), sharding
  5. Trust: retry policy, flake quarantine, reporting humans read
  6. Adoption: code review for tests, docs, who writes tests besides you

Interviewers pass candidates who justify choices, not candidates who name the most tools. "Playwright because trace files cut my debugging time in half" beats a feature list.

The Scenario Classics

"How would you test a login page?" is a filter for structured thinking:

  1. Clarify scope first (which auth methods? SSO? rate limiting? mobile?)
  2. Functional: valid/invalid matrix, case sensitivity, whitespace, unicode
  3. Boundaries: field lengths, empty, paste behavior
  4. Security-adjacent: lockout policy, error-message information leakage, password masking (note: deep pentest is its own discipline)
  5. Non-functional: latency, concurrent sessions, accessibility of the form
  6. State: remember-me, expired sessions, back-button after logout

Announcing the categories before diving in is the pass signal.

STAR Story Bank

Build six stories minimum, each with a number in the Result:

Story slotSource material
The big catchA defect you found that would have cost real money/users
The flake huntA flaky suite you stabilized, with the root causes
The framework decisionA tool/architecture choice you made and defended
The conflictDeveloper pushed back on a bug; how it resolved
The missA defect that escaped YOU, and what changed after
The advocacyYou changed a process (gating, coverage rule, review)

The miss story matters most. Interviewers distrust candidates with no failure story; the recovery is the demonstration of seniority.

Questions That Signal Seniority

Ask two or three of:

  • "What percent of regression is automated today, and do engineers trust it enough to gate merges?"
  • "Who owns test infrastructure when it breaks at 9 AM on release day?"
  • "What is the current flake rate, and is anyone's job to reduce it?"
  • "How is AI changing your testing work this year?"

Each one signals you have lived the failure modes the team is hiring against.

For QA and Testing Roles

Round-zero preparation: re-read your own resume as the interviewer will. Every tool listed is fair game for "walk me through how you used X"; every metric will get "how was that measured?" Practice defending your top three claims out loud once before the loop. Candidates fail more interviews on their own unprepared resume claims than on algorithm difficulty.

Frequently asked questions

What does the Automation Interview Prep AI skill do?

Prepare for SDET and automation interviews round by round, covering coding screens, framework design, API testing tasks, scenario questions, and STAR stories built from real testing work.

Why use Automation Interview Prep on TypingMind?

Because you install it once and use it with any model. Automation Interview Prep is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Automation Interview Prep in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/PramodDutta/qaskills/tree/main/seed-skills/automation-interview-prep. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Automation Interview Prep?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Automation Interview Prep?

As many as you like. As long as a model supports skills, you can use Automation Interview Prep with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Automation Interview Prep AI skill free?

Yes. It is published on GitHub by PramodDutta under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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