Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add PramodDutta/qaskills --skill automation-interview-prepgit clone --depth 1 https://github.com/PramodDutta/qaskillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/pramoddutta/qaskills/automation-interview-prep)<a href="https://agentmods.dev/skills/pramoddutta/qaskills/automation-interview-prep"><img src="https://agentmods.dev/badge/skills/pramoddutta/qaskills/automation-interview-prep/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/pramoddutta/qaskills/automation-interview-prep"><img src="https://agentmods.dev/badge/skills/pramoddutta/qaskills/automation-interview-prep.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00038 | $0.01251 |
| Opus 5.5 | $0.00015 | $0.00500 |
| Sonnet 5.5 | $0.00008 | $0.00250 |
| Haiku 4.5 | $0.00004 | $0.00125 |
Grade A, and why
Automation Interview Prep scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 113 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
| Round | What actually happens | Preparation focus |
|---|---|---|
| Recruiter screen | Stack verification, salary bands, notice period | 90-second experience summary, exact tool years |
| Coding screen | Easy/medium algorithms or string/array work in your language | 20-30 problems, narrate while coding |
| Framework design | "Design test automation for X" on a whiteboard/doc | Architecture narration with trade-offs |
| API/practical | Test this endpoint, review this test code, find the bugs | Postman/code fluency, boundary thinking out loud |
| Scenario | "How would you test a login page / payment flow / search" | Structured decomposition, not feature listing |
| Behavioral | STAR stories, conflict, quality advocacy | 6 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).
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 113 lines · 38 tokens per session scan A da8ed164e99b
Automation Interview Prep is a skill published in the GitHub repository PramodDutta/qaskills (237 stars, last pushed 5d ago), licensed MIT. It adds 38 tokens to every session and 1,251 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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