Use when adding or editing QA skills in seed-skills/ or getting them onto the live qaskills.sh catalog, e.g. "add N new skills", "create a seed skill for X", "seed the database", "the skill page is empty", "skill 404s on the site".
Use when deploying qaskills.sh to production, verifying whether a deploy landed, or when a push to main did not show up on the live site, e.g. "deploy", "ship it", "push this live", "is prod updated?", "the site still shows the old version".
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.
Build an SDET or automation engineer resume around framework ownership, tool-stack evidence, CI/CD integration, and GitHub proof that survives technical screening and recruiter keyword search.
Build a DevOps or SRE resume around pipeline ownership, infrastructure as code, reliability metrics, and incident evidence, structured to pass both ATS parsing and platform-team screening.
Optimize a QA or SDET LinkedIn profile for recruiter search with headline token strategy, skills ordering, featured proof, and content habits that convert profile views into interviews.
Turn test suites, frameworks, and bug hunts into a portfolio recruiters actually open, with case studies, linkable repos, live demo suites, and writeups structured like incident reports.
Build and optimize a manual/functional QA tester resume with test design vocabulary, defect metrics, ISTQB positioning, and ATS-safe structure that survives both parsers and hiring managers.
Marktforschungsanalysten und Marketingspezialisten
Beschreibung
Use when publishing SEO blog articles to qaskills.sh, e.g. "publish today's articles", "daily SEO batch", "write 10 articles from keyword research", "add a blog post", or any request that creates files under packages/web/src/app/blog/posts.
Generate test data from the schemas you already have. Read OpenAPI, JSON Schema, SQL DDL, or TypeScript models and produce deterministic factories, boundary and negative cases, relational datasets with valid foreign keys, cleanup scripts, and PII-safe…
Analyze a git diff, map affected risks, select the tests that matter, detect coverage gaps on changed lines, run configurable quality gates, and produce a go/no-go release report with cited evidence. Recommends only; never merges or deploys.
Test LLM, RAG, MCP, and agentic systems end to end. Build golden datasets, run deterministic checks and LLM judges, score retrieval, probe prompt injection, verify tool use, and gate CI on thresholds. Orchestrates DeepEval, Ragas, promptfoo, and Langfuse.
Diagnose flaky test failures from Playwright reports, traces, and rerun history. Classify each failure as product, test, environment, data, or unknown with cited evidence and a proposed fix. Never auto-modifies code without opt-in.
Teach agents to use AI browser agents for exploratory and smoke QA with step budgets, evidence-based assertions, guardrails, and Playwright conversion.