> Testing for React/Next.js with Jest, React Testing Library, and Playwright. Use when generating tests, writing unit tests, analyzing coverage, scaffolding E2E tests, configuring Jest, or improving test quality.
npx skills add https://github.com/borghei/Claude-Skills --skill senior-qa
Test automation, coverage analysis, and quality assurance patterns for React and Next.js applications. Generates Jest + React Testing Library unit test stubs, analyzes Istanbul/LCOV coverage for gaps, and scaffolds Playwright E2E suites for App Router and Pages Router projects.
jest-axe a11y).Before generating tests, confirm these inputs. If any is unknown or vague, ASK — do not assume:
test_suite_generator vs e2e_test_scaffolder)jest-axe) (sets which test stubs are emitted)Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.
| Tool | Purpose | Command |
|------|---------|---------|
| test_suite_generator.py | Generate Jest + RTL test stubs from React components | python scripts/test_suite_generator.py src/components/ --output __tests__/ |
| coverage_analyzer.py | Analyze Istanbul/LCOV coverage and report gaps | python scripts/coverage_analyzer.py coverage/coverage-final.json --threshold 80 |
| e2e_test_scaffolder.py | Scaffold Playwright E2E tests from Next.js routes | python scripts/e2e_test_scaffolder.py src/app/ --output e2e/ |
Load the reference that matches the task — keep this file lean and pull detail on demand:
This skill covers:
jest-axe integration.This skill does NOT cover:
senior-backend for Express/Node.js testing patterns).senior-devops for infrastructure and performance tooling).| Skill | Integration | Data Flow |
|-------|-------------|-----------|
| senior-frontend | Generated test stubs align with component patterns from the frontend skill | Frontend components --> test_suite_generator.py --> test files |
| senior-fullstack | Code quality analyzer consumes the same coverage reports produced here | coverage_analyzer.py output --> fullstack quality dashboard |
| senior-devops | E2E test scaffolder generates CI-ready Playwright configs that plug into DevOps pipelines | e2e_test_scaffolder.py --> playwright.config.ts --> GitHub Actions workflow |
| code-reviewer | Coverage gaps feed directly into code review checklists for untested changes | coverage_analyzer.py gaps --> review checklist items |
| tdd-guide | TDD workflow references this skill's test generator for initial red-phase stub creation | TDD cycle --> test_suite_generator.py --scan-only --> write tests --> implement |
| qa-browser-automation | Page Object Models generated here are consumed by the browser automation skill for advanced E2E scenarios | e2e_test_scaffolder.py --include-pom --> POM classes --> browser automation flows |
Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.
Use when implementation is complete, all tests pass, and you need to decide how to integrate the work - guides completion of development work by presenting structured options for merge, PR, or cleanup
Use when implementing any feature or bugfix, before writing implementation code
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes
Use when about to claim work is complete, fixed, or passing, before committing or creating PRs - requires running verification commands and confirming output before making any success claims; evidence before assertions always
Expert guidance for systematic backtesting of trading strategies. Use when developing, testing, stress-testing, or validating quantitative trading strategies. Covers "beating ideas to death" methodology, parameter robustness testing, slippage modeling, bias prevention, and interpreting backtest results. Applicable when user asks about backtesting, strategy validation, robustness testing, avoiding overfitting, or systematic trading development.
Cloud laboratory platform for automated protein testing and validation. Use when designing proteins and needing experimental validation including binding assays, expression testing, thermostability measurements, enzyme activity assays, or protein sequence optimization. Also use for submitting experiments via API, tracking experiment status, downloading results, optimizing protein sequences for better expression using computational tools (NetSolP, SoluProt, SolubleMPNN, ESM), or managing protein design workflows with wet-lab validation.
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
Take borghei/senior-qa from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.