Explorar skills

Statistical analysis toolkit. Hypothesis tests (t-test, ANOVA, chi-square), regression, correlation, Bayesian stats, power analysis, assumption checks, APA reporting, for academic research.

テスト計画・実行・報告を標準化する運用スキル。

Automatically activated when user asks about test quality, code coverage, test reliability, test maintainability, or wants to analyze their test suite. Provides framework-agnostic test quality analysis and improvement recommendations. Does NOT provide framework-specific patterns - use jest-testing or playwright-testing for those.

Diagnoses and fixes CI/CD pipeline failures. Use when user mentions 'CI', 'GitHub Actions', 'GitLab CI', 'ビルドエラー', 'テスト失敗', 'パイプライン', 'CIが落ちた', or asks to analyze build/test failures. Do NOT load for: ローカルビルド, 通常の実装作業, レビュー, セットアップ.

This skill should be used when the user asks to "learn ethical hacking", "understand penetration testing lifecycle", "perform reconnaissance", "conduct security scanning", "exploit vulnerabilities", or "write penetration test reports". It provides comprehensive ethical hacking methodology and techniques.

pytest testing patterns for Python. Triggers on: pytest, fixture, mark, parametrize, mock, conftest, test coverage, unit test, integration test, pytest.raises.

This skill should be used when the user asks to "test for broken authentication vulnerabilities", "assess session management security", "perform credential stuffing tests", "evaluate password policies", "test for session fixation", or "identify authentication bypass flaws". It provides comprehensive techniques for identifying authentication and session management weaknesses in web applications.

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World-class data science skill for statistical modeling, experimentation, causal inference, and advanced analytics. Expertise in Python (NumPy, Pandas, Scikit-learn), R, SQL, statistical methods, A/B testing, time series, and business intelligence. Includes experiment design, feature engineering, model evaluation, and stakeholder communication. Use when designing experiments, building predictive m

Build GitLab CI/CD pipelines with multi-stage workflows, caching, and distributed runners for scalable automation. Use when implementing GitLab CI/CD, optimizing pipeline performance, or setting up automated testing and deployment.

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes

Test smart contracts comprehensively using Hardhat and Foundry with unit tests, integration tests, and mainnet forking. Use when testing Solidity contracts, setting up blockchain test suites, or validating DeFi protocols.