Skill UI

Tracks and runs regression suites, automating execution of critical tests and analyzing outcomes so Claude highlights failures, flaky checks, and stability impacts before deployments.

An expert specialist for rigorous visual validation, focusing on UI testing, design system compliance, and WCAG accessibility verification. This skill systematically analyzes visual evidence for cross-platform consistency, pixel-perfect accuracy, and adherence to modern design standards, utilizing principles from visual regression testing and comprehensive QA methodologies.

Automatically checks application metrics such as load times, bundle sizes, and API responses against predefined performance budgets, surfacing regressions and preventing slippage before deployments—ideal for CI/CD workflows.

Provides Wycheproof test vectors for validating cryptographic implementations against known attacks and edge cases, making it ideal for CI regression sweeps and third-party crypto audits.

Aeon is a scikit-learn compatible toolkit for time series machine learning, covering classification, regression, clustering, forecasting, anomaly detection, segmentation, similarity search, and feature extraction on temporal data.

A comprehensive guide for systematic statistical analysis, designed for academic research. This skill assists in selecting the correct hypothesis tests (t-test, ANOVA, regression, chi-square), checking assumptions (normality, homogeneity), performing advanced analyses (Bayesian methods, power analysis), calculating effect sizes, and generating full APA-formatted reports for publication.

This specialized skill guides users through a structured, forensic investigation process when facing sudden, significant drops in organic traffic or search rankings. It is designed for incident scenarios—such as suspected penalties, Core Updates, major technical regressions, or unexplained traffic crashes—providing a systematic approach to triage, root-cause analysis, and developing a prioritized recovery plan.

Framework ensuring no completion claims are made until fresh verification commands are run and their outputs read, so tests/builds/regressions are proven before anyone says the work is done.

This comprehensive guide outlines a rigorous, human-in-the-loop engineering workflow for leveraging advanced AI agents to complete complex implementation tasks. It mandates defining clear completion criteria, decomposing work into manageable units, and strategically routing models (Haiku, Sonnet, Opus) based on task complexity. The workflow emphasizes continuous evaluation, regression testing, security review of invariants and edge cases, and strict cost discipline to ensure robust and reliable software development using AI assistance.

This comprehensive skill guides users through the entire debugging lifecycle, from reproducing elusive bugs to tracing the root cause, implementing permanent fixes, and preventing future regressions. It covers advanced techniques like binary search, time travel debugging, and handling common patterns such as race conditions and null values, ensuring professional and systematic troubleshooting for any production issue.

A rigorous, security-focused code review process designed for Pull Requests (PRs), commits, and diffs. It moves beyond general reviews by focusing intensely on high-risk areas such as authentication, cryptography, external calls, and value transfer. The methodology is evidence-based, requiring detailed attack scenario modeling and generating comprehensive, auditable markdown reports to identify overlooked vulnerabilities and regressions.

Diagnose and fix failing or flaky Playwright tests via a systematic reproduce-trace-categorize-fix workflow, validate stability by repeated runs, and recommend CI safeguards to prevent regressions.