mastepanoski/claude-skills
GitHubux-audit-rethink
Comprehensive UX audit using IxDF's 7 factors, 5 usability characteristics, and 5 interaction dimensions. Holistic evaluation with redesign proposals based on user-centered design principles.
ui-design-review
Comprehensive visual design and aesthetics evaluation. Analyzes typography, color, spacing, hierarchy, consistency, branding, and modern design trends for polished, professional interfaces.
don-norman-principles-audit
Evaluate UX/UI using Don Norman's 7 fundamental design principles from The Design of Everyday Things. Audit discoverability, affordances, signifiers, feedback, mapping, constraints and conceptual models.
iso-42001-ai-governance
AI governance audit using ISO 42001 standard. Ensures AI systems are developed and deployed responsibly with risk management, ethics, security, transparency, and compliance best practices.
wcag-accessibility-audit
Comprehensive web accessibility audit using WCAG 2.1/2.2 guidelines. Evaluate compliance across 4 POUR principles (Perceivable, Operable, Understandable, Robust) with A, AA, AAA conformance levels.
cognitive-walkthrough
Deep-dive usability evaluation of specific user tasks. Simulates novice user cognition step-by-step to identify learnability issues, unclear actions, and points of confusion.
owasp-llm-top10
Security audit for LLM and GenAI applications using OWASP Top 10 for LLM Apps 2025. Assess prompt injection, data leakage, supply chain, and 7 more critical vulnerabilities.
nielsen-heuristics-audit
Evaluate UX/UI using Jakob Nielsen's 10 usability heuristics. Comprehensive audit of visibility, control, consistency, error prevention, recognition, flexibility, aesthetics, error recovery, and documentation.
nist-ai-rmf
AI risk assessment using NIST AI RMF 1.0 framework. Evaluate AI systems across 4 core functions (Govern, Map, Measure, Manage) for trustworthy and responsible AI deployment.
owasp-ai-testing
AI trustworthiness testing using OWASP AI Testing Guide v1. Execute 44 test cases across 4 layers (Application, Model, Infrastructure, Data) with practical payloads and remediation.
ai-assessment-scale
Evaluate AI contribution in projects using the AI Assessment Scale (AIAS) 5-level framework. Measure AI involvement from no AI to full AI exploration across development stages.