Skill UI

This skill performs an adversarial audit of web application authentication flows. It systematically probes for critical security vulnerabilities such as missing CSRF protection, insecure session cookies (lacking Secure/HttpOnly flags), risky cross-origin redirects, and OAuth misconfigurations. It generates a structured report detailing findings and severity levels, making it ideal for pre-deployment security audits and regression testing.

Automates comprehensive end-to-end Quality Assurance (QA) for deployed web applications. This skill performs multi-stage testing, including smoke tests, complex user interactions (forms, authentication), visual regression checks across breakpoints, performance monitoring (Core Web Vitals), and WCAG accessibility audits.

This technique audits user interaction flows (like button clicks) to detect subtle bugs that static analysis often misses. It specifically tracks the full sequence of state changes, identifying instances where later actions unintentionally cancel, contradict, or leave the UI in an inconsistent state due to state store side effects or race conditions. Ideal for debugging complex state management architectures (Redux, Zustand).

This utility provides garbage collection for Claude Code configurations. It periodically scans various directories—including skills, memory files, hooks, permissions, and caches—to identify orphaned, stale, or redundant configuration items. Crucially, the process is designed to be non-destructive, always requiring explicit human confirmation for every deletion to ensure a safe, audited cleanup process.

This tool audits the token consumption across all components (agents, skills, rules, MCP servers) within a Claude Code session. It analyzes token overhead, identifies bloat (e.g., overly long descriptions, redundant components), and generates a detailed context budget report. Use it to maintain optimal context window usage, improve performance, and prevent context saturation.

This tool performs robust outlier detection on individual session spending. Utilizing Median and Median Absolute Deviation (MAD), it calculates a modified z-score, making it highly resistant to extreme values, unlike mean+sigma methods. It specifically identifies which singular sessions are anomalous, making it crucial for cost monitoring, auditing, and integrating into CI/CD pipelines to prevent unexpected resource overruns.

This skill runs comprehensive benchmarks against structural and adversarial corpora. It measures the performance and cost efficiency of a booster model compared to leading LLM baselines (Gemini, Anthropic). Results are persisted in verifiable JSON reports, crucial for auditing claims and verifying booster win rates before publishing.

This utility manages scheduled, persistent background workers using the `CronCreate` mechanism. Unlike simple in-session loops, scheduled cron jobs survive system restarts, making them ideal for Continuous Integration (CI), system monitoring, and scheduled maintenance tasks (e.g., security audits, performance optimization, data consolidation). Users can define various recurring schedules and understand the difference between persistent cron scheduling and active session looping.

A comprehensive tool designed to audit and validate the structural integrity of a Domain-Driven Design (DDD) codebase. It detects cross-context import violations, checks for violations of aggregate invariant rules, and enforces naming conventions for domain events and repositories. Ideal for CI/CD gates and pre-merge code reviews to prevent architectural decay.

A comprehensive guide detailing security patterns and audit checklists for Automated Market Maker (AMM) smart contracts in DeFi. Covers critical vulnerabilities such as flash loans, slippage, reentrancy attacks, price manipulation, and integer overflow/underflow. Essential for reviewing major DeFi protocols like Uniswap.

A deterministic 'Stop hook' designed to enforce session hygiene for AI agents. It checks machine-verifiable facts such as file modification timestamps of learning logs, disk space, and predefined rationalization patterns. This gate ensures that complex tasks lead to captured knowledge, acting as a mechanical safeguard complementing AI self-auditing.

This comprehensive tool scans all project dependencies against databases of known CVEs (Common Vulnerabilities and Exposures) and security vulnerabilities. It is essential for conducting thorough security audits of third-party packages, ensuring code quality before major releases, or responding to specific security advisories. It provides actionable reports categorized by severity.