Skill UI

This skill provides a systematic pipeline for safely executing large-scale code refactors across multiple files or subsystems. It first performs parallel, dependency-aware analysis to map the entire scope, synthesizing a single, actionable plan. The process then breaks the work into independent 'work packets,' which are executed sequentially or in parallel by specialized worker agents, ensuring strict dependency management and behavior parity throughout the entire process.

This skill provides a comprehensive, structured workflow for diagnosing performance bottlenecks in SwiftUI applications. It guides users through a code-first review to detect common issues like invalidation storms, layout thrash, or heavy main-thread work. When code review is insufficient, it provides detailed guidance on collecting runtime evidence using Instruments, enabling precise root cause analysis and targeted remediation strategies.

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).

A comprehensive skill for conducting authorized Bluetooth Low Energy (BLE) security assessments. It covers detecting various attacks, including sniffing, Man-in-the-Middle interception, replay attacks, and GATT enumeration abuse. The workflow guides users through packet capture using specialized sniffers (Ubertooth, nRF Sniffer), analyzing services and characteristics via Python/bleak, and performing cryptographic analysis using tools like crackle.

This system detects ransomware by combining multiple advanced techniques: real-time entropy analysis, file system I/O monitoring, and behavioral heuristics. It identifies abnormal mass file modification patterns, sudden spikes in data entropy (indicating encryption), and suspicious process activities like shadow copy deletion. Ideal for building advanced EDR detection layers to catch zero-day and unknown ransomware variants.

A comprehensive guide and technical framework for detecting and mitigating ransomware kill switch mechanisms. It details methods for analyzing mutex guards, domain-based communication, and registry checks. Learn how to deploy proactive mutex vaccination and use Sysmon/DNS monitoring to prevent malware execution during incident response and analysis.

This tool facilitates comprehensive purple team exercises by executing Atomic Red Team tests mapped to MITRE ATT&CK techniques. It is primarily used for gap analysis, validating detection coverage (blue team visibility) against specific threat vectors, and building continuous, automated adversarial emulation pipelines in CI/CD environments.

This skill detects malicious Command and Control (C2) communications tunneled through the DNS protocol. It analyzes DNS query logs for indicators such as DNS tunneling tools (Iodine, dnscat2), Domain Generation Algorithms (DGA), and encoded payloads within TXT/CNAME records. Techniques include Shannon entropy analysis of subdomains, statistical anomaly detection, and machine learning classification. Ideal for network security analysts conducting deep threat hunting and forensic investigations.

Automates the complex pre-development workflow for large-scale system transformations, such as major rewrites, migrations, or overhauls. This skill performs comprehensive project analysis, task decomposition, planning document generation, and sets up detailed progress tracking across multiple phases, ensuring a structured approach before any coding begins. It supports adaptive control and integrates robust task tracking via GitHub or local files.

A comprehensive set of specialized tools for deep interaction with source codebases. This context-mode is mandatory for advanced tasks such as running code snippets, analyzing files, batch processing repository commands, indexing knowledge, searching content, and fetching web data. It ensures that interactions are context-aware and highly efficient, moving beyond basic file listing or general chat prompts.

A comprehensive guide on implementing Firebase Analytics in Flutter applications. Covers setup, automatic screen view tracking, logging custom and predefined events, setting user properties, and adhering to data privacy best practices for accurate user flow analysis and conversion funnel tracking.

This skill provides a comprehensive framework for measuring Go application performance. It guides users through writing robust benchmarks using `testing.B`, running tests with various flags (`-benchmem`, `-cpuprofile`), interpreting complex profiling data (pprof), statistically comparing results using tools like `benchstat`, and setting up CI regression detection. It is essential for deep performance analysis and making statistically sound optimization decisions.