Skill UI

A comprehensive guide to conducting security assessments on thick client (desktop) applications. This skill details techniques ranging from static analysis (using tools like dnSpy, IDA Pro) to dynamic analysis (process monitoring with Procmon, network interception via Burp Suite). It covers finding hardcoded credentials, identifying insecure local storage, mitigating DLL hijacking, and analyzing memory manipulation within local applications.

This comprehensive guide teaches advanced iOS reverse engineering techniques using Frida dynamic instrumentation. Users can analyze proprietary app logic, extract encryption keys, trace method calls, and bypass anti-tampering or anti-debugging mechanisms without needing source code. It is essential for authorized mobile security testing and protocol analysis.

This comprehensive guide details a step-by-step workflow for reverse engineering malware binaries using Ghidra. It guides users through static and dynamic analysis techniques to study internal logic, C2 communication protocols, cryptographic routines, and obfuscation methods at the assembly and pseudo-C code level. Use this when deep code review is required to extract configuration data, understand exploit mechanisms, or develop detection signatures.

An advanced debugging skill designed for AI agents. It utilizes gdb-cli to provide intelligent assistance for C/C++ programs, enabling the analysis of core dumps, debugging live processes, and investigating complex issues like crashes, deadlocks, and memory corruption. It correlates runtime state inspection with source code context for deeper insights.

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 skill focuses on detecting and mitigating sophisticated code injection and privilege escalation attacks across the entire serverless ecosystem (AWS Lambda, Azure, GCP). It combines static analysis of dangerous code sinks (e.g., eval, exec) with continuous monitoring of event sources (S3, SQS, API Gateway) and rigorous IAM auditing. It provides a comprehensive framework to secure the expanding serverless attack surface.

This tool monitors Modbus TCP communication within SCADA and ICS environments to detect security anomalies. It performs deep packet inspection, baselines normal communication patterns (e.g., typical function code distribution, register access), and applies statistical analysis to identify unauthorized register writes, reconnaissance attempts, or denial-of-service attacks targeting industrial control devices.

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.

This skill provides comprehensive interaction with the DeerFlow AI agent platform via its robust HTTP API. It is designed for managing complex, multi-step AI workflows, including deep academic research, code execution, web browsing, and file analysis. Users can initiate and continue conversation threads, manage agent memory, upload documents (PDF, etc.), and delegate complex research tasks to the autonomous agent system.

A comprehensive guide and toolset for Go code quality assurance using `golangci-lint`. It enables developers to run multiple linters in parallel, configure best practices via `.golangci.yml`, automate fixes, and interpret complex lint output. Ideal for maintaining code standards, setting up CI/CD pipelines, and cleaning up legacy codebases.

This comprehensive guide provides detailed procedural steps for configuring and executing CodeQL code scanning. It covers both implementing automated workflows using GitHub Actions (`codeql.yml`) and running scans via the CodeQL CLI. Users will learn how to manage language matrices, set build modes, interpret SARIF outputs, handle monorepo structures, and troubleshoot complex security analysis setups across multiple languages (e.g., Python, Java, C++).