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.

A comprehensive guide and workflow demonstrating how to use YARA rules for proactive threat hunting. This method helps detect unknown malware, suspicious patterns, and Indicators of Compromise (IOCs) across filesystems, memory dumps, and network artifacts, making it essential for incident response and security analysis.

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 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.

This skill covers the complete workflow for Go performance analysis. It guides developers on writing robust benchmarks, running tests with statistical rigor, profiling hot paths using pprof, analyzing memory/CPU usage, and detecting performance regressions in CI. Ideal for any scenario requiring deep performance measurement in Go.

This skill proactively detects and reports comprehensive system resources, including CPU cores, GPU types (NVIDIA, AMD, Apple Silicon), available memory, and disk space. It generates a detailed JSON report with strategic, context-aware recommendations for optimizing scientific computing workflows, such as recommending parallel processing libraries (Dask, joblib), out-of-core methods (Zarr), or appropriate GPU acceleration backends (Metal, ROCm). Use this before any large-scale data analysis, model training, or computationally intensive simulation to make informed architectural decisions.

Latch is a robust Python framework designed for building, deploying, and managing complex, scalable bioinformatics workflows as serverless pipelines. It supports integrating multiple analysis tools (like Nextflow/Snakemake) and handles cloud data management (LatchFile/LatchDir) and detailed resource configuration (GPU, CPU, memory). Ideal for running large-scale analyses such as RNA-seq, AlphaFold, and single-cell analysis in a reproducible, production-ready environment.

A deep-dive curriculum into core memory corruption vulnerability classes, covering topics like stack/heap buffer overflows, Use-After-Free (UAF), integer overflows, and format strings. This guide uses real-world CVE case studies (e.g., QNAP, Chrome) to illustrate attack vectors, impact analysis, and modern mitigation techniques. Ideal for cybersecurity professionals, penetration testers, and students learning exploit development.

This advanced playbook details expert techniques for format string exploitation. It covers arbitrary stack reading (%p), memory writing (%n), and exploiting weaknesses like GOT/hook overwrites. Essential for CTF challenges and binary vulnerability analysis to achieve reliable Remote Code Execution (RCE).

A comprehensive playbook for advanced mobile security assessments of iOS applications. Covers both jailbroken and non-jailbroken environments, focusing on critical attack vectors such as keychain extraction, URL scheme hijacking, Universal Links exploitation via AASA misconfigurations, runtime memory manipulation (Frida/Objection), and binary protection analysis.

A comprehensive guide for advanced memory forensics using Volatility 2 and 3. This playbook covers artifact extraction, including hidden process detection, network connections, credential dumping, DLL analysis, and code injection detection (malfind). It is essential for incident response, malware analysis, and reconstructing attack timelines from memory dumps.

This playbook provides advanced techniques for symbolic execution using angr, Z3, and Unicorn Engine. It covers automating CTF challenges, handling constraint solving patterns, function hooking, memory manipulation, and emulating custom virtual machines for binary analysis and key recovery.