Skill UI

A comprehensive guide for analyzing binary exploitation techniques, covering buffer overflows, ROP chains, and memory corruption vulnerabilities. It details the use of specialized tools like pwntools, checksec, and ROPgadget for identifying and developing exploits in ELF binaries during authorized security assessments and CTF challenges. Focuses on advanced offensive security practices.

This tool performs comprehensive security testing on GraphQL APIs by generating deeply nested, recursive, or highly repetitive queries. It specifically aims to identify Denial-of-Service (DoS) vulnerabilities and resource exhaustion issues by overwhelming the server's CPU, memory, or database connections. Essential for security auditors and penetration testers.

This automated toolkit streamlines memory forensics using the Volatility3 framework. It is designed to analyze raw memory dumps from Windows, Linux, and macOS, revealing complex threats such as process injection, hidden rootkits, malicious code artifacts, and illicit network connections that traditional disk-based methods cannot detect. Essential for real-time incident response and deep security auditing.

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.

A professional-grade 'Living Pipeline' that integrates advanced AI reasoning into the SDLC. It provides autonomous guardrails for system security, cloud cost management (FinOps), and Kubernetes compliance. It audits Linux Kernel patches for memory vulnerabilities, detects cost drifts in Terraform plans, and generates hardened K8s manifests from natural language policies.

This skill systematically audits a given project by analyzing its historical memory, rollout summaries, and local skill definitions. It identifies repeated complex workflows, recurring operational patterns, or areas where existing skills are stale. The audit process guides the user to recommend the highest-value skills to either add (if a new pattern emerges) or update (if current skills are outdated or incomplete), ensuring the AI agent operates with the most accurate and context-specific knowledge base for optimal project execution.

This tool serves as a persistent memory and state manager for complex coding projects within the Claude environment. It allows users to initialize new projects with structured metadata, track detailed session progress (including summaries, decisions, and next steps), and save the entire context reliably. By ensuring project continuity across disconnected sessions, it drastically improves the flow and consistency of the development workflow.

This advanced assistant profiles specified targets (scripts, processes, GPU, memory, etc.) to identify performance bottlenecks. It selects appropriate profiling strategies, runs external tools (e.g., cProfile, nvidia-smi), and can write code instrumentation to measure CPU, GPU, memory, and interconnect overheads. It generates a detailed, actionable report.

A comprehensive guide for Go developers covering the entire performance measurement lifecycle. Learn how to write rigorous benchmarks using b.Loop(), track memory allocations, run tests with various flags (e.g., -benchmem, -cpuprofile), and analyze results using profiling tools like pprof and benchstat. Ideal for ensuring code stability, detecting regressions, and optimizing critical paths.

A comprehensive guide for advanced Go developers, detailing the internals, optimal usage, and performance pitfalls of core data structures like slices, maps, and arrays. It covers capacity growth mechanics, preallocation strategies using `make` and `slices` package functions, and best practices for choosing between various standard library containers (e.g., `strings.Builder` vs `bytes.Buffer`). Essential for writing highly efficient, memory-safe Go code.

A comprehensive guide for optimizing Golang applications, covering systematic approaches to identifying and resolving bottlenecks. It details best practices for reducing memory allocations, improving CPU efficiency, tuning GC, and managing concurrency. The methodology emphasizes profiling first (using pprof, fgprof) and following an iterative cycle: Benchmark → Diagnose → Improve → Benchmark.

A comprehensive, type-safe in-memory caching library for Go, designed for advanced system architecture. It supports nine sophisticated eviction algorithms (like W-TinyLFU, LRU, LFU, ARC) to optimize cache hit rates based on specific access patterns. Key features include TTL management, loader chains for read-through caching (with singleflight deduplication), sharding, and Prometheus metrics. Ideal for high-frequency services needing to reduce database load and latency.