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A comprehensive guide for authorized penetration testers on identifying and exploiting Cross-Site Request Forgery (CSRF) vulnerabilities. This skill covers analyzing state-changing requests, testing anti-CSRF token implementations, and verifying defenses like SameSite cookies and Origin/Referer header validation using tools like Burp Suite.

A comprehensive guide for performing static analysis of Windows Portable Executable (PE) files using tools like PEStudio and scripting libraries. This technique examines file headers, calculates section entropy, identifies suspicious API calls (e.g., process injection, persistence), and extracts obfuscated strings and Indicators of Compromise (IoCs) without executing the sample, making it a crucial step in malware triage and reverse engineering.

Performs TAM/SAM/SOM sizing via Claude-led research and Python computation while summarizing competitive landscapes and tech-stack maturity to support venture capital diligence.

The /hub:spawn command launches N subagents that work on the same task simultaneously. Each agent operates within an isolated git worktree, ensuring true parallelism and resource separation. Users can optionally specify templates (e.g., `optimizer`, `refactorer`) to guide agents toward specific strategies for improving code quality, performance, or test coverage. Ideal for complex software engineering tasks requiring diverse, competing approaches.

Handles IMO/Putnam/USAMO/AIME style problems by generating refined proofs, then running a fresh-context adversarial verifier to catch flaws, finally producing honest confidence reports and verified PDFs when possible.

This skill provides a managed cloud environment for training and fine-tuning advanced vision models. Users can build and customize models for object detection (e.g., DETR, YOLOS), image classification, and segmentation (SAM/SAM2). By leveraging Hugging Face Jobs, training is executed on powerful cloud GPUs without requiring local GPU setup, and all resulting models are automatically saved to the Hugging Face Hub.

A type-safe dependency injection toolkit for Go (Golang) that helps manage service lifecycles, scopes, and dependencies in a structured way. It allows developers to register services and retrieve them through a central container, promoting clean architecture and making code easier to test and maintain. Ideal for refactoring manual constructors into a robust DI system.

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.

A comprehensive, generics-first utility library for Go, providing over 500 type-safe, functional helpers (like Map, Filter, Reduce, GroupBy) for slices, maps, and channels. Designed to replace manual, verbose loops with declarative code. It supports immutable operations by default and offers progressive complexity variants (lo, lop, lom, loi) to handle different performance needs, from standard transforms to CPU-bound parallelism and lazy iteration. Ideal for writing clean, maintainable, and performant Go code.

This library provides powerful monadic types, including Option, Result, and Either, for Go. It enhances type safety by replacing manual nil checks with type constraints and transforming verbose error handling into composable pipelines. It is essential for adopting modern functional programming patterns and building robust, resilient Go applications.

Improves Go error handling by treating errors as structured data. This utility adds critical context—such as domain, user IDs, stack traces, and machine-readable error codes—to standard Go errors. This capability is vital for enhancing observability, enabling APM tools (like Sentry or Datadog) and on-call engineers to diagnose complex issues quickly without relying on manual log correlation.

A type-safe, generics-first library for implementing ReactiveX streams in Golang. It provides declarative, composable pipelines for handling infinite, asynchronous data sources (like WebSockets or timers) with automatic backpressure, robust error propagation, and full Go context integration. Ideal for building real-time, event-driven architectures.