Skill UI

This skill enables security professionals to conduct authorized assessments on Linux systems, identifying misconfigurations and vulnerabilities that allow privilege escalation from standard users to root. It covers techniques like SUID exploitation, sudo misconfigurations, and cron job hijacking, strictly requiring written authorization and defensive guardrails.

Expert skill for building production-grade AI systems using LangChain 0.1+ and LangGraph. Covers agent architecture, RAG pipelines, memory systems, and deployment strategies. Ideal for developing sophisticated LLM applications with observability and scalability.

Comprehensive guide to LLM Operations (LLMOps). Covers advanced topics like Retrieval-Augmented Generation (RAG), vector database management (Pinecone, Chroma), embeddings, fine-tuning, prompt engineering, and cost optimization. Essential for bridging the gap between AI prototypes and reliable, scalable, production-grade AI systems.

Conduct authorized security assessments for LLM applications and AI agents. Covers prompt injection, tool abuse, RAG exposure, memory poisoning, and system prompt extraction following OWASP LLM/ASI Top 10 guidelines. Includes red-teaming, vulnerability mapping, and compliance engineering for AI systems to ensure safety and robustness.

Provides cross-language patterns for developing memory-safe systems code. This skill covers crucial concepts like RAII, ownership models, smart pointers, and robust resource management. It is essential for preventing common vulnerabilities such as use-after-free, memory leaks, and ensuring reliable resource handling across complex systems.

This skill provides methodologies for designing sophisticated, layered memory architectures for AI agents. It moves beyond simple context windows and basic vector stores by incorporating short-term, long-term, and graph-based memory systems. Implementing this is crucial for agents that require persistence across sessions, maintaining complex entity consistency, and performing advanced, relationship-driven reasoning over accumulated knowledge bases, including time-aware fact tracking.

This skill provides a comprehensive framework for mastering microservices architecture patterns. It covers critical aspects of building robust, distributed systems, including defining service boundaries (Domain-Driven Design), managing inter-service communication, handling distributed data transactions, and implementing various resilience patterns (e.g., circuit breaker, circuit breaker). It is essential for large-scale system decomposition.

Master Minecraft server plugin development with Bukkit, Spigot, and Paper APIs. Covers event systems, NMS internals, packet manipulation, performance tuning, and ecosystem integration for Java plugin projects.

This skill enables building production-ready machine learning systems using modern frameworks like PyTorch and TensorFlow. It covers model serving, feature engineering, A/B testing, monitoring, and MLOps practices. Focuses on scalable, reliable, and efficient ML infrastructure for delivering business value in production environments.

Build scalable ML infrastructure and automation pipelines using tools like MLflow, Kubeflow, and cloud platforms. Manage the full MLOps lifecycle from experimentation to production deployment, ensuring reliable and efficient machine learning systems.

Expert skill for architecting, reviewing, and scaling software systems. Handles architecture, databases, microservices, and infrastructure design to create resilient backend solutions.

Expert in monorepo architecture, build systems, and dependency management at scale. Masters Nx, Turborepo, Bazel, and Lerna for efficient multi-project development. Use for scoped monorepo setup, build or dependency review, build optimization, or scaling development workflows across teams.