Skill UI

This skill automates the complex and tedious process of porting academic papers written in LaTeX from one publisher's journal template (e.g., Springer) to another (e.g., MDPI, IEEE). It executes a structured multi-stage workflow, including content extraction, structural mapping, systematic fixing of common LaTeX issues (like float placement and math environments), and final compilation to ensure the output adheres to the target journal's specifications.

An AI-powered suite for generating LinkedIn posts, carousels, newsletters, and content calendars with SEO optimization and a personal memory system that improves over time.

Master core Linux administration tasks including package management, process control, filesystem handling, and scheduling. Essential for managing production servers, troubleshooting system issues, and maintaining infrastructure across Debian and RHEL distributions.

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.

This skill provides production-ready shell script templates for common Linux system administration tasks such as backups, monitoring, user management, and log analysis. It serves as a foundation for security operations and penetration testing environments, requiring basic bash knowledge and appropriate system permissions.

A systematic, multi-phase workflow designed for diagnosing and resolving complex Linux system issues. It guides users through initial assessment, resource analysis (CPU, memory, disk), process investigation, log analysis, network diagnostics, and service failure resolution. Ideal for system administrators and DevOps engineers handling production incidents.

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.

An expert guide for deploying and optimizing Large Language Models (LLMs) entirely on local hardware. Mastering the entire local AI ecosystem, including inference engines like Ollama, vLLM, and llama.cpp. Provides deep knowledge on quantization formats (GGUF, EXL2, AWQ), VRAM calculation, and ensuring privacy-first, offline deployment for state-of-the-art models.

This skill covers configuring Grafana Loki for log aggregation and analysis within DevOps environments. It includes setting up Promtail for log shipping, deploying via Docker or Kubernetes, and writing LogQL queries for filtering. Ideal for replacing ELK stacks with a lighter-weight solution for monitoring application and system logs.

Loki Mode is a multi-agent autonomous startup system designed to execute workflows from PRD to production without human intervention. It integrates research-enhanced AI models including OpenAI SDK, DeepMind, Anthropic, and AWS Bedrock. This skill ensures zero human involvement in the deployment process while maintaining strict constraints on dependencies and response times.