Skill UI

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.

This skill provides comprehensive techniques for optimizing prompts used with any Large Language Model (LLM). It teaches proven methodologies—including Role definition (R), Context setting (S), Constraints (C), Instructions (I), and Templates (T)—to transform vague inputs into precision-engineered instructions. Users can reliably achieve structured JSON output, implement Chain-of-Thought reasoning, reduce hallucinations, and minimize token usage, ensuring consistent and high-quality results across different AI platforms.

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.

Design and operate production LLMOps platforms with CI/CD, model promotion workflows, evaluation gates, rollback, and governance for cloud and self-hosted inference.

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.

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.

Automated visual tuning: a vision or video model rates rendered variants in a loop. Render labeled variants into one artifact, ask the model to rate them and suggest better values, render suggestions, pick the best, repeat until good. Ideal for aesthetics, timing, layout, color grading, and other subjective visual parameters without numeric metrics.

Discover, find, compare, audit, repair, adapt, craft, run, debrief, and prepare repeatable AI-agent loops for publication. A comprehensive tool for managing bounded feedback loops in AI agent workflows.

Build enterprise agents for Microsoft 365, Teams, and Copilot Studio with the Microsoft 365 Agents SDK in TypeScript/Node.js. Covers Express hosting, AgentApplication routing, streaming responses with Azure OpenAI, invoke activities, and Copilot Studio client and WebChat integration.

Design and implement complete machine learning pipelines using multi-agent orchestration following modern MLOps best practices. Covers data engineering, feature stores, model training, and production deployment. Ideal for building scalable, monitored, and reproducible ML systems in production environments.

Analyze suspected malware through static, dynamic, and behavioral techniques: IOC extraction, YARA/Sigma rule authoring, sandbox orchestration, and anti-analysis detection.

Connect to MAXIA AI-to-AI marketplace on Solana. Discover, buy, and sell AI services, earn USDC. Includes 13 MCP tools, A2A protocol, DeFi yields, sentiment analysis, and rug detection.