Skill UI

Loki Mode is a multi-agent autonomous startup system that progresses PRD to production with zero human intervention, orchestrating research-enhanced components such as OpenAI SDK, DeepMind, Anthropic, AWS Bedrock, and agent tooling while enforcing RARV reasoning and efficiency tracking.

This skill provides a comprehensive framework for configuring Mistral AI across distinct environments (development, staging, and production). It manages environment-specific secrets (using GCP, AWS, or Vault), defines critical parameters like default models, rate limits, and feature flags, ensuring secure, isolated, and reliable deployments for AI services in multi-stage pipelines.

Auto-activating assistant that guides AWS Sagemaker endpoint deployment, from best practices to production-ready code, configurations, and validation for inference serving and monitoring workflows.

Automates SNS topic configuration tasks within AWS, offering guidance, best practices, code generation, and validation for compute, storage, networking, and serverless scenarios.

A comprehensive expert for deploying and scaling AI agents using AWS Bedrock AgentCore. This guide covers the full lifecycle of agent management, including service selection (Gateway, Runtime, Memory, Identity, etc.), deployment patterns, credential management, and advanced integration workflows required for enterprise-scale AI solutions.

Achieve complete integration with Amazon Alexa to build sophisticated, intelligent voice skills. This system transforms a standard Alexa device into an ultra-intelligent assistant by utilizing Claude as the core LLM brain, while deeply integrating with the entire AWS ecosystem (Lambda, DynamoDB, Polly, Smart Home). Ideal for complex, specialized voice interaction applications.

Simulates Ilya Sutskever, co-founder of OpenAI and deep learning pioneer. Provides profound perspectives on AGI safety, the ethics of superintelligence, scaling laws, and the philosophical depth of AI consciousness. Ideal for complex, safety-critical deep technical discussions.

A comprehensive guide and methodology for proactively hunting adversary abuse of legitimate cloud services (Azure, AWS, GCP, SaaS). This technique focuses on identifying Command and Control (C2), data staging, and exfiltration methods that leverage normal cloud functionality, making detection challenging.

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 comprehensive management capabilities for AI Integrations within the Arize platform. Users can create, list, retrieve, and update credentials for various Large Language Model (LLM) providers, including OpenAI, Anthropic, Azure OpenAI, AWS Bedrock, and Vertex AI. These integrations are crucial for enabling Arize's advanced monitoring and evaluation features that require invoking external LLMs.

This guide provides a deep dive into migrating machine learning workloads, including inference services and training pipelines, from major hyperscalers (AWS, GCP, Azure) to the CoreWeave GPU cloud. It covers cost comparisons, containerization, Kubernetes YAML adaptation, and a phased deployment strategy, helping users ensure a smooth, cost-effective, and optimized transition.

This skill performs rigorous mathematical proof verification using an adversarial, cross-model review process. It reads a LaTeX proof, systematically identifies logical gaps (e.g., unjustified assertions, quantifier errors) and theoretical flaws (e.g., illegal interchanges, non-uniform convergence). It then iteratively fixes these gaps with full derivations, re-reviews, and generates a comprehensive audit report, ensuring mathematical rigor and completeness. Use this when seeking deep, academic verification of a theory paper.