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 comprehensive checklist guides developers and DevOps engineers through the necessary steps for deploying MaintainX integrations to a production environment. It covers crucial areas such as authentication security, error resilience (retry/circuit breaking), data integrity (idempotency, pagination), observability setup (logging/metrics), and modern deployment best practices, ensuring a stable and reliable go-live process.

Manages and automates deployment rollbacks with safety checks, ensuring rapid recovery from failed releases by reverting to known-good versions and verifying system health.

Helps Claude manage Kubernetes network policies and firewall rules by generating or updating YAML configurations based on infrastructure needs, ensuring secure pod-to-pod communication and compliance with least-privilege principles.

Provides 139 proven marketing ideas for SaaS/software products, guiding you through context-aware recommendations, stage/budget considerations, and implementation tips to spark growth or resolve marketing plateaus.

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.

A comprehensive guide detailing essential security measures for integrating Mistral AI models. Covers API key management, defending against prompt injection, implementing content moderation, sanitizing outputs, and secure logging practices, ensuring robust and safe application development.

A comprehensive skill set for building, deploying, and managing production-grade Machine Learning systems. Expertise spans modern frameworks (PyTorch, TensorFlow), model serving using Kubernetes, advanced feature engineering (Feast, Tecton), and robust MLOps practices (monitoring, A/B testing, CI/CD). Focuses on delivering scalable, reliable, and business-value-driven AI solutions.

Mastering the full lifecycle of machine learning models from data ingestion to deployment. This expertise covers building robust, production-grade MLOps pipelines using orchestrators like Kubeflow, Airflow, or Prefect. Key skills include feature store implementation with Feast, rigorous data validation, experiment tracking (e.g., MLflow), model registry management, and automated deployment workflows. Essential for automating model training and ensuring reproducibility at scale.

Enforces mobile-first, touch-first decisions by requiring feasibility checks, mandatory readings, and anti-pattern bans before any UI work to prevent desktop or AI-default assumptions.

Documenting React Native and Flutter patterns for building responsive, performant product lists with typed navigation, safe areas, and platform-aware styles, plus anti-patterns and checklist to keep JS threads smooth.

This expert specializes in secure mobile development practices, covering areas like input validation, WebView security, secure data storage (KeyStore/Keychain), and advanced network security (Certificate Pinning). Use this skill to write secure, resilient mobile code for both iOS and Android, mitigating platform-specific vulnerabilities and ensuring sensitive data protection.