Skill UI

A comprehensive platform designed for building, deploying, and managing continuous, autonomous AI agents. Users can create complex, multi-step automation workflows using a visual, drag-and-drop node editor, or extend functionality using a dedicated developer toolkit. Ideal for implementing persistent agents triggered by webhooks, schedules, or manual input, enabling sophisticated, end-to-end AI automation pipelines.

A comprehensive platform designed for building, deploying, and managing continuous, autonomous AI agents. Users can create complex, multi-step automation workflows using a visual, drag-and-drop node editor, or extend functionality using a dedicated developer toolkit. Ideal for implementing persistent agents triggered by webhooks, schedules, or manual input, enabling sophisticated, end-to-end AI automation pipelines.

Chroma is an open-source, AI-native embedding database designed for building sophisticated LLM applications. It allows users to store high-dimensional embeddings and associated metadata, facilitating powerful vector search and full-text filtering. Ideal for implementing Retrieval-Augmented Generation (RAG) systems, semantic search, and document retrieval in local or self-hosted environments, making it perfect for open-source AI projects and prototyping.

Guides teams through choosing and implementing validated Exa deployment architectures—from direct search to cached semantic layers and RAG pipelines—so integrations scale from simple apps to high-query, AI-enhanced solutions.

Ensures Gamma integrations respect privacy by implementing consent gates, PII sanitization, retention schedules, GDPR request handling, and audit trails so teams can deploy compliant workflows.

This skill guides the combination of vector similarity search and keyword-based matching to achieve superior information retrieval. It is essential when building advanced Retrieval-Augmented Generation (RAG) systems, custom search engines, or when standard search methods fail to capture both semantic meaning and specific term requirements.

LitGPT provides clean, readable, and production-ready implementations for over 20 state-of-the-art LLMs (including Llama, Gemma, Phi, Mistral). It supports comprehensive workflows, including full fine-tuning, efficient LoRA/QLoRA adaptation, and pretraining from scratch. Ideal for developers needing academic understanding or robust, deployable model architectures.

Guides implementing Kling AI content-policy compliance with prompt screening, moderation checks, violation handling, and documentation to ensure generated responses stay within safety guidelines for production deployments.

Guides choosing and implementing Perplexity-validated architectures across scales, from simple widgets to cached layers and automated research pipelines, including deployment trade-offs and error mitigation.

This skill provides expertise in advanced caching strategies for Large Language Models (LLMs). It details methods to drastically reduce API costs and latency by implementing multi-level caching: prompt prefix caching, full response caching, and semantic similarity matching. Ideal for optimizing complex LLM workflows and building cost-efficient AI applications.

Guides selecting and implementing Retell AI architecture blueprints from single webhook servers to event-driven voice pipelines, helping teams match scale, latency budgets, and state management when building or migrating voice agent integrations.

This skill provides comprehensive patterns and best practices for implementing efficient similarity search mechanisms using vector databases. It is essential for building advanced AI systems such as semantic search engines, implementing Retrieval-Augmented Generation (RAG), and developing recommendation systems. Use this when optimizing retrieval performance or scaling to massive volumes of vectors.