Skill UI

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.

A sophisticated system for recovering and reconstructing complex project context across distributed, multi-agent AI workflows. It utilizes advanced semantic vector search, relevance filtering, and optimized rehydration patterns to maintain high knowledge fidelity and ensure seamless continuity in long-running, complex AI projects. Essential for advanced RAG implementations.

Leverages the Exa API for semantic/embeddings search, discovering similar content, or filtering by categories like companies, people, and research papers, making research and knowledge retrieval more targeted.

Implements robust content policy compliance for generative AI prompts and outputs, such as those from Kling AI. This skill provides pre-submission filtering functionality, preventing the generation of restricted content (e.g., violence, explicit material, hate speech) and saving API credits. It includes Python code examples for prompt screening and handling server-side rejection messages.

NVIDIA’s NeMo Guardrails adds runtime safety rails to LLM apps, covering jailbreak detection, hallucination and fact-checking, PII filtering, and toxicity checks via Colang 2.0 rules and integrations for production use on T4 GPUs.

Outlines guarantees valid JSON/XML/code outputs by applying CFG/FSM-constrained token filtering, Pydantic schemas, and grammar-level sampling while supporting local Transformers, llama.cpp, and vLLM backends for fast inference.

Meta’s Prompt Guard is an 86M-parameter classifier that spots prompt injections and jailbreak attempts before they reach LLMs, filtering user input, third-party data, or RAG documents with multilingual support and sub-2ms latency for fast deployments.

Qdrant is a high-performance, Rust-powered vector database designed for production-grade RAG (Retrieval-Augmented Generation) and semantic search applications. It provides fast nearest neighbor search, supports complex hybrid search using metadata filtering, and offers horizontal scalability through sharding and replication. Ideal for building real-time recommendation systems and enterprise-level knowledge retrieval.

Sanitize user prompts through Google Model Armor templates via the gws CLI command, ensuring outbound safety and supporting stdin, text, or explicit JSON inputs for prompt filtering.

Provides comprehensive patterns and techniques for securing AI agent systems. Learn how to enforce governance policies, including tool allow/block lists, content filtering, rate limiting, and human approval workflows. Essential for building robust, compliant, and trustworthy agents in multi-agent or production environments that interact with external APIs and sensitive data.

This comprehensive skill provides multi-layered guardrails for integrating with LLMs like Claude. It enforces security and compliance by implementing input validation (PII detection, length checks), system prompt restriction (scope control, safety rules), output filtering (preventing sensitive data leakage), cost governance, and model access policies. Ideal for building secure, robust, and governed AI applications.

This guide addresses critical LangGraph streaming pitfalls, including state overdraw, proxy buffering issues (Nginx, Cloud Run), and excessive event noise. It provides a production-grade pattern for implementing robust SSE/WebSocket endpoints using FastAPI. Learn how to correctly select the `stream_mode` (messages, updates, values) and implement server-side event filtering to reliably build live-token chat UIs and perform comprehensive state visualization.