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Found 262 Skills
Testing web applications for clickjacking vulnerabilities by assessing frame embedding controls and crafting proof-of-concept overlay attacks during authorized security assessments.
Building & extending Pi — authoring TypeScript extensions (ExtensionAPI, registerTool, registerProvider, /commands, UI hooks), publishing as npm/git packages (pi-package), embedding via JSON-RPC mode (--mode rpc/json, JSONL framing, AgentSession SDK), and developing inside the pi_agent_rust repo. Use for any "how do I build a Pi extension/package/SDK client" question.
Generates a Gemini LiveAPI client service class in the user's chosen programming language. Use when the user wants to build, scaffold, or integrate a client that connects to the Gemini Enterprise LiveAPI websocket endpoint, handles session setup/resumption, bearer token refresh, and sending/receiving `ClientMessage`/`ServerMessage` protos. Don't use for general (non-live, non-bidirectional) Gemini API usage such as one-shot `generateContent`, embeddings, image/video generation, or fine-tuning — use the `gemini-api` skill for those.
Configures Embedded Messaging Deployments for Messaging for In-App and Web (MIAW). Use when the user needs to create a new embedded messaging deployment from scratch using Connect API with defaults, or update an existing deployment's settings using Metadata API. Produces Connect API request payloads for new deployments and EmbeddedServiceConfig metadata XML for updates. TRIGGER when the user mentions embedded messaging deployment, embedded service deployment, MIAW deployment, messaging widget setup, chat widget configuration, embedded chat deployment, or references a .EmbeddedServiceConfig-meta.xml file. DO NOT TRIGGER when the user is creating a messaging channel (use service-digital-engagement-channel-configure), configuring legacy Live Agent embedded service, or generating the JavaScript code snippet for website embedding.
Integrate Tavus CVI into React apps using @tavus/cvi-ui components. Use when embedding conversations in web apps, customizing the video UI, using React hooks for CVI events, or building custom conversation interfaces with Vite/Next.js.
Guide for implementing deep linking in .NET MAUI apps. Covers Android App Links with intent filters, Digital Asset Links, and AutoVerify; iOS Universal Links with Associated Domains entitlements and Apple App Site Association files; custom URI schemes; and domain verification for both platforms. USE FOR: "deep linking", "app links", "universal links", "custom URI scheme", "intent filter", "Associated Domains", "Digital Asset Links", "open app from URL", "handle incoming URL", "domain verification". DO NOT USE FOR: in-app Shell navigation (use maui-shell-navigation), push notification handling (use maui-push-notifications), or web content embedding (use maui-hybridwebview).
WebAssembly runtime skill using wasmtime. Use when running WASM modules with wasmtime CLI, working with WASI preview2, using the component model, embedding wasmtime in Rust applications, limiting execution with fuel metering, or debugging WASM with DWARF in wasmtime. Activates on queries about wasmtime, WASI, WASM component model, wasmtime embedding, WIT interfaces, fuel metering, or server-side WebAssembly.
Person re-identification (ReID). Learns discriminative embeddings to match the same person across different camera views, based on metric learning. Use when training, evaluating, exporting, or running inference for a TAO person re-identification model. Trigger phrases include "train ReID", "person re-identification", "cross-camera person matching", "ReID embeddings", "person re-id".
Minimal text embedding smoke test for Model Studio embedding models.
Use when text embeddings are needed from Alibaba Cloud Model Studio models for semantic search, retrieval-augmented generation, clustering, or offline vectorization pipelines.
Redis vector search guidance covering HNSW vs FLAT algorithm choice, vector index configuration (dims, distance metric, datatype), filtered hybrid search combining vector similarity with TAG or NUMERIC filters, and the RAG retrieval pattern with RedisVL. Use when defining a VECTOR field in FT.CREATE, integrating embeddings (OpenAI, Cohere, sentence-transformers), tuning HNSW parameters (M, EF_CONSTRUCTION, EF_RUNTIME), building a retrieval-augmented generation pipeline, or filtering vector results by attribute.
Query integrated indexes using text with Pinecone MCP. IMPORTANT - This skill ONLY works with integrated indexes (indexes with built-in Pinecone embedding models like multilingual-e5-large). For standard indexes or advanced vector operations, use the CLI skill instead. Requires PINECONE_API_KEY environment variable and Pinecone MCP server to be configured.