Loading...
Loading...
Found 208 Skills
Semantic search for Marp presentations using vector embeddings. Use when finding relevant slides by topic, retrieving slide content, or exploring presentation materials. Triggers on "find slides about...", "search presentations for...", "get slide content", "what slides cover...", or any Marp/presentation search query.
Configure pgvector extension for vector search in Supabase - includes embedding storage, HNSW/IVFFlat indexes, hybrid search setup, and AI-optimized query patterns. Use when setting up vector search, building RAG systems, configuring semantic search, creating embedding storage, or when user mentions pgvector, vector database, embeddings, semantic search, or hybrid search.
Semantic and multi-modal search across documents using LanceDB vector embeddings. Use when searching knowledge bases, finding information semantically, ingesting documents for RAG, or performing vector similarity search. Triggers on "search documents", "semantic search", "find in knowledge base", "vector search", "index documents", "LanceDB", or RAG/embedding operations.
This skill should be used when building data processing pipelines with CocoIndex v1, a Python library for incremental data transformation. Use when the task involves processing files/data into databases, creating vector embeddings, building knowledge graphs, ETL workflows, or any data pipeline requiring automatic change detection and incremental updates. CocoIndex v1 is Python-native (supports any Python types), has no DSL, and is currently under pre-release (version 1.0.0a1 or later).
プロダクトデモ動画を自動生成。百聞は一見にしかず、を体現。Use when user mentions '/generate-video', video generation, product demos, or visual documentation. Do NOT load for: embedding video players, live demos, video playback features. Requires Remotion setup.
Visualizes datasets in 2D using embeddings with UMAP or t-SNE dimensionality reduction. Use when exploring dataset structure, finding clusters, identifying outliers, or understanding data distribution.
Expert Guide to Indie Game Development with Go (Golang) and its game engines (such as Ebitengine or Raylib-go). Covers core areas including memory management, concurrency-safe game loops, asset embedding (go:embed), and cross-platform cross-compilation. Suitable for scenarios where high-performance 2D/3D games are built using the Go ecosystem.
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).
Guidance for embedding web content in .NET MAUI apps using HybridWebView, including JavaScript–C# interop, bidirectional communication, raw messaging, and trimming/NativeAOT considerations. USE FOR: "HybridWebView", "JavaScript interop", "embed web content", "JS to C# interop", "C# to JavaScript", "web view interop", "raw message", "InvokeJavaScriptAsync", "web content MAUI". DO NOT USE FOR: deep linking from external URLs (use maui-deep-linking), REST API calls (use maui-rest-api), or Blazor Hybrid apps (different from HybridWebView).
Standardizes release approvals with GitHub-aware checklists. Use when preparing releases, validating deployment gates, conducting release reviews, embedding release gate snippets in PRs. Do not use when weekly status updates - use github-initiative-pulse. DO NOT use when: code reviews - use pensive review skills.
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.