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Found 1,354 Skills
Guide for modernizing and migrating MSBuild project files to SDK-style format. Only activate in MSBuild/.NET build context. Use when encountering legacy .csproj/.vbproj files with verbose XML, packages.config, or AssemblyInfo.cs patterns. Covers legacy-to-SDK migration, removing boilerplate, PackageReference migration, and Directory.Build consolidation. Invoke when asked to modernize, migrate, or clean up project files.
Used to hide or restore skills; supports single or batch operations; manages skill parent folder paths; prevents self-hiding; supports multi-directory search and specified directory operations; supports one-click hiding/restoring of all skills. Use this skill when users need to hide or restore skills.
Analyze stock correlations to find related companies and trading pairs. Use this skill whenever the user asks about correlated stocks, related companies, sector peers, trading pairs, or how two or more stocks move together. Triggers include: "what correlates with NVDA", "find stocks related to AMD", "correlation between AAPL and MSFT", "what moves with", "sector peers", "pair trading", "correlated stocks", "when NVDA drops what else drops", "find me a pair for", "stocks that move together", "beta to", "relative performance", "which stocks follow AMD", "supply chain partners", "correlation matrix", "co-movement", "related tickers", "sympathy plays", "if GOOGL moves what else moves", "semiconductor peers", "compare correlation", "hedging pair", "sector clustering", "realized correlation", "rolling correlation", or any request about finding stocks that move in tandem or inversely. Also triggers when the user mentions well-known pairs like AMD/NVDA, GOOGL/AVGO, LITE/COHR and wants to understand or find similar relationships. Always use this skill even if the user only provides one ticker — infer that they want to find correlated peers.
This skill should be used when the user asks to "implement dependency injection in Python", "use the dependency-injector library", "decouple Python components", "write testable Python services", or needs guidance on Inversion of Control, DI containers, provider types, and wiring in Python applications.
This skill should be used when the user asks to "connect to Turso", "use libSQL", "set up a Turso database", "query Turso with TypeScript", or needs guidance on Turso Cloud, embedded replicas, or vector search with libSQL.
Apply when implementing asynchronous payment methods (Boleto, Pix, bank redirects) or working with callback URLs in payment connector code. Covers undefined status response, callbackUrl notification, X-VTEX-signature validation, sync vs async handling, and the 7-day retry window. Use for any payment flow where authorization does not complete synchronously.
Use when the user needs to integrate OpenClaw with Alibaba Cloud SLS/Observability, including collector setup, machine groups, indexes, dashboards, collection configs, or Logtail bindings on Linux.
Reference HLSL shader implementations for the Slug font rendering algorithm, enabling high-quality GPU-accelerated vector font and glyph rendering.
Authors and structures professional-grade agent skills following the agentskills.io spec. Use when creating new skill directories, drafting procedural instructions, or optimizing metadata for discoverability. Don't use for general documentation, non-agentic library code, or README files.
Install Orderly SDK packages and related dependencies (hooks, UI, features, wallet connectors) using the preferred package manager.
Convert raster images (photos, illustrations, AI-generated art) into high-quality SVG recreations. Breaks the image into isolated features, builds each as a standalone SVG layer, then composites them. Use when the user wants to recreate an image as SVG, create vector versions of artwork, or extract specific elements from images as scalable graphics.
Use when cognee is a Python AI memory engine that transforms documents into knowledge graphs with vector and graph storage for semantic search and reasoning. Use this skill when writing code that calls cognee's Python API (add, cognify, search, memify, config, datasets, prune, session) or integrating cognee-mcp. Covers the full public API, SearchType modes, DataPoint custom models, pipeline tasks, and configuration for LLM/embedding/vector/graph providers. Do NOT use for general knowledge graph theory or unrelated Python libraries.