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Found 316 Skills
Use this skill when users ask how to build or customize Syncfusion PivotView pivot tables in React. Trigger for React pivot grid/OLAP, aggregation, data binding (JSON/remote), drill-down/drill-through, grouping, filtering, conditional formatting, exports (Excel/PDF/CSV), or pivot charts. React-only, not Angular/Vue/Blazor.
Implements Syncfusion Angular Grid component for feature-rich data tables and grids. Use this when working with data display, sorting, filtering, grouping, aggregates, editing, or exporting. This skill covers grid configuration, CRUD operations, virtual scrolling or infinite scrolling, hierarchy grids, state persistence, and advanced data management features for data-intensive applications.
Use this skill for ASP.NET Core apps needing Excel-like UI using the Syncfusion Spreadsheet Component. Trigger for creating, viewing, editing Excel (.xlsx, .xls, .xlsb) and CSV files; embedding spreadsheet editors; data binding from APIs/JSON; using formulas, charts, validation, filtering, or conditional formatting. Also trigger when users reference spreadsheet files ("open xlsx", "load Excel file", "add Syncfusion spreadsheet", "bind data to spreadsheet"). Do NOT trigger for standalone file processing without UI components.
Create and manipulate Excel workbooks using Syncfusion Flutter XlsIO library. Supports two modes — generate Dart code for the user's Flutter project or provide code snippets. Use when the user mentions Excel creation, worksheets, cells, formatting, filtering, hyperlinks, or Syncfusion Flutter XlsIO.
Guides implementation of the Syncfusion WinForms AutoComplete control for text input with auto-suggestion functionality. Use when users want to add autocomplete textboxes, implement auto-suggestion features, create search boxes with suggestions, enable URL/email autocomplete, or build type-ahead search functionality in Windows desktop applications. Covers data binding, customization, filtering, multi-column dropdowns, events, and all AutoComplete-specific features.
Log queries, filtering, pattern analysis, and log correlation. Search and analyze application and infrastructure logs.
Resolves experiment references from natural language to concrete experiment IDs. Handles name lookups, fuzzy descriptions ('the signup experiment', 'my latest experiment'), status filtering, and disambiguation when multiple experiments match. TRIGGER when: user refers to an experiment by name, description, or relative reference ('latest', 'most recent', 'the one I created yesterday') and you don't already have the experiment ID. DO NOT TRIGGER when: user provides an experiment ID directly, or you already resolved the experiment earlier in the conversation.
Creates data table patterns with filtering, sorting, pagination, row actions, column configuration, server/client rendering modes, and empty/loading states. Use when building "data tables", "list views", "admin tables", or "data grids".
API contract design conventions for FastAPI projects with Pydantic v2. Use during the design phase when planning new API endpoints, defining request/response contracts, designing pagination or filtering, standardizing error responses, or planning API versioning. Covers RESTful naming, HTTP method semantics, Pydantic v2 schema naming conventions (XxxCreate/XxxUpdate/XxxResponse), cursor-based pagination, standard error format, and OpenAPI documentation. Does NOT cover implementation details (use python-backend-expert) or system-level architecture (use system-architecture).
Performs quality control on single-cell RNA-seq data (.h5ad or .h5 files) using scverse best practices with MAD-based filtering and comprehensive visualizations. Use when users request QC analysis, filtering low-quality cells, assessing data quality, or following scverse/scanpy best practices for single-cell analysis.
Excel to CSV conversion skill. Convert specific bounding tables or entire worksheets within `.xlsx` or `.xls` binary formats into flat `.csv` tabular data. Use this when you find an Excel file and need its data mapped into an accessible format for text analysis, filtering, or programmatic pipelining.
Amazon Movers & Shakers data acquisition tool. This skill is used when users need to find hot products with recently soaring sales on Amazon, discover bestseller trends, and obtain the soaring list product list. It supports filtering by category, outputs data such as basic product information, price, ranking trend, etc., providing original market data for Temu product selection.