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Found 373 Skills
Implement advanced filtering and sorting capabilities for APIs with query parsing, field validation, and optimization. Use when building search features, complex queries, or flexible data retrieval endpoints.
Builds flexible API filtering and sorting systems with query parameter parsing, validation, and security. Use when implementing search endpoints, building data grids, or creating dynamic query APIs.
Discover and list all URLs on a website, with optional search filtering. Use this skill when the user wants to find a specific page on a large site, list all URLs, see the site structure, find where something is on a domain, or says "map the site", "find the URL for", "what pages are on", or "list all pages". Essential when the user knows which site but not which exact page.
Search UX, filter patterns, sort options, and empty states. Use when building search interfaces or filterable lists.
Search the web using Tavily API for high-quality, AI-optimized results with advanced filtering options. Use when you need structured search results, domain filtering, relevance scores, or AI-generated answer summaries. Requires TAVILY_API_KEY. Keywords: tavily, advanced search, filtered search, domain filtering, relevance scoring.
This skill should be used when docs-researcher agent needs guidance on "how to search documentation", "WebSearch query patterns", "filtering search results", "documentation research strategy", or "creating knowledge files". Provides systematic methodology for effective technical documentation research.
Query TradingView screener data for HK, A-share, A-share ETF, and US symbols with deepentropy/tvscreener. Use for stock lookup, technical indicators (price/change/RSI/MACD/volume), symbol filtering, and custom field/filter-based market queries.
Search arXiv for academic papers. Use when users want to find research papers, preprints, or academic articles on any topic. Supports filtering by date, category, and author.
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.
Tavily: web search optimized for AI agents, answer synthesis, domain filtering, depth control
Generate synthetic training data when you don't have enough real examples. Use when you're starting from scratch with no data, need a proof of concept fast, have too few examples for optimization, can't use real customer data for privacy or compliance, need to fill gaps in edge cases, have unbalanced categories, added new categories, or changed your schema. Covers DSPy synthetic data generation, quality filtering, and bootstrapping from zero.
Configures Laravel Nightwatch data collection, sampling rates, filtering rules, and redaction policies. Use when setting up Nightwatch, managing data volume, protecting sensitive data (PII), or optimizing event collection for production workloads.