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Found 342 Skills
Comprehensive backend development guide for Langfuse's Next.js 14/tRPC/Express/TypeScript monorepo. Use when creating tRPC routers, public API endpoints, BullMQ queue processors, services, or working with tRPC procedures, Next.js API routes, Prisma database access, ClickHouse analytics queries, Redis queues, OpenTelemetry instrumentation, Zod v4 validation, env.mjs configuration, tenant isolation patterns, or async patterns. Covers layered architecture (tRPC procedures → services, queue processors → services), dual database system (PostgreSQL + ClickHouse), projectId filtering for multi-tenant isolation, traceException error handling, observability patterns, and testing strategies (Jest for web, vitest for worker).
Integrates SAP Cloud SDK for AI into JavaScript/TypeScript and Java applications. Use when building applications with SAP AI Core, Generative AI Hub, or Orchestration Service. Covers chat completion, embedding, streaming, function calling, content filtering, data masking, document grounding, prompt registry, and LangChain/Spring AI integration. Supports OpenAI GPT-4o, Claude, Gemini, Amazon Nova, and other foundation models via SAP BTP.
Download videos, audio, playlists, and channels from YouTube and 1000+ websites using yt-dlp. Supports quality selection, format conversion, subtitle download, playlist filtering, metadata extraction, thumbnail download, and batch operations. Use when downloading YouTube videos in any quality (4K, 8K, HDR), extracting audio as MP3/M4A/FLAC, downloading entire playlists/channels, getting subtitles in multiple languages, converting to specific formats, downloading live streams, archiving content, or batch processing multiple URLs. Optimized for reliability with automatic retries, rate limiting, and error handling.
Protein Dynamics, Evolution, and Structure analysis. Specialized in Normal Mode Analysis (NMA) using Anisotropic (ANM) and Gaussian Network Models (GNM). Features tools for structural ensemble analysis, PCA, and co-evolutionary analysis (Evol). Use for protein flexibility prediction, collective motions, structural ensemble comparison, hinge region identification, binding site analysis, MD trajectory filtering, and evolutionary analysis.
ESM2 protein language model for embeddings and sequence scoring. Use this skill when: (1) Computing pseudo-log-likelihood (PLL) scores, (2) Getting protein embeddings for clustering, (3) Filtering designs by sequence plausibility, (4) Zero-shot variant effect prediction, (5) Analyzing sequence-function relationships. For structure prediction, use chai or boltz. For QC thresholds, use protein-qc.
Automate Todoist task management, projects, sections, filtering, and bulk operations via Rube MCP (Composio). Always search tools first for current schemas.
JSON querying, filtering, and transformation with jq command-line tool. Use when working with JSON data, parsing JSON files, filtering JSON arrays/objects, or transforming JSON structures.
Transform song lyrics into vivid visual scene descriptions and image generation prompts — filtering for concrete imagery and rendering each distinct scene as a numbered canvas.
Install and extend data-table-filters — a React data table system with faceted filters (checkbox, input, slider, timerange), sorting, infinite scroll, virtualization, and BYOS state management. Delivered as 9 shadcn registry blocks installable via `npx shadcn@latest add`. Use when: (1) installing data-table-filters from the shadcn registry, (2) adding extension blocks (command palette, cell renderers, sheet panel, store adapters, schema system, Drizzle helpers, query layer), (3) configuring store adapters (nuqs/zustand/memory), (4) generating table schemas from a data model, (5) wiring up server-side filtering with Drizzle ORM, (6) connecting the React Query fetch layer, (7) troubleshooting integration issues. Triggers on mentions of "data-table-filters", "data-table.openstatus.dev", filterable data tables with shadcn, or any of the registry block names.
Enable and configure Kibana audit logging for saved object access, logins, and space operations. Use when setting up Kibana audit, filtering events, or correlating Kibana and ES audit logs.
Execute read-only T-SQL queries against Fabric Data Warehouse, Lakehouse SQL Endpoints, and Mirrored Databases via CLI. Default skill for any lakehouse data query (row counts, SELECT, filtering, aggregation) unless the user explicitly requests PySpark or Spark DataFrames. Use when the user wants to: (1) query warehouse/lakehouse data, (2) count rows or explore lakehouse tables, (3) discover schemas/columns, (4) generate T-SQL scripts, (5) monitor SQL performance, (6) export results to CSV/JSON. Triggers: "warehouse", "SQL query", "T-SQL", "query warehouse", "show warehouse tables", "show lakehouse tables", "query lakehouse", "lakehouse table", "how many rows", "count rows", "SQL endpoint", "describe warehouse schema", "generate T-SQL script", "warehouse performance", "export SQL data", "connect to warehouse", "lakehouse data", "explore lakehouse".
A Tushare data research skill for Chinese natural language. It converts requests like "How has this stock been performing lately?", "Help me check the financial report trend", "Which sector is the strongest recently?", "What are northbound funds buying?", "Export a market data report for me" into executable workflows for data acquisition, cleaning, comparison, filtering, export, and brief analysis. It applies to research scenarios such as A-shares, indices, ETFs/funds, finance, valuation, capital flows, announcements & news, sector concepts, and macroeconomic data.