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Found 1,005 Skills
Use when you need to implement or improve Java logging and observability — including selecting SLF4J with Logback/Log4j2, applying proper log levels (ERROR, WARN, INFO, DEBUG, TRACE), parameterized logging, secure logging without sensitive data exposure, environment-specific configuration, log aggregation and monitoring, or validating logging through tests. This should trigger for requests such as Improve logging; Apply logging; Refactor logging; Add logging support. Part of cursor-rules-java project
Data export to CSV, Excel (XLSX), and JSON. ExcelJS, SheetJS (xlsx), Papa Parse, Apache POI (Java), openpyxl (Python). Streaming exports for large datasets. USE WHEN: user mentions "export CSV", "export Excel", "XLSX generation", "download spreadsheet", "ExcelJS", "SheetJS", "Papa Parse", "data export" DO NOT USE FOR: PDF generation - use `pdf-generation`; file upload/download - use `file-upload`/`cloud-storage`
Use this skill when the user asks about Goldsky Edge — the managed RPC endpoint service for EVM chains. Triggers on: 'Edge RPC', 'Goldsky RPC endpoint', 'edge.goldsky.com', 'eth_getLogs is slow', 'RPC rate limit', 'hedged requests', 'flashblocks', 'HyperEVM system transactions', 'x402 pay-per-request RPC', 'Goldsky Edge pricing', 'Edge dashboard', 'gs_edge_ API key', 'rpc-edge'. Also use this skill when the user wants a resilient, low-latency JSON-RPC endpoint for EVM chains (Ethereum, Base, Arbitrum, Optimism, Polygon zkEVM, BSC, Avalanche, Berachain, HyperEVM, Monad, Sei, Sonic, Unichain, zkSync, etc.), is debugging RPC errors like -32005/-32012/-32014/-32015/-32016, or is comparing providers (Alchemy, Infura, QuickNode, Ankr) against Edge. For questions about self-hosting eRPC or custom eRPC configuration beyond what Edge exposes, point them at https://docs.erpc.cloud/llms.txt. Do NOT trigger on Goldsky Mirror, Turbo, or Subgraph pipeline questions — those belong to their respective skills.
DataWorks metadata Skill for Alibaba Cloud — browse Data Map metadata and perform non-destructive writes via Aliyun CLI. READ scope: list/get catalogs, databases, tables, columns, partitions; query data lineage (upstream/downstream impact); list/get datasets & versions; list/get metadata collections (Category/Album) and entities inside them; preview dataset version content. WRITE scope (non-destructive only): update table & column business metadata; register lineage relationships; create/update datasets and versions; create/update metadata collections and add entities to them. This Skill exposes NO delete or remove APIs — every `delete-*` and `remove-*` operation is intentionally out of scope. For deletions, use the DataWorks console. Triggers: "dataworks metadata", "data map", "data lineage", "meta collection", "dataset", "catalog", "table info", "column info", "partition", "impact analysis", "register lineage", "create dataset", "update business metadata".
ALWAYS use when: creating/editing marimo notebooks, working with any .py file containing @app.cell decorators, building reactive Python notebooks, doing exploratory data analysis in notebook form, converting Jupyter (.ipynb) to marimo, or when user mentions "marimo", "reactive notebook", or asks for an interactive Python notebook. Covers marimo CLI (edit, run, convert, export), UI components (mo.ui.*), layout functions, SQL integration, caching, state management, and wigglystuff widgets. If a task involves notebooks and Python, invoke this skill first.
Implements Syncfusion ASP.NET Core TreeGrid for hierarchical data with sorting, filtering, editing, exporting, paging, virtual scrolling, and advanced features. Supports configuration, CRUD, aggregates, templates, state persistence, and performance optimization in ASP.NET Core applications.
Use when operating the vigolium CLI for web vulnerability scanning, security testing, traffic ingestion, server management, AI agent-driven scanning and code review, cloud-storage management, or writing custom JavaScript extensions. Invoke for scan commands, scan-url, scan-request, run, ingest, server, agent (query/autopilot/swarm/olium/piolium/audit/session), traffic browsing, database queries, storage uploads/downloads, module management, extension scripting, export, project management, and configuration tuning.
Cross-cutting reliability patterns for PubNub apps. Covers reconnect with exponential backoff + jitter, idempotent publish with client-generated message IDs, dedup-on-merge for live + history streams, queue-and-retry for offline writes, and schema versioning of message envelopes. Use during design reviews, when planning offline support, or during incident response when network or delivery reliability is the concern.
Audit your biggest closed-won deals to find your PROVEN ideal customer profile, then find more accounts like them. Use whenever someone wants to analyze won deals, audit their best customers, see which companies generated the most revenue, find their real ICP, build a look-alike target list, segment customers by what actually pays, or learn which acquisition channel produced their best revenue. Triggers on: 'audit my biggest deals', 'which customers made us the most money', 'analyze my closed-won', 'what's my proven ICP', 'find more customers like my best ones', 'look-alike accounts', 'HubSpot deal analysis', 'revenue by account', 'which channel generated my best deals', 'acquisition source analysis'. For RevOps, Heads of Sales/Marketing, founders and growth leads doing ICP refinement, account-based targeting or pipeline/QBR review. Reads HubSpot via its MCP or a CSV export, then hands the profile to sales-nav-search-builder to generate the prospecting search. Maintained by La Growth Machine.
DINO (DETR with Improved DeNoising Anchor Boxes) for 2D object detection. Transformer-based detector with denoising training, multi-scale features, and optional distillation support. Use when training, evaluating, exporting, distilling, quantizing, or running inference for a TAO DINO detector. Trigger phrases include "train DINO", "DETR object detection", "TAO 2D detection", "DINO with distillation".
Pose classification using ST-GCN (Spatial Temporal Graph Convolutional Network). Classifies skeleton sequences into action categories from pose-keypoint data. Use when training, evaluating, exporting, or running inference for a TAO pose-classification model. Trigger phrases include "train pose classification", "skeleton action recognition", "ST-GCN", "keypoint sequence classifier".
Use this skill to bring any vision model from HuggingFace or NVIDIA NGC into an NVIDIA DeepStream pipeline with end-to-end automation: ONNX download, SafeTensors export, TRT engine build, custom nvinfer bbox parser, multi-stream benchmark, and PDF report. Object detection models only.