Loading...
Loading...
Found 288 Skills
Generate deep links to the Arize UI. Use when the user wants a clickable URL to open a specific trace, span, session, dataset, labeling queue, evaluator, or annotation config.
Implements Syncfusion WinUI Cartesian Charts (SfCartesianChart) for data visualization in WinUI applications. Use this when working with column, line, bar, area, or financial charts (OHLC, Candle). This skill covers axis configuration, legends, tooltips, zooming/panning, data labels, and high-performance fast series for large datasets.
Implement and configure Syncfusion MultiColumnComboBox control in Windows Forms - an advanced combobox with multiple columns in dropdown and virtual data binding for large datasets. Use when creating dropdown lists with multiple data fields, DataSource binding, DisplayMember/ValueMember configuration, or column headers in dropdown. Covers filtered dropdown lists and replacing standard ComboBox with multi-column alternatives.
Fine-tune and serve Physical Intelligence OpenPI models (pi0, pi0-fast, pi0.5) using JAX or PyTorch backends for robot policy inference across ALOHA, DROID, and LIBERO environments. Use when adapting pi0 models to custom datasets, converting JAX checkpoints to PyTorch, running policy inference servers, or debugging norm stats and GPU memory issues.
Transform, filter, reshape, join, and manipulate football data. Use when the user needs to clean data, merge datasets, convert between formats, handle missing values, work with large datasets, or do any data manipulation task on football data.
Look up Y Combinator companies, batches, and startup ecosystem data using the yc-oss API (read-only). Use this skill whenever the user wants to research YC-backed startups, find companies in a specific batch or industry, check which YC companies are hiring, explore top YC companies, or analyze startup trends by sector or tag. Triggers include: "YC companies in fintech", "who's in the latest YC batch", "YC startups hiring", "top Y Combinator companies", "find YC companies tagged AI", "W25 batch", "S24 companies", "YC stats", "Y Combinator portfolio", "startup research", "which YC companies do X", "venture research on YC", any mention of Y Combinator, YC batch, or YC-backed companies in the context of startup research, venture analysis, or market intelligence. This is a read-only data source — the API is a static JSON dataset updated daily.
Write and edit AML/AQL code for Holistics models, datasets, dashboards, and metrics. Use this whenever the user wants to create or modify a model, dataset, dashboard page, AQL metric, or any file in an AMQL project.
Cohere integration. Manage Documents, Models, Datasets, Jobs. Use when the user wants to interact with Cohere data.
Zero-shot time series forecasting with Google's TimesFM foundation model. Use this skill when forecasting ANY univariate time series — sales, sensor readings, stock prices, energy demand, patient vitals, weather, or scientific measurements — without training a custom model. Supports both basic forecasting and advanced covariate forecasting (XReg) with dynamic and static exogenous variables. Automatically checks system RAM/GPU before loading the model, validates dataset fit before processing, supports CSV/DataFrame/array inputs, and returns point forecasts with calibrated prediction intervals. Includes a preflight system checker script that MUST be run before first use to verify the machine can load the model and handle your specific dataset.
Run vLLM performance benchmark using synthetic random data to measure throughput, TTFT (Time to First Token), TPOT (Time per Output Token), and other key performance metrics. Use when the user wants to quickly test vLLM serving performance without downloading external datasets.
Benchmark vLLM or OpenAI-compatible serving endpoints using vllm bench serve. Supports multiple datasets (random, sharegpt, sonnet, HF), backends (openai, openai-chat, vllm-pooling, embeddings), throughput/latency testing with request-rate control, and result saving. Use when benchmarking LLM serving performance, measuring TTFT/TPOT, or load testing inference APIs.
This is a skill for benchmarking the efficiency of automatic prefix caching in vLLM using fixed prompts, real-world datasets, or synthetic prefix/suffix patterns. Use when the user asks to benchmark prefix caching hit rate, caching efficiency, or repeated-prompt performance in vLLM.