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Found 331 Skills
Comprehensive guide for implementing Syncfusion WPF Range Selector (SfDateTimeRangeNavigator) for time-bound data visualization with interactive scrolling, zooming, and range selection. Use this when working with range selectors, date-time range navigation, or time-bound data visualization. This skill covers interactive data range selection, chart range zooming, and dashboard time navigation features for large time-based datasets in WPF applications.
Assess data quality with checks for missing values, duplicates, type issues, and inconsistencies. Use for data validation, ETL pipelines, or dataset documentation.
Generates a Jupyter notebook that evaluates a fine-tuned SageMaker model using LLM-as-a-Judge. Use when the user says "evaluate my model", "how did my model perform", "compare models", or after a training job completes. Supports built-in and custom evaluation metrics, evaluation dataset setup, and judge model selection.
Generates a Jupyter notebook that transforms datasets between ML schemas for model training or evaluation. Use when the user says "transform", "convert", "reformat", "change the format", or when a dataset's schema needs to change to match the target format — always use this skill for format changes rather than writing inline transformation code. Supports OpenAI chat, SageMaker SFT/DPO/RLVR, HuggingFace preference, Bedrock Nova, VERL, and custom JSONL formats from local files or S3.
Build and deploy new Goldsky Turbo pipelines from scratch. Triggers on: 'build a pipeline', 'index X on Y chain', 'set up a pipeline', 'track transfers to postgres', or any request describing data to move from a chain/contract to a destination (postgres, clickhouse, kafka, s3, webhook). Covers the full workflow: requirements → dataset selection → YAML generation → validation → deploy. Not for debugging (use /turbo-doctor) or syntax lookups (use /turbo-pipelines).
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.
Read any data file (CSV, JSON, Parquet, Avro, Excel, spatial, SQLite) or remote URL (S3, HTTPS). Use when user references a data file, asks "what's in this file", or wants to preview/profile a dataset. Not for source code.
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.
Tableau platform help — Tableau Desktop, Tableau Cloud, Tableau Server, Tableau Prep, Tableau Pulse, Embedding API, REST API (v3.28, PAT/JWT auth, 300+ endpoints), MCP server, and Tableau+. Use when dashboards are slow with large datasets, LOD expressions or calculated fields aren't working, licensing costs are confusing or spiraling, Tableau won't connect to Salesforce or your data warehouse, embedded analytics aren't rendering, Tableau Prep flows keep failing, or you need help choosing Creator vs Explorer vs Viewer licenses. Do NOT use for general CRM config (use /sales-salesforce) or sales forecasting methodology (use /sales-forecast).
Hugging Face integration. Manage Models, Datasets, Spaces. Use when the user wants to interact with Hugging Face data.
Spatial data gridding and interpolation with a machine-learning style API. Process geographic and Cartesian point data onto regular grids. Use when Claude needs to: (1) Grid scattered spatial data onto regular grids, (2) Interpolate point data using splines, linear, or cubic methods, (3) Process geographic coordinates with projections, (4) Reduce large datasets using block averaging, (5) Remove polynomial trends from spatial data, (6) Cross-validate gridding parameters, (7) Create processing pipelines with Chain, (8) Grid vector data like GPS velocities.
Use when analyzing research datasets, cleaning tabular data, selecting statistical tests, producing result tables, creating publication figures, or moving notebook logic into reproducible code.