Loading...
Loading...
Found 278 Skills
Comprehensive guide to Spark Structured Streaming for production workloads. Use when building streaming pipelines, implementing real-time data processing, handling stateful operations, or optimizing streaming performance.
Build professional financial services data packs from various sources including CIMs, offering memorandums, SEC filings, web search, or MCP servers. Extract, normalize, and standardize financial data into investment committee-ready Excel workbooks with consistent structure, proper formatting, and documented assumptions. Use for M&A due diligence, private equity analysis, investment committee materials, and standardizing financial reporting across portfolio companies. Do not use for simple financial calculations or working with already-completed data packs.
Combining IoT sensor data using algorithms like Kalman filters for improved accuracy and reliability
This Skill supports screening qualified stocks based on stock selection criteria (such as market indicators, financial indicators, etc.); it allows querying stocks, listed companies within specified industries/sectors, as well as component stocks of sector indices; it also supports related tasks such as stock, listed company, and sector/index recommendations, avoiding the use of outdated information by large models during stock selection.
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).
Generate falsifiable trade strategy hypotheses from market data, trade logs, and journal snippets. Use when you have a structured input bundle and want ranked hypothesis cards with experiment designs, kill criteria, and optional strategy.yaml export compatible with edge-finder-candidate/v1.
Scalable data processing for ML workloads. Streaming execution across CPU/GPU, supports Parquet/CSV/JSON/images. Integrates with Ray Train, PyTorch, TensorFlow. Scales from single machine to 100s of nodes. Use for batch inference, data preprocessing, multi-modal data loading, or distributed ETL pipelines.
Detect IBD-style Distribution Days for QQQ/SPY (close down at least 0.2% on higher volume), track 25-session expiration and 5% invalidation, count d5/d15/d25 clusters, classify market risk (NORMAL/CAUTION/HIGH/SEVERE), and emit TQQQ/QQQ exposure recommendations. Use after market close, before TQQQ exposure changes, or as input to FTD/market-state frameworks. Does not execute trades.
Find substitute materials using CWICR data. Identify equivalent alternatives based on function, cost, and availability.
Assess construction data quality using completeness, accuracy, consistency, timeliness, and validity metrics. Automated validation with regex patterns, thresholds, and reporting.
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`
Zero-shot time series forecasting with Google's TimesFM foundation model. Use for any univariate time series (sales, sensors, energy, vitals, weather) without training a custom model. Supports CSV/DataFrame/array inputs with point forecasts and prediction intervals. Includes a preflight system checker script to verify RAM/GPU before first use.