Skill UI

Guides you through building Airflow operators for data pipelines with automated best practices, production-ready code/config generation, and validation for ETL, transformation, orchestration, and streaming scenarios.

A comprehensive skill for analyzing Non-Fungible Tokens (NFTs). It fetches collection metadata from APIs (like OpenSea), parses trait attributes, and calculates advanced rarity scores using multiple algorithms (e.g., rarity_score, information entropy). Use this tool to rank tokens by uniqueness, compare specific NFTs, or analyze the distribution of traits within an entire collection, outputting results in JSON or CSV format. Ideal for crypto research and market due diligence.

This powerful tool calculates detailed cryptocurrency tax obligations by processing transaction history from multiple exchanges. It supports various cost basis methods (FIFO, LIFO, HIFO) to accurately determine capital gains and losses. Key features include identifying staking, airdrop, and mining income, and generating comprehensive reports compatible with Form 8949, simplifying complex tax filing processes.

Automates creating and validating GCP BigQuery views with best-practice guidance, production-ready code snippets, and integrations for Vertex AI, Cloud Run, Firebase, and similar services to keep analytics workloads consistent.

Automates common CTE query builder tasks for data analytics, offering best-practice guidance, production-ready SQL snippets, and validation against standard requirements when you need help with BI, visualization, or statistical analysis.

This skill details how to set up automated CI/CD pipelines for Databricks data pipelines. It leverages Declarative Automation Bundles (DABs) and GitHub Actions to manage the entire deployment lifecycle. Key functionalities include running unit tests on pull requests, validating and deploying to staging environments, and promoting validated code to production upon merge, ensuring reliable and automated data infrastructure.

This skill manages the complete lifecycle of Fireflies.ai meeting transcripts. It allows users to fetch raw data, export in various formats (JSON, CSV, SRT), sanitize sensitive personal information (PII Redaction), and enforce data retention policies to ensure compliance with regulations like GDPR and CCPA. Ideal for data governance and compliance workflows.

Configure automated Continuous Integration and Continuous Deployment (CI/CD) pipelines for Juicebox AI data analysis integrations. This setup utilizes GitHub Actions to run comprehensive unit tests (with mocked datasets) and integration tests (connecting to live APIs) on every PR and merge to main. It ensures that data upload logic, analysis execution, and result parsing workflows are robust and reliable before deployment.

This guide provides advanced strategies for optimizing interactions with the Juicebox AI API. It covers crucial best practices such as implementing caching mechanisms (with defined TTLs), executing batch operations for efficiency, managing rate limits, and optimizing data chunking for large datasets. Following these steps can significantly reduce end-to-end analysis time and ensure responsive, scalable data workflows.

Track experiments, catalog models, compare versions, and deploy trained artifacts with MLflow’s framework-agnostic lifecycle tools across PyTorch, TensorFlow, and other stacks.

NeMo Curator streams GPU-accelerated curation for LLM training, with super-fast fuzzy deduplication, multimodal quality filters, semantic dedupl, PII redaction, NSFW checks, and RAPIDS-scaled pipelines for prepping massive corpora.

Auto-activating ML training skill that kicks in whenever you mention optuna study creator, then guides you through data prep, model training, hyperparameter tuning, and experiment tracking with production-ready code and validations.