Skill UI

This skill acts as an expert risk manager, providing comprehensive guidance on monitoring portfolio risk, calculating R-multiples, and optimizing position limits. It covers advanced quantitative finance topics such as Value at Risk (VaR), correlation analysis, and stress testing using Monte Carlo simulations. Use it for systematic risk assessment, developing hedging strategies (options/futures), and implementing rigorous risk controls in trading workflows.

This advanced workflow orchestrates complex, multi-node GPU clusters on Vast.ai. It is designed for large-scale distributed machine learning training, automatically handling spot instance preemption and resuming jobs from checkpoints. Furthermore, it provides comprehensive cost analysis by tracking billing history, allowing users to optimize GPU spending and manage cluster teardown efficiently.

This skill provides comprehensive management for annotation schemas (configs) and human review workflows (queues) within the Arize platform. It supports defining categorical, continuous, and freeform label types. Users can programmatically apply these annotations to project spans and datasets using the Python SDK, facilitating structured human feedback loops and improving model monitoring.

A powerful toolset for automating core PostHog product analytics tasks via Rube MCP. It enables developers and analysts to capture user events, list and filter event data, manage feature flags (creation, retrieval, listing), and inspect user profiles. Ideal for integrating advanced product tracking and A/B testing workflows directly into development pipelines.

This skill provides comprehensive commands to manage the entire AI model evaluation lifecycle using Arize. Users can list, retrieve metadata, and export results from experiments. It is ideal for comparing multiple model runs, benchmarking performance against a dataset, and analyzing model accuracy using structured evaluation workflows (CRUD operations).

Automates GA4 reporting and property management via Composio’s Rube MCP, covering account/property discovery, standard/batch/pivot/funnel reports, and key event workflows so analysts can run repeatable queries without manual UI steps.

LaminDB is an open-source lineage-native lakehouse designed for biological datasets and models. It enables queryable, traceable, and validated data management across various storage formats like S3 and Postgres. Users can track computational workflows, curate data with ontologies via Bionty, and ensure FAIR compliance. Ideal for managing scRNA-seq, spatial transcriptomics, and integrating ML pipelines while maintaining reproducibility and collaboration.

Provides comprehensive programmatic control over Apify's core data infrastructure, including Datasets, Key-Value Stores, and Request Queues. This skill allows users to perform CRUD operations on various storage types, manage data persistence, and orchestrate complex, multi-stage data pipelines (e.g., scrape -> transform -> export). Ideal for building robust, end-to-end web scraping and data processing automation workflows.

Utilize Bright Data's Scraping Browser with Playwright or Puppeteer to reliably scrape structured data from JavaScript-rendered pages, Single Page Applications (SPAs), and anti-bot-protected sites. This tool manages proxy routing, CAPTCHA solving, and fingerprint management, ensuring robust data collection from complex web environments.

This skill provides the core mechanism for integrating Hex into external data pipelines. It enables triggering, monitoring, and managing Hex projects (e.g., via Airflow or Dagster) programmatically. Users can implement parameterized runs, execute multi-step ETL workflows sequentially, and handle status polling and error management, making it ideal for production data orchestration.

This workflow automates the extraction of key insights from meeting recordings using Fathom transcripts and summaries. It builds a robust pipeline to identify action items, sync tasks to project management tools, and analyze overall meeting patterns, streamlining post-meeting follow-up and record-keeping.

Master the creation of robust ELT (Extract, Load, Transform) data pipelines within Snowflake. This guide demonstrates advanced data orchestration using Snowflake Streams for Change Data Capture (CDC), Tasks for scheduled transformations and DAG management, and Dynamic Tables for declarative, auto-refreshing data models. Ideal for building sophisticated, scalable data warehousing solutions.