Skill UI

This skill models the expertise of a senior data scientist, covering the entire data science lifecycle. It provides advanced capabilities in statistical modeling, machine learning (deep learning, XGBoost), predictive analytics, and business intelligence. From raw data exploration (EDA) to deploying production-ready models and creating actionable dashboards, it covers technical depth across Python, R, SQL, and cloud platforms, while applying domain knowledge in finance, marketing, and operations.

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

A comprehensive tool for analyzing social media campaign performance. It calculates key metrics such as Engagement Rate, ROI, CTR, and provides industry benchmarks across major platforms (Instagram, Facebook, LinkedIn, etc.). Use this when auditing campaigns, optimizing ad spend, or measuring overall marketing effectiveness.

A comprehensive toolkit designed to track and analyze content performance, engagement metrics, and Return on Investment (ROI) across major social media platforms. By utilizing specialized Apify Actors, this skill extracts deep insights from Instagram, Facebook, YouTube, TikTok, and more, helping users identify high-performing content, monitor growth, and measure marketing campaign effectiveness.

Provides a detailed, multi-platform workflow to monitor long-running computational experiments. It checks the status of jobs across various environments (SSH, vast.ai, Modal), collects screen logs, fetches structured JSON results, and pulls detailed training metrics (loss curves, accuracy) from Weights & Biases (W&B). Essential for MLOps and automated result review.

This comprehensive tool manages the complete lifecycle of ML experiments, providing robust deployment capabilities across diverse environments. It supports running training jobs on local GPUs, dedicated remote servers, Vast.ai, and serverless platforms like Modal. The workflow handles environment detection, code synchronization (rsync/git), dependency pinning, and integrates advanced logging tools (e.g., W&B), ensuring reliable, scalable, and reproducible ML training runs.

An advanced tool for network intelligence that calculates weighted graph scores to map the optimal path to a target. It identifies potential introducers, measures 'bridge value' across connections, and helps determine whether a connection requires a warm introduction or direct outreach on platforms like X and LinkedIn. Ideal for users needing the raw ranking methodology.

This workflow utilizes the SerpApi to perform advanced, multi-engine searches across platforms including YouTube, Bing, Google News, Google Shopping, and Google Maps. It is essential for data scraping, SEO research, and building comprehensive search features that require aggregating results from diverse sources beyond standard Google search. Use it to collect videos metadata, product pricing, local business details, and news articles efficiently.

This comprehensive guide provides blueprints for implementing optimal Snowflake data architectures. It covers diverse patterns, including traditional data warehouses, Lakehouse designs using Iceberg tables, decentralized Data Mesh structures, and advanced data sharing and Snowpark native patterns. Use this when designing enterprise data platforms across varying scales.

This advanced helper assists users in writing, debugging, and validating complex formulas, pivot tables, and array functions for platforms like Excel and Google Sheets. It ensures formulas are robust by handling common edge cases (e.g., blank rows, mixed data types, time zone quirks) and provides detailed, worked examples. It also manages cross-platform dialect translation.

This skill provides a highly rigorous framework for analyzing complex public issues, platform governance, and institutional compliance. It is designed to structure evidence boundaries, assess procedural justice, and track public commitments by synthesizing source ledgers and multiple data points. It is intended as a specialized sub-routine triggered by the main CrossFrame suite, not for standalone use. Key focus areas include source verification, identifying weak signals, and mitigating AI compliance performance risks.

Velociraptor is a powerful open-source platform for endpoint visibility and digital forensics. It allows users to deploy agents across thousands of endpoints and execute sophisticated VQL (Velociraptor Query Language) queries. This enables large-scale, on-demand forensic artifact collection, TTP hunting, and continuous monitoring during incident response, even on air-gapped or unmanaged systems.