Skill UI

Firecrawl provides a comprehensive API for web data harvesting. It enables users to perform single-page scraping, multi-page crawling, site mapping (URL discovery), and advanced structured data extraction using Large Language Models (LLMs). Ideal for integrating data pipelines, building web monitoring tools, or conducting market research.

PathML is a Python toolkit for computational pathology workflows, loading 160+ slide formats, preprocessing H&E images, constructing spatial graphs, training HoVer-Net/HACTNet models, and analyzing multiplex imaging for ML-ready pathology data.

A comprehensive guide for defining, tracking, and interpreting product metrics across all stages—from discovery to maturity. This skill covers selecting metric frameworks (AARRR, HEART), defining stage-appropriate KPIs, designing multi-layered dashboards, and executing deep analyses like cohort and retention modeling. It helps teams transform raw data into actionable product strategies, moving beyond vanity metrics.

Systematically evaluates cyber-campaign evidence to attribute a cyber operation to a specific threat actor. This skill utilizes advanced analytical frameworks like the Diamond Model and Analysis of Competing Hypotheses (ACH) to weigh multiple indicators, including infrastructure overlaps, TTP consistency, malware similarity, and operational patterns, providing confidence-weighted attribution assessments for incident response.

Implements a robust, multi-factor model to calculate the criticality score of IT assets. The scoring incorporates data sensitivity, business function dependency, regulatory scope, and network exposure, moving beyond simple CVSS scores. The resulting 1-5 criticality tier directly modifies vulnerability remediation Service Level Agreements (SLAs), ensuring security resources are focused on the organization's most valuable and risky systems.

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).

Build local, research-only computational pathology workflows with PathML: load and tile whole-slide images, run preprocessing and QC pipelines, manage h5path data, quantify multiplex images, construct spatial graphs, and plan bounded model inference.

This skill covers using the SHAP library to explain and audit machine learning predictions. It guides users on selecting explainers, computing feature attributions, and validating results. It includes workflows for local and global visualizations, handling multi-output models, and ensuring model behavior descriptions are accurate without implying causality. Requires Python 3.12+ and specific SHAP versions.

This guide details how to set up and execute large-scale, distributed GPU training jobs on the CoreWeave platform. It covers both single-node multi-GPU setups and multi-node training utilizing PyTorch DDP and Kubernetes resources (A100/H100). This workflow is ideal for fine-tuning large language models (LLMs) or running complex deep learning models that require high-performance, shared-storage compute clusters.

A comprehensive tool for creating high-quality training datasets for custom object detection and segmentation models. It supports multiple advanced annotation methods including BBox drawing, SAM2 pixel-perfect segmentation, and DINOv3 visual grounding. Features include AI auto-detection, object tracking, and standardized COCO format export, streamlining the entire data pipeline from capture to training.

This guide provides a comprehensive reference architecture for building production-ready, enterprise-grade applications on Palantir Foundry. It covers the entire data lifecycle, detailing best practices for the standard data pipeline (Raw > Clean > Model), Ontology design, external API integration patterns, and robust multi-layered security models. Use it when designing, planning, or optimizing complex data infrastructure within Foundry.

This skill allows for the generation and execution of complex, optimized SQL queries to derive deep business insights. It supports sophisticated data workflows, including multi-table joins, multi-dimensional analysis (e.g., trend analysis, attribution modeling, comparative analysis), and subsequent recommendations for appropriate data visualizations (e.g., line charts, waterfall charts). It goes beyond simple SELECT statements by planning the query structure, handling schema relationships, and ensuring adherence to best practices.