Skill UI

This system deploys advanced AI, NLP, and behavioral analytics to detect sophisticated Business Email Compromise (BEC) attacks. Unlike traditional filters that rely on malicious links, this solution analyzes writing style, tone, grammatical patterns, and contextual anomalies to identify pure social engineering impersonation, providing a robust defense layer against advanced phishing and fraud.

This skill provides a systematic framework for managing Indicators of Compromise (IOCs) throughout their entire life cycle—from initial discovery and validation to enrichment, deployment, monitoring, and eventual retirement. It emphasizes implementing confidence decay functions, tracking hit rates, and managing false positives to maintain a high-quality, actionable threat intelligence database, thereby minimizing analyst fatigue and maximizing detection efficacy.

This skill provides institutional-depth equity analysis by combining public SEC EDGAR filings and real-time market data. It can generate structured, verdict-style memos, score stocks across four key pillars (Momentum, Stability, Financial Health, Upside), and perform side-by-side comparisons of two tickers, enabling data-driven investment decision support without requiring proprietary terminal access.

This skill acts as a critical bridge between the conceptual idea phase and the automated execution phase. It ingests structured experiment plans (e.g., EXPERIMENT_PLAN.md), parses objectives, metrics, and methodology. It then implements the necessary code (training, evaluation), performs internal code reviews, and deploys the experiments to collect initial results, advancing the research workflow.

This skill provides programmatic access to the Cancer Dependency Map (DepMap), a comprehensive resource characterizing genetic dependencies in cancer cell lines. Use it to identify genes essential for specific cancer types, discover synthetic lethal interactions, and validate potential oncology drug targets using CRISPR and RNAi data.

This advanced tool utilizes the Digital Human DNA (DHDNA) framework to extract a comprehensive cognitive signature from any piece of text. It analyzes the author's unique thinking patterns, reasoning methods, decision-making styles, and core values across 12 detailed dimensions (e.g., Analytical Depth, Ethical Reasoning, Emotional Processing). It provides a structured profile, revealing the topology of the mind behind the writing, without judgment.

Latch is a robust Python framework designed for building, deploying, and managing complex, scalable bioinformatics workflows as serverless pipelines. It supports integrating multiple analysis tools (like Nextflow/Snakemake) and handles cloud data management (LatchFile/LatchDir) and detailed resource configuration (GPU, CPU, memory). Ideal for running large-scale analyses such as RNA-seq, AlphaFold, and single-cell analysis in a reproducible, production-ready environment.

A robust job queue designed to orchestrate large-scale, multi-seed, and multi-configuration Machine Learning experiments on remote GPU clusters. It handles complex workflows, including wave transitions, dependency chaining (teacher-student), Out-of-Memory (OOM) retries, and stale screen cleanup, ensuring reliable execution for demanding scientific computing tasks.

This comprehensive checklist guides users through critical operational readiness steps for a ClickHouse production deployment. It covers server tuning (config.xml), schema design best practices, robust backup strategies (S3), detailed monitoring setup (Prometheus/Grafana), security hardening, and application integration verification. Use this document before go-live, during major upgrades, or when auditing production systems to ensure maximum stability and performance.

This comprehensive guide details best practices for securing a ClickHouse deployment. It covers essential security measures including role-based access control (RBAC) for granular user permissions, implementing row-level security for multi-tenancy, configuring network IP restrictions, enabling TLS encryption, and setting up detailed audit logging. Use this when hardening a production ClickHouse instance or enforcing strict data governance.

This guide demonstrates how to deploy GPU workloads on the CoreWeave platform using Kubernetes (kubectl). It covers deploying vLLM inference services for large language model (LLM) testing and running batch CUDA jobs for benchmarking. This is ideal for users running their first GPU workloads, testing inference pipelines, or verifying cluster access on bare-metal GPU nodes (A100, H100).

Provides a comprehensive checklist for deploying Hex data analytics integrations into production. It covers critical operational best practices, including API key management, rate limit handling, error resilience (circuit breakers, exponential backoff), and robust monitoring strategies. This guide ensures that data pipelines are deployed reliably, minimizing downtime and guaranteeing consistent, high-quality data reporting for go-live scenarios.