Skill UI

Provides comprehensive monitoring for the Clay data enrichment pipeline. It tracks critical operational metrics such as credit consumption velocity, enrichment success rates (hit rates), data quality scores, and lead distribution. Essential for cost control and ensuring pipeline health in data-intensive CRM workflows.

A comprehensive system for implementing policy guardrails in data enrichment pipelines. It enforces critical controls including credit spending limits, blocks the enrichment of sensitive PII (e.g., SSN, bank details), and validates input data quality. Essential for maintaining compliance (GDPR/CCPA) and managing costs when processing large volumes of personal and business data.

Use Declarative Automation Bundles (DABs) to manage and deploy Databricks assets, including jobs, DLT pipelines, and ML models, using an Infrastructure-as-Code approach. This skill allows defining resources and deployment targets (dev, staging, prod) in a single YAML file, enabling robust CI/CD practices and controlled promotion of changes across environments.

This comprehensive checklist guides the deployment of Databricks data pipelines and jobs to a production environment. It covers critical stages including pre-deployment security hardening (Service Principals, Unity Catalog), validating infrastructure (cluster types, autoscaling), ensuring code quality (unit/integration tests, Delta Lake best practices), executing controlled deployment, and performing post-deployment health checks and monitoring setup. Ideal for CI/CD pipelines and go-live procedures.

Controls Mixpanel analytics via Rube MCP to aggregate events, run segmentation, funnels, cohorts, profiles, and JQL/insight queries while ensuring schema discovery precedes automation.

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.

Guides senior data scientists through designing controlled experiments, engineering structured features, training ML models, and translating results into decisions using Python, R, SQL, and tools like Scikit-learn, XGBoost, and MLflow.

Guides production ML engineering with patterns for deploying models, building MLOps pipelines, monitoring drift, integrating LLMs and RAG systems, and enforcing feature-store, retraining, and cost controls in live environments.

RDKit delivers a Python-based cheminformatics toolkit for parsing SMILES/SDF, computing descriptors, generating 2D/3D coordinates, fingerprinting, searching substructures, managing reactions, and visualizing molecules, ideal for drug discovery and computational chemistry workflows.

This skill analyzes DNS query logs to detect sophisticated threats such as data exfiltration via DNS tunneling, Domain Generation Algorithm (DGA) usage, and covert Command and Control (C2) channels. It utilizes advanced techniques like entropy analysis, query volume anomaly detection, and subdomain length analysis within SIEM platforms, helping SOC teams identify threats that bypass traditional network security controls.

This tool applies advanced role mining techniques (including clustering and formal concept analysis) to analyze existing user-permission assignments. It discovers optimal, minimal role sets for Role-Based Access Control (RBAC) systems, helping organizations mitigate 'role explosion' and enforce the principle of least privilege. Ideal for identity governance and access control architecture redesign.

This script performs statistical analysis on Zeek connection logs (conn.log) to detect Command and Control (C2) beaconing patterns. It leverages the ZAT library to load data into Pandas DataFrames, calculates inter-arrival time statistics, and flags connections with low standard deviation relative to the mean. This technique is crucial for network threat hunting and security incident investigation when identifying malicious, periodic callbacks.