Skill UI

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 guide details how to perform authenticated (credentialed) vulnerability scans using industry-leading tools like Nessus, Qualys, and OpenVAS. By utilizing service credentials (SSH, SMB, WinRM, SNMPv3), the scan moves beyond superficial banner checks to deeply inspect installed patches, configurations, and local system settings on Linux, Windows, and network devices. This is crucial for detecting vulnerabilities that unauthenticated scans often miss, significantly improving security assessment accuracy.

A comprehensive guide for Security Operations Center (SOC) analysts on reducing false positive alerts in SIEM systems. Techniques include systematic threshold tuning, refining correlation logic, implementing allowlisting, and enriching data with threat intelligence to combat alert fatigue and improve detection accuracy.

This skill enforces a rigorous, 5-phase debugging protocol (Reproduce, Isolate, Root Cause, Fix, Verify) for AI coding agents. It forces a disciplined approach, preventing agents from guessing fixes or making premature edits. Use this when debugging complex, intermittent, or hard-to-isolate bugs to ensure the root cause is fully confirmed before any code changes are applied, maximizing debugging accuracy.

An automated verification system designed for critical healthcare application deployments (EMR/EHR). This harness enforces stringent patient safety standards by running multiple mandatory test gates, including Clinical Decision Support System (CDSS) accuracy, Protected Health Information (PHI) leakage prevention, and data integrity checks. A single failure in critical gates automatically blocks deployment, ensuring regulatory compliance and optimal patient safety in the CI/CD pipeline.

This comprehensive tool generates high-quality Mermaid diagram code (including flowcharts, sequence diagrams, ER diagrams, etc.) directly from natural language user requirements. It handles complex structured output, mandatory syntax validation using dedicated CLI tools, and detailed visual review to ensure the accuracy of all components and connections.

This tool is designed to enhance lead intelligence by processing and analyzing prospect data. It helps users gain deep insights into potential customers, improving targeting accuracy, optimizing sales pipelines, and refining marketing strategies for business growth.

This expert tool provides deep, systematic auditing of datasets to assess completeness, consistency, accuracy, and structural validity. It performs full data profiling, detects anomalies (outliers), analyzes missing data mechanisms (MCAR/MAR/MNAR), and calculates a Data Quality Score (DQS). Use it when validating data for model training, debugging pipelines, or establishing ongoing monitoring checks.

A highly advanced communication mode designed for maximum token efficiency and technical brevity. It drastically compresses output by removing filler words, articles, and pleasantries while rigorously maintaining technical accuracy, code integrity, and specialized terminology. Ideal for developers working in environments requiring extremely concise, direct, and high-density information exchange.

A comprehensive skill for the entire lifecycle of AI capability development. Use this tool to draft new skills from scratch, iteratively improve existing ones, and rigorously measure their performance. The process includes capturing user intent, defining test cases, running quantitative benchmarks, evaluating results, and optimizing the skill description for maximum triggering accuracy.

An advanced technical writer specializing in creating structured, high-quality software documentation. This skill strictly adheres to the Diátaxis Framework, ensuring content is categorized into Tutorials (learning), How-to Guides (problem-solving), Reference (technical details), and Explanations (concept understanding). It follows a rigorous workflow: clarification, structure proposal, and content generation, guaranteeing clarity, accuracy, and user-centricity.

Provides patterns and techniques for building robust evaluation loops for AI agents. This skill guides the implementation of self-critique, evaluator-optimizer pipelines, and test-driven refinement workflows. Use it to significantly enhance the accuracy and quality of complex outputs like code, reports, and analyses by implementing structured, iterative improvement cycles (Generate → Evaluate → Critique → Refine).