Skill UI

This tool performs comprehensive security testing on SOAP web services. It analyzes WSDL definitions to identify available methods and then tests for critical XML-related vulnerabilities, including XXE, XPath injection, XML bombs, and WS-Security bypass. It is designed for automated penetration testing of enterprise-level SOAP APIs.

Writes rigorous mathematical proofs for ML/AI theory, including theorem/lemma drafting, filling missing proof steps, formalizing proof sketches, and checking whether claims are provable from assumptions.

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.

This comprehensive guide details best practices for securing Helm chart deployments within Kubernetes. It covers critical security measures including verifying chart signatures and provenance, scanning templates for configuration misconfigurations, enforcing Pod Security Contexts via values.yaml, managing sensitive data using external secret stores, and scoping RBAC roles for CI/CD pipelines. Essential for maintaining supply chain integrity and meeting compliance standards.

A comprehensive guide and set of payloads for penetration testing web applications that process XML input. It details how to test for major vulnerabilities such as XXE, XPath injection, SSRF, and XML parsing attacks, providing step-by-step workflows and advanced payloads for identifying data exposure and security flaws.

This guide details methodologies for identifying and exploiting XML External Entity (XXE) injection vulnerabilities in web applications. It covers basic file retrieval, blind XXE detection using out-of-band techniques, and data exfiltration, all critical steps during authorized penetration testing of XML-consuming APIs and services.

Orchestrates WCAG 2.2 Level A/AA scans, fixes, and verifications across React, Next.js, Vue, Angular, Svelte, and plain HTML codebases, producing before/after diffs, contrast checks, compliance reports, and CI/CD-ready hooks.

Guides on using SwanLab for open-source ML experiment tracking, offering local or self-hosted dashboards, rich media logging, and integrations with PyTorch, Transformers, Lightning, or Fastai for comparing runs and monitoring metrics.

A lightweight CLI tool for systematically comparing various coding AI agents (such as Claude Code, Aider, and Codex) head-to-head. It measures crucial metrics—pass rate, API cost, execution time, and consistency—on reproducible tasks defined via YAML manifests. Use this to objectively select the best agent for your codebase or to validate model updates before production deployment.

This skill systematically probes an AI model's core behavioral patterns across six critical dimensions, including reasoning, refusal, formatting, and grounding. It generates a comprehensive, styled HTML report with radar charts and actionable insights, allowing users to understand a model's true capabilities, compare different models, or perform compliance and red-team assessments without needing an external API key.

A comprehensive tool for generating publication-quality figures essential for Machine Learning and AI academic papers. It supports two distinct workflows: generating complex system architecture and workflow diagrams using AI (Gemini), and creating precise, data-driven visualizations (line charts, heatmaps, etc.) using standard libraries like matplotlib and seaborn. Use it when preparing any figure for a conference submission.

Trackio is an essential library for logging, visualizing, and analyzing machine learning training experiments. It provides multi-interface support: a Python API for real-time metric logging and alert firing during training; a Command Line Interface (CLI) for retrieving historical metrics and alerts; and synchronization capabilities to Hugging Face Spaces for persistent dashboards. This tool is crucial for debugging, diagnosing training issues (like loss spikes), and automating ML workflow iteration.