Skill UI

This guide provides a structured framework for presenting complex empirical results in academic manuscripts, particularly for international marketing journals. It details the essential 'spine exhibits' (conceptual models, invariance tables, cross-national patterns) and teaches techniques like the 'claim test' and 'triage' to ensure arguments are clear, rigorous, and adhere to strict page limits.

Exa MCP provides advanced neural search capabilities across the web, specialized code repositories (like GitHub), and corporate/personnel databases. Use it for deep background research, finding the latest news, debugging complex code patterns, or conducting thorough market and competitor analysis.

NetworkX is a powerful Python package designed for creating, manipulating, and analyzing complex networks and graphs. It supports various graph types (Directed, Undirected, MultiGraphs) and provides extensive functionality for graph algorithms, including shortest path calculation, centrality measures (like PageRank), community detection, and connectivity checks. Users can generate synthetic networks, read/write data from multiple formats (GraphML, CSV, Pandas), and visualize results using Matplotlib. Ideal for social network analysis, biological research, and system modeling.

This SDK allows asynchronous Python programming to control a live web browser, simulating user interactions for web automation, testing, and data extraction. Users can navigate pages, locate elements, fill forms, click buttons, and capture snapshots of the current page's content. It enforces a 'perceive → act → verify' cycle, making it ideal for building robust web scrapers and end-to-end testing tools.

A comprehensive, 10-phase methodology for authorized security assessment of modern APIs, including REST, GraphQL, WebSocket, and SOAP. This guide covers the entire testing lifecycle—from endpoint discovery and authentication bypass (JWT/OAuth 2.0) to sophisticated authorization flaws (BOLA/IDOR/BFLA), input validation, rate limiting, and CI/CD integration testing.

This skill scaffolds a gold-standard annotation project tailored for evaluating clinical NER and de-identification models (OpenMed). It guides users through defining label schemas, writing rigorous guidelines, configuring annotation tools (like BRAT/Label Studio), generating synthetic clinical text, and creating disciplined, non-leaking train/dev/test splits. It ensures that all committed data are synthetic fixtures for reproducible evaluation.

Automates the entire Continuous Integration (CI) workflow, crucial before opening a Pull Request (PR). It systematically runs local unit tests for multiple languages (Python, Node, Rust), performs type checking, pushes the current branch to the remote repository, and finally provides the direct URL and status check for the CI platform (GitHub Actions/GitLab). This single command replaces multiple manual steps.

Quickly retrieve and display the current Continuous Integration/Continuous Deployment (CI/CD) pipeline status for the active branch or a specific Pull/Merge Request. This tool supports major Git platforms including GitLab CI and GitHub Actions, providing developers with an immediate snapshot of build and test results.

This tool provides a unified interface to run test suites for various programming stacks (Python, Node.js, Rust) within a CI/CD pipeline. It automatically detects the project's environment (by checking lock files or config) and executes the appropriate testing framework (e.g., pytest, vitest, cargo test), ensuring consistent code quality checks regardless of the underlying language. Ideal for pre-commit or CI/CD integration.

A comprehensive, authorized methodology for security assessments covering cloud platforms, container runtimes, and Kubernetes clusters. It guides users through checking critical areas such as IAM privilege escalation, metadata SSRF, container escape paths, and granular RBAC misconfigurations, ensuring testing is systematic and non-destructive.

A comprehensive white-box security auditing workflow designed for proprietary source code. It integrates static analysis security testing (SAST) tools like Semgrep and CodeQL to detect common vulnerabilities (e.g., injection, IDOR, crypto misuse), followed by mandatory manual threat modeling and verification steps. Applicable for PR reviews and pre-deployment security checks.

This tool provides a preferred, end-to-end method for modifying code in real-world repositories. It simulates a professional development workflow: cloning the target repository, running changes on a dedicated task branch, and critically, verifying the implementation through an independent red $\to$ green $\to$ regression test loop. It is designed for fixing GitHub issues, implementing new features, or adding functions to existing codebases, ensuring the structural integrity and correctness of the changes.