Skill UI

This skill provides guidance and patterns for generating pairwise test cases. Pairwise testing is an advanced testing technique used in software quality assurance (QA) to ensure that every combination of two input parameters is tested. It is highly effective for complex systems with multiple interacting variables, helping developers improve test coverage efficiently while minimizing the overall number of required test cases.

A comprehensive technical guide covering the entire lifecycle of Python packaging. It teaches developers how to structure, build, test, and distribute robust Python libraries using modern standards like pyproject.toml, generating wheels, and publishing artifacts reliably to PyPI or private package indexes. Ideal for creating reusable, professional-grade Python code.

A structured workflow designed to prevent 'reinventing the wheel.' Before writing custom code, this skill guides the user through analyzing needs, conducting parallel searches across multiple channels (npm, PyPI, GitHub, MCP), evaluating candidate solutions based on maintainability and community support, and making a decision (Adopt, Extend, or Custom Build). It ensures that solutions leverage existing, proven libraries and patterns.

This guide details proactive threat hunting techniques focused on detecting supply chain compromises. Use it when investigating trojanized software updates, compromised dependencies (npm/PyPI), or tampered build artifacts. It outlines a structured workflow using EDR/SIEM data (CrowdStrike, Splunk) to identify hidden threats and scope the impact of sophisticated attacks.

This tool detects malicious packages mimicking legitimate dependencies in npm and PyPI. It analyzes package names using Levenshtein distance, examines publish date heuristics, and identifies download count anomalies to secure the software supply chain. Ideal for auditing project dependencies and proactively monitoring for supply chain threats.

Guides Python authors from commit to release with end-to-end steps for structuring src layouts, choosing setuptools/hatchling/flit/poetry backends, enforcing SOLID+type-hinting patterns, orchestrating lint/test/CI, managing PEP 440 semantics, and publishing trusted releases to PyPI.

A comprehensive tool and methodology for identifying and preventing dependency confusion attacks across major package ecosystems (npm, PyPI, Maven). It detects instances where internal, private package names are leaked and subsequently published publicly by attackers. The skill provides both detection (enumerating claimable internal names) and prevention strategies (enforcing registry pinning and scope restrictions) to secure the software supply chain.

This skill detects malicious, misspelled, or brand-jacked package names (typosquatting) across major ecosystems (npm, PyPI, crates.io). It uses advanced techniques like edit-distance and keyboard-proximity analysis, powered by tools like typomania and OSSGadget, to flag potential supply chain attacks *before* a dependency is installed. It is essential for implementing robust CI/CD security gates and proactive dependency screening.