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 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.

This skill provides a comprehensive framework to detect and prevent dependency confusion—a critical supply chain vulnerability. It guides users through enumerating internal package names, scanning manifests (npm, PyPI, Maven, etc.) using tools like `confused` and OWASP `dep-scan`, and implementing preventative measures like registry pinning and defensive package registration.

A critical security tool for detecting typosquatted, brandjacked, or misspelled packages across major registries (npm, PyPI, crates.io). It uses advanced techniques like edit-distance and corpus matching to proactively screen dependency names before installation. Ideal for implementing CI/CD gates, auditing lockfile diffs, or reviewing dependency lists sourced from AI models to mitigate supply chain attacks.

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.

Enumerate cloud storage buckets, confirm dangling-CNAME takeover risks, recover AWS account IDs offline from leaked access keys, validate npm/PyPI dependency-confusion vectors, and fingerprint cloud-native/Kubernetes/CI exposure for authorized recon.

A deep-dive methodology for authorized penetration testing targeting dependency confusion and namespace resolution vulnerabilities across major package ecosystems (npm, PyPI, NuGet, Maven, etc.). This skill details advanced reconnaissance techniques, from analyzing lock files and source maps to exploiting error messages, alongside safe Proof-of-Concept (PoC) creation using DNS canaries for discovery during penetration engagements.

A comprehensive methodology covering advanced offensive techniques targeting the software supply chain. This includes exploiting dependency confusion across npm, PyPI, and NuGet, executing build system hooks, tampering with CI/CD pipelines, and compromising container images. It provides detailed PoC patterns and detection guidance for red team operations.

Generates pairwise test cases to reduce combinations while covering interactions, useful for software QA, configuration testing, and pypict workflows.