Skill UI

This router serves as the initial guide for comprehensive security assessments on new targets or unknown attack surfaces. It helps users map the scope, discover hidden assets, perform detailed technology fingerprinting, build endpoint inventories, and strategically plan the optimal testing path before diving into specific vulnerability checks (e.g., API, Auth, Injection).

This playbook details the methodology for detecting and exploiting subdomain takeover vulnerabilities. It focuses on identifying CNAME/NS/MX records pointing to deprovisioned or unclaimed cloud resources (e.g., AWS S3 buckets, Heroku apps). Understanding these weaknesses allows attackers to serve malicious content under the victim's trusted domain, leading to phishing, credential theft, and security bypasses.

A structured methodology for validating non-trivial decisions by subjecting them to an adversarial, fresh-context review. This process is crucial when correctness is paramount—such as in production, security-sensitive logic, or complex architecture—by forcing a skeptical examination that aims to disprove assumptions rather than simply approve them.

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.

A comprehensive, four-phase methodology for debugging complex software issues. It guides users through root cause investigation, pattern analysis, hypothesis testing, and controlled implementation. The core principle emphasizes finding the underlying cause rather than applying quick, symptomatic fixes, ensuring robust and reliable code improvements across various failure scenarios.

This skill is designed to identify subtle and complex logic bugs within a single code file or function. It employs a semi-formal execution tracing methodology, systematically breaking down the suspected bug into five stages: Premises (expected state), Trace (execution path), Divergence (actual deviation), Trigger (reproduction case), and Remedy (fix). It is ideal for scenarios where users suspect code is wrong but cannot specify a concrete failure point.

Generates championship-grade, F1-inspired test suites based on the WJTTC methodology. This skill enforces a 'tests first' philosophy, analyzing the codebase to define structured test plans. It covers standard industry unit tests (Layer 1) and deep expert edge cases (Layer 2). Furthermore, it includes Signal Integrity Audits to ensure the test suite's reliability is paramount, guiding development toward maximum code quality.

WGM is a robust, agentic methodology designed to transform vague or rough requirements into working, validated software. It enforces a disciplined workflow across six stages: deep alignment (Grill), structured planning, deterministic iterative building (Loop), mandatory holdout-scenario judging, and final documentation audits. It ensures that the resulting code converges against rigorously tested acceptance criteria rather than relying on single-pass generation.

A comprehensive guide to writing reliable and maintainable tests in Go. This skill covers the TDD methodology (Red-Green-Refactor cycle), advanced patterns like table-driven tests, subtests, benchmarks, and fuzzing. Learn how to ensure high code quality and test coverage in idiomatic Go projects.

A comprehensive, end-to-end methodology designed for software engineers and AI agents to safely and systematically integrate machine learning models into existing non-ML codebases. It guides users through critical phases, including problem framing, data readiness, architectural decoupling (via APIs/services), baseline model implementation, and final MLOps pipeline setup.

Master professional Python testing practices using the pytest framework. This guide covers Test-Driven Development (TDD) methodology, advanced features like fixtures (with different scopes), parametrization, assertion techniques, and achieving high code coverage, ensuring robust and maintainable software.

This guide provides comprehensive patterns for writing reliable and maintainable tests in Rust, adhering to the TDD methodology. It covers unit testing, integration testing, asynchronous programming with Tokio, property-based testing, mocking, and robust error/panic handling. Ideal for developers looking to elevate their Rust code quality and coverage.