Skill UI

A comprehensive playbook for professional bug bounty hunting and penetration testing. This guide covers systematic reconnaissance, from initial asset discovery (subdomains, IPs) and technology fingerprinting to advanced endpoint and source code mining. Learn the structured methodology used by top hunters to maximize coverage and find high-severity vulnerabilities.

This tool simulates a Chief Technology Officer's comprehensive review, stress-testing any major technical plan. It systematically interrogates critical areas including scaling limits (scaling cliffs), accumulated technical debt, team capacity planning, build-vs-buy economics, system reliability (SLOs), and security compliance. Use it before committing to core architecture changes or making high-stakes engineering decisions.

This expert framework guides users through the systematic process of conducting rigorous A/B tests and growth experiments. It covers all stages: formulating strong hypotheses, selecting appropriate metrics (primary, secondary, guardrail), calculating necessary sample sizes, and choosing the best implementation method (client-side vs. server-side). Use this when planning to compare two versions or build a systematic growth experimentation program.

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 model provides an engineering operational framework for teams that leverage AI agents to generate large volumes of code. It shifts the focus from simple syntax to process quality, robust architecture design, rigorous testing, and systematic risk control, ensuring the development of highly stable and reliable systems in AI-assisted environments.

A comprehensive strategy for regression testing in AI-assisted development. It focuses on identifying systematic blind spots, such as schema inconsistencies between sandbox and production paths, and integrating automated bug checks into the development workflow. This guide teaches how to build robust, database-free API tests to ensure code quality when AI models contribute to the codebase.

This utility executes an autonomous, iterative loop, simulating a comprehensive agent workflow. It systematically manages tasks by checking current status, predicting the optimal next action using specialized tools, executing the required task (e.g., coding, testing), logging the progress, and scheduling the next iteration. It supports task discovery from multiple sources, including team tasks, swarm tasks, and file checklists, ensuring continuous, automated progress towards a defined goal.

This skill provides a rigorous, measured framework for optimizing system performance. It guides users to convert vague goals (e.g., 'make it faster') into a bounded, repeatable process. The loop requires establishing a clear baseline, systematically testing hypotheses (variants), identifying bottlenecks, and promoting the safest, most measurably improved solution, ensuring reproducibility and stability throughout the process.

This skill performs an adversarial audit of web application authentication flows. It systematically probes for critical security vulnerabilities such as missing CSRF protection, insecure session cookies (lacking Secure/HttpOnly flags), risky cross-origin redirects, and OAuth misconfigurations. It generates a structured report detailing findings and severity levels, making it ideal for pre-deployment security audits and regression testing.

A comprehensive pipeline designed for Laravel projects to ensure code quality and stability before deployment. It systematically executes environment checks, linting, static analysis, full testing with coverage, security audits, migration validation, and cache preparation. Essential for robust CI/CD pipelines and pre-Pull Request validation.

This guide assists developers in creating a systematic QA plan for RevenueCat integration. It provides a structured approach to verifying the entire subscription lifecycle, including initial purchases, renewal, upgrades, and cancellations. The core principle is linking tests to specific, verifiable dashboard signals, ensuring robust and reliable in-app monetization setups.

Design a comprehensive and systematic framework for optimizing ad creative performance. This tool guides users through defining testable variables (headlines, CTAs, imagery), designing A/B and multivariate tests, calculating required sample sizes, and establishing optimal iteration cadences across major advertising platforms (Meta, Google, etc.) for continuous marketing improvement.