Skill UI

Provides methodology and scripts for proactive threat hunting, IOC sweeps, and statistical anomaly detection that surface attacker activity before automated alerts fire.

A comprehensive guide covering advanced optimization techniques for Golang, including allocation reduction, CPU cycle efficiency, memory layout tuning, Garbage Collection (GC) management, pooling, and caching strategies. This skill provides a systematic methodology (Measure -> Hypothesize -> Improve -> Benchmark) for resolving bottlenecks identified through profiling tools like pprof or fgprof, ensuring optimizations are targeted and validated.

A systematic guide for debugging complex issues in Go applications, including crashes, deadlocks, concurrency errors, and performance bottlenecks. This skill teaches the 'Golden Rules' of debugging: reproducing bugs, isolating variables, and finding the true root cause rather than applying symptom fixes. It covers advanced techniques using pprof for profiling, the Delve debugger (dlv), and Go's built-in race detector, providing a structured methodology for professional software development.

A systematic methodology for initiating projects, features, or major changes. It mandates creating a detailed, structured specification (spec) before writing any code, ensuring all assumptions are surfaced and vague requirements are translated into measurable success criteria. This process involves four validated phases: Specify, Plan, Tasks, and Implement, guaranteeing alignment between the human team and the development process.

Guides learners through reconnaissance, scanning, vulnerability analysis, exploitation, and reporting phases of authorized penetration testing, pairing command examples with tool choices and professional deliverables.

A comprehensive methodology guiding technical professionals through the design process before coding. It helps structure complex features by producing detailed design documents, including system overviews, data models, API specifications, multiple trade-off analyses, and implementation plans, ensuring robustness and clarity from the start.

This guide implements the Red-Green-Refactor cycle, the core methodology of Test-Driven Development (TDD). It teaches developers to first write a failing test (RED), then implement the minimum required code to make the test pass (GREEN), and finally improve the structure and clarity of the code without changing its behavior (REFACTOR). Use this guide when mastering TDD, ensuring robust unit test coverage, or following best practices for writing resilient software.

A structured methodology for coordinating multiple, simultaneous investigation threads to determine the root cause of complex system failures, performance regressions, or integration bugs. This skill decomposes a large problem into independent tracks (e.g., database, application code, infrastructure), assigns ownership, and manages structured sync points (Continue/Pivot/Converge) to converge on a solution faster than serial debugging. Essential for high-stakes production incident response.

A comprehensive, five-stage methodology for reviewing code quality. This process systematically checks for requirements compliance, logical correctness, code maintainability, test coverage, and critical security/performance vulnerabilities. It provides actionable feedback categorized by severity (Blocker, Major, Minor, Nit), ensuring high standards for all merged code.

A comprehensive methodology for pinpointing the root cause of complex bugs, regressions, or unexpected behavior in large codebases. Techniques include binary search debugging, Git bisect for tracking regressions across commits, strategic data/control flow logging, component isolation, and advanced breakpoint strategies (conditional breakpoints, watch expressions). Ideal for scenarios where the bug source is unclear or difficult to reproduce.

A comprehensive, four-stage methodology for debugging complex software issues, ensuring that fixes target the root cause rather than merely masking symptoms. This guide mandates rigorous investigation (gathering evidence, tracing data flow), pattern analysis, controlled hypothesis testing, and disciplined implementation via test-driven development. Use this when facing production bugs, test failures, performance degradation, or unexplained system behavior, especially under time pressure.

This advanced tutor covers a wide range of university-level STEM subjects, including Computer Science, AI/ML, Physics, Chemistry, and Engineering. It moves beyond rote memorization, focusing instead on developing deep conceptual understanding and transferable problem-solving skills. The methodology emphasizes Socratic questioning, first principles reasoning, and diagnosing the root cause of knowledge gaps across disciplines.