Skill UI

This skill provides comprehensive linting and static analysis for Go projects using golangci-lint. It enforces numerous best practices, checks for common errors, manages configurations via `.golangci.yml`, and supports auto-fixing. Use it during the entire development lifecycle, including setting up CI/CD pipelines, running pre-commit checks, or refactoring large, legacy Go codebases to ensure high code quality and maintain security standards.

This comprehensive skill provides a senior Go security engineer persona, guiding users through rigorous security practices for Golang development and code auditing. It covers threat modeling (STRIDE), vulnerability analysis (DREAD), secure development principles, and specialized modes like Review and Audit to detect issues such as injection, crypto flaws, and authorization weaknesses.

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.

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

This comprehensive guide provides detailed procedural steps for configuring and executing CodeQL code scanning. It covers both implementing automated workflows using GitHub Actions (`codeql.yml`) and running scans via the CodeQL CLI. Users will learn how to manage language matrices, set build modes, interpret SARIF outputs, handle monorepo structures, and troubleshoot complex security analysis setups across multiple languages (e.g., Python, Java, C++).

This skill teaches users to apply the scientific method to software debugging. Instead of random changes, it guides the process through five steps: Observation, Hypothesizing, Prediction, Testing, and Analysis. By systematically forming and testing specific, measurable hypotheses, users can efficiently isolate failing components and confirm the true root cause of technical issues.

A comprehensive guide to creating production-grade Product Requirements Documents (PRDs). This skill helps bridge the gap between vague business concepts and concrete technical specifications for modern software systems and AI features. It enforces a structured workflow, ensuring all necessary components—from user stories and success KPIs to technical architecture and risk analysis—are defined.

Performs a systematic and structured investigation to determine the true source of bugs, errors, and unexpected system behavior, rather than merely treating symptoms. This method utilizes techniques like the Five Whys, hypothesis-driven testing, stack trace analysis, and gathering evidence (logs, git diffs) across multiple phases. It is ideal for complex, intermittent, or deeply rooted technical issues in software development.

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.

Generates detailed prompts to document end-to-end application workflows by auto-detecting architectures, stacks, entry points, services, data access, error handling, and tests across .NET, Java/Spring, React, microservices, and other tech stacks.

A universal assistant for conducting deep code reviews across major SQL databases (MySQL, PostgreSQL, SQL Server, Oracle). It rigorously analyzes SQL code for critical vulnerabilities (like SQL injection), performance bottlenecks, maintainability issues, and adherence to industry best practices. Features include security audits, index optimization recommendations, query structure analysis, and schema design validation, ensuring robust and secure database development.

Conducts STRIDE-A threat modeling on repositories or systems, running either a full single-pass analysis with architectures, DFDs, prioritized findings, and executive assessments, or an incremental update that compares a baseline report to the current code and highlights deltas.