Skill UI

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 comprehensive guide for creating end-to-end SEO strategies for new or existing websites. This skill covers the entire digital presence lifecycle, including market discovery, deep competitive analysis, optimal site architecture design, robust content planning (E-E-A-T), technical SEO foundation setup, and a phased implementation roadmap across 12 months. It ensures all facets—from foundational infrastructure to thought leadership—are addressed for maximum organic growth.

Perform a comprehensive, deep-dive audit on a single URL, covering all critical aspects of Search Engine Optimization. Analysis includes on-page elements (title, headings, meta tags), content quality (readability, E-E-A-T), technical structure (schema, canonical, OG tags), image optimization, and core web vitals potential issues. Ideal for SEO audits and content quality checks.

This comprehensive tool guides the systematic creation of detailed technical specifications. It orchestrates the entire design lifecycle, including context loading, deep discovery and analysis (tailored for new features, extensions, or simple additions), synthesis using established design principles, and generating a boundary-first, structured design draft. Ensures all requirements are mapped to explicit technical components.

This skill analyzes the implementation gap by comparing approved feature requirements against the existing codebase, architecture, and context. It helps identify missing capabilities, potential integration challenges, and necessary technical research areas. The output provides multiple viable implementation options and a comprehensive analysis report, guiding the transition from requirements definition to technical design.

Defines a Markdown/Mermaid-first documentation skill for scientific and technical writing, providing diagram standards, 24 reference types, and templates so every report, README, or analysis stays text-first, versionable, and visualization-ready.

This skill guides AI agents to handle complex technical tasks with maximum precision and minimal computational overhead. It implements adaptive verbosity rules, sophisticated input filtering (for logs, source files, VCS, and payloads), and surgical output generation. It ensures context-window awareness, structured reasoning, and adherence to best practices for coding, debugging, and architecture analysis.

This comprehensive skill guide focuses on Amazon Aurora DSQL, a serverless, PostgreSQL-compatible distributed SQL database. It provides deep technical knowledge covering schema management, direct query execution via MCP tools, multi-tenant isolation, and full migration support (e.g., MySQL to DSQL). Users will learn advanced concepts like DDL operations, IAM authentication, and diagnosing slow queries using detailed plan explainability analysis, making it essential for building scalable, robust applications.

This guide provides a systematic approach to deep debugging complex HubSpot API issues. It includes layer-by-layer network testing (DNS, TCP, TLS), correlation ID tracking, and timing analysis to isolate intermittent or hard-to-reproduce errors. Use this when standard troubleshooting fails or when compiling detailed evidence for technical support.

A multi-phase workflow designed to guide users through complex problem analysis and solution design. Instead of providing immediate answers, it systematically enforces deep thinking by reviewing the problem's validity, assessing information gaps, performing root cause analysis, and iterating through solution vetting (self-check and final review). Ideal for complex technical troubleshooting, decision-making, or theoretical discussions requiring rigorous methodology.