vercel-composition-patterns
React composition patterns for scalable codebases. Refactor complex components, build flexible libraries, and implement compound components or React 19 architecture patterns.
Discover reusable agent skills, browse implementation details, and find the right skill for your workflow.
406 skills found
React composition patterns for scalable codebases. Refactor complex components, build flexible libraries, and implement compound components or React 19 architecture patterns.
Epistemic safety analysis for JSON data in prompts to prevent LLM hallucinations and reasoning errors when handling incomplete or large-scale datasets.
Create and run unit tests for AnySoftKeyboard following project architecture guidelines (Robolectric, naming, location).
Systematic debugging workflow for Claude Code hooks. Use to troubleshoot non-firing hooks, output errors, or unexpected behavioral issues.
Use when profiling native macOS or iOS apps with Instruments/xctrace. Covers binary selection, CLI commands, trace exports, and common debugging gotchas.
Perform comprehensive SEO audits covering technical foundations, crawlability, on-page optimization, and content E-E-A-T to improve search rankings and organic performance.
Automates the triage, prioritization, and feedback process for new MultiQC module requests by analyzing repository activity, community engagement, and technical feasibility.
Deep single-page SEO audit for technical, content, and performance analysis. Includes on-page, schema, image, and E-E-A-T checks to optimize search rankings and user experience.
Expert-level guidance for ffuf web fuzzing, enabling automated discovery of hidden directories, files, parameters, and vulnerabilities during penetration testing.
Interprets Culture Index (CI) surveys and behavioral profiles. Analyzes team composition, burnout risk, and hiring profiles using data-driven trait assessment.
Automated security skill for identifying and validating XSS vulnerabilities, including Reflected, Stored, and DOM-based attacks across various contexts.
Bayesian modeling and probabilistic programming with PyMC. Build hierarchical models, perform MCMC sampling (NUTS), variational inference, and conduct rigorous model comparison using LOO and WAIC.