ml-rigor
Enforces baseline comparisons, cross-validation, interpretation, and leakage prevention for ML pipelines
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Enforces baseline comparisons, cross-validation, interpretation, and leakage prevention for ML pipelines
Enforces baseline comparisons, cross-validation, interpretation, and leakage prevention for ML pipelines
Build features with AI coding tools (Claude Code, Lovable, Replit, Cursor). Use when implementing specs, iterating on AI code, or choosing tools. Focuses on tool selection, effective prompting, and iteration wo...
Track production app health and catch issues before users complain. Use after deploying, to check app status, or when investigating user reports. Covers error tracking, uptime monitoring, and metrics for non-te...
Test features before users find bugs. Use when feature is built, before deploying, or when bugs reported. Covers manual testing, edge cases, cross-browser testing, and testing checklists for non-technical found...
Protect your SaaS app from common vulnerabilities. Use when building auth, handling user data, or deploying features. Covers authentication, data protection, API security, and OWASP Top 10 for non-technical fou...
Optimize your SaaS app across four dimensions - Speed (page load, API response), Code (unused files, dead code), Database (orphaned data, schema hygiene), and Dependencies (package bloat, bundle size). Use when...
Turn ideas into clear, buildable specs for AI tools or stakeholder review. Use when starting features, planning projects, or when AI keeps building the wrong thing. Creates Quick Feature Specs (10-15 min) for i...
Fix bugs systematically instead of guessing. Use when features break, users report errors, or tests fail. Covers reproducing bugs, gathering diagnostic info, and working with AI tools to fix issues efficiently...
Create and manage project-specific design systems for SaaS applications. Use when starting a new app (CREATE mode), updating an existing design system (MODIFY mode), or building features that need to follow est...
Auto-invoke when reviewing API routes, server logic, Express/Node.js code, or backend architecture. Enforces REST conventions, middleware patterns, and separation of concerns.
Auto-invoke when reviewing loops, data fetching, rendering, database queries, or resource-intensive operations. Identifies N+1 queries, unnecessary re-renders, memory leaks, and scalability issues.
Auto-invoke when reviewing JSX with interactive elements, forms, buttons, or navigation. Enforces WCAG compliance and inclusive design.
Auto-invoke when reviewing README files, JSDoc comments, or inline documentation. Enforces "WHY not WHAT" principle.
Verify performance implications were considered and no obvious anti-patterns exist. Issues result in WARNINGS.
Verify security considerations were addressed before shipping. Issues result in WARNINGS that strongly recommend fixing.
Auto-invoke for general code quality review. Enforces naming conventions, function size, DRY principles, SOLID principles, and code organization.
Verify errors are handled gracefully with meaningful user feedback. Issues result in WARNINGS.
Auto-invoke when reviewing test files or discussing testing strategy. Enforces testing pyramid, strategic coverage, and stack-appropriate frameworks.
Auto-invoke when reviewing schema design, database queries, ORM usage, or migrations. Enforces normalization, indexing awareness, query optimization, and migration safety.
Auto-invoke when reviewing React, Vue, or frontend component code. Enforces component architecture, state management patterns, and UI best practices.
Auto-invoke when reviewing HTML head, meta tags, or Next.js page components. Enforces semantic HTML and search optimization.
Verify code quality standards are met - naming, structure, DRY principles. Issues result in SUGGESTIONS for improvement.
Auto-invoke when reviewing authentication, authorization, input handling, data exposure, or any user-facing code. Enforces OWASP top 10 awareness and security-first thinking.
默认按已收录数据中的 GitHub 仓库 Stars 排序。Stars 属于来源仓库,同一仓库的多个技能可能共享相同数值;它反映仓库关注度,不代表单个技能的安装量或质量评分。