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antigravity-ai-kit
antigravity-ai-kit contient 34 skills collectées depuis besync-labs, avec une couverture métier par dépôt et des pages de détail sur le site.
Skills dans ce dépôt
Pull request lifecycle domain knowledge — branch strategy detection, PR size classification, confidence-scored review, git-aware context, PR analytics, dependency management, and split/merge/describe operations.
Production readiness audit domains, weighted scoring criteria, and check specifications for the /preflight workflow.
Application scaffolding orchestrator. Creates full-stack applications from requirements, selects tech stack, coordinates agents.
Production deployment workflows, rollback strategies, and CI/CD best practices.
Internationalization and localization patterns for multi-language applications
Mobile UI/UX patterns for iOS and Android. Touch-first, platform-respectful design with React Native/Expo focus.
Web application testing principles. E2E, Playwright, component testing, and deep audit strategies.
System design patterns, DDD, 12-Factor App, SOLID principles, event-driven architecture, and architectural decision frameworks
Database schema design, optimization patterns, distributed system consistency models, and zero-downtime migration strategies
Structured task planning with clear breakdowns, dependencies, and verification criteria.
Application security best practices including Zero Trust principles, OAuth 2.0 / OpenID Connect flows, API security, supply chain security, and vulnerability prevention
Quality gate for implementation plans. Validates schema compliance, cross-cutting concerns, and completeness scoring before user presentation.
PowerShell shell conventions for Windows. Avoid bash-isms. Reference before running terminal commands.
UI/UX design intelligence with anti-AI-slop philosophy. 50+ styles, 21 palettes, 50 font pairings, 20 charts, 9 stacks.
Model Context Protocol (MCP) integration patterns for extending AI capabilities with external tools and data sources.
Multi-agent orchestration patterns for complex tasks requiring multiple domain expertise or comprehensive analysis.
AI operational modes (brainstorm, implement, debug, review, teach, ship). Adapts behavior based on task type for Trust-Grade execution.
Context window budget management and selective capability loading for LLM token optimization.
Automatic agent selection and intelligent task routing. Analyzes user requests and selects the best specialist agent(s) for Trust-Grade execution.
RESTful API design patterns and best practices
Code quality principles and best practices
Systematic debugging approaches for complex problems
Docker and containerization best practices
Frontend development patterns for React and modern frameworks
Git workflow patterns and best practices
Node.js and backend framework best practices
Testing strategies and patterns for quality assurance
Advanced TypeScript patterns and best practices
Socratic questioning and discovery protocol. Ensures requirements clarity before implementation.
Performance profiling principles. Core Web Vitals, measurement, analysis, and optimization.
Extract reusable patterns from sessions and save them as knowledge
Evaluation framework for measuring agent performance
Context window management with strategic compaction
Comprehensive verification running all quality gates