dev-review-pr
Handle automated PR review feedback and merge when ready
Explora e instala miles de habilidades para AI Agents en el directorio de Killer-Skills. Compatible con Claude Code, Windsurf, Cursor y más.
This directory brings installable AI Agent skills into one place so you can filter by search, category, topic, and official source, then install them directly into Claude Code, Cursor, Windsurf, and other supported environments.
Handle automated PR review feedback and merge when ready
Create new AI chat interface components for the ai-elements library following established composable patterns, shadcn/ui integration, and Vercel AI SDK conventions. Use when creating new components in packages/elements/src or when the user asks to add a new component to ai-elements.
Onchain learning platform for Solana builders.
Physics-Informed Neural Networks with PINA - solve PDEs, inverse problems, and operator learning with PyTorch
Use the CRISP‑T MCP server to run a full qualitative analysis workflow linking textual narratives and numeric data.
do-patch es una habilidad de edición quirúrgica de código para resolver bloqueos específicos en proyectos de IA
do-plan es una herramienta de planificación de características que crea planes estructurados y define problemas y soluciones
AI governance through geometric cost scaling — 14-layer security pipeline using hyperbolic geometry to make adversarial attacks mathematically impossible. Open source. Patent pending. F1=0.813.
AI-powered quiz app - test your knowledge on any topic with AI-generated questions. PWA with offline support.
Research tools and frameworks for evidence-based analysis across technology, health, economics, and human performance. Outputs include podcasts, reports, and educational materials.
Checklist-driven review of staged Git changes with deterministic rule scanning and semantic analysis. Use when the user wants to review staged changes, check for errors before commit, or validate code quality before committing.
Distributed adversarial behavioral security evaluation framework for LLMs - Swarm-based parallel probing with cryptographic consensus