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Found 101 Skills
Manage Linux systems covering systemd services, process management, filesystems, networking, performance tuning, and troubleshooting. Use when deploying applications, optimizing server performance, diagnosing production issues, or managing users and security on Linux servers.
Optimize Next.js 15 applications for performance, Core Web Vitals, and production best practices using App Router patterns
Rust coding best practices based on Microsoft Pragmatic Rust Guidelines. ALWAYS invoke before writing or modifying Rust code. Covers error handling, API design, performance, and idiomatic patterns.
Principal backend engineering intelligence for Python services and data systems. Actions: plan, design, build, implement, review, fix, optimize, refactor, debug, secure, scale backend code and architectures. Focus: correctness, reliability, performance, security, observability, scalability, operability, cost.
Configure nginx for static sites, reverse proxying, load balancing, SSL/TLS termination, caching, and performance tuning. When setting up web servers, application proxies, or load balancers, this skill provides production-ready patterns with modern security best practices for TLS 1.3, rate limiting, and security headers.
nginx C module performance optimization and reliability guidelines based on the official nginx development guide. This skill should be used when optimizing nginx C modules for throughput, latency, memory efficiency, and operational resilience. Triggers on tasks involving buffer optimization, connection tuning, shared memory contention, error recovery, timeout strategy, caching implementation, worker process tuning, or logging performance in nginx C modules.
React-specific component, hook, and rendering patterns. Use when writing React components, hooks, JSX, or optimizing React performance.
Get best practices for Entity Framework Core
Execution-aware preflight analysis (control-flow, timing/energy) on existing callees using compiled artifacts, to catch problems while the design is still cheap to change.
Use when the user wants to push past conventional workflow limits with advanced performance techniques like parallel orchestration, streaming pipelines, or adaptive routing.
Generate Triton kernel code for Ascend NPU based on operator design documents. Used when users need to implement Triton operator kernels and convert requirement documents into executable code. Core capabilities: (1) Parse requirement documents to confirm computing logic (2) Design tiling partitioning strategy (3) Generate high-performance kernel code (4) Generate test code to verify correctness.
Battle-tested PyTorch training recipes for all domains — LLMs, vision, diffusion, medical imaging, protein/drug discovery, spatial omics, genomics. Covers training loops, optimizer selection (AdamW, Muon), LR scheduling, mixed precision, debugging, and systematic experimentation. Use when training or fine-tuning neural networks, debugging loss spikes or OOM, choosing architectures, or optimizing GPU throughput.