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Found 301 Skills
End-to-end SGLang SOTA performance workflow. Use when a user names an LLM model and wants SGLang to match or beat the best observed vLLM and TensorRT-LLM serving performance by searching each framework's best deployment command, benchmarking them fairly, profiling SGLang if it is slower, identifying kernel/overlap/fusion bottlenecks, patching SGLang code, and revalidating with real model runs.
Comprehensive patient stratification for precision medicine by integrating genomic, clinical, and therapeutic data. Given a disease/condition, genomic data (germline variants, somatic mutations, expression), and optional clinical parameters, performs multi-phase analysis across 9 phases covering disease disambiguation, genetic risk assessment, disease-specific molecular stratification, pharmacogenomic profiling, comorbidity/DDI risk, pathway analysis, clinical evidence and guideline mapping, clinical trial matching, and integrated outcome prediction. Generates a quantitative Precision Medicine Risk Score (0-100) with risk tier assignment (Low/Intermediate/High/Very High), treatment algorithm (1st/2nd/3rd line), pharmacogenomic guidance, clinical trial matches, and monitoring plan. Use when clinicians ask about patient risk stratification, treatment selection, prognosis prediction, or personalized therapeutic strategy across cancer, metabolic, cardiovascular, neurological, or rare diseases.
Use this skill when diagnosing, configuring, or monitoring NICs for AF_XDP / XDP workloads. Covers driver detection, hardware queue configuration, ring buffer sizing, RSS indirection table management, interrupt coalesce tuning, offload control (GSO/GRO/TSO/LRO), VLAN offloads, Flow Director (FDIR) rules with loc pinning and ixgbe wipe bug workaround, RPS/XPS queue CPU mapping, sysctl network tuning, CPU core pinning and NUMA awareness, hardware queue and drop monitoring, softirq and rx_missed_errors analysis, BPF program inspection with bpftool (prog dump xlated, net show), kernel tracing via ftrace and dmesg, perf profiling and flamegraphs, IRQ-to-queue-to-core mapping, bonding interface diagnostics, socket inspection, and a quick diagnostic checklist.
Debug Flutter applications systematically with this comprehensive troubleshooting skill. Covers RenderFlex overflow errors, setState() after dispose() issues, null check operator failures, platform channel problems, build context errors, and hot reload failures. Provides structured four-phase debugging methodology with Flutter DevTools, widget inspector, performance profiling, and platform-specific debugging for Android, iOS, and web targets.
Debug Angular applications systematically with expert-level diagnostic techniques. This skill provides comprehensive guidance for troubleshooting dependency injection errors, change detection issues (NG0100), RxJS subscription leaks, lazy loading failures, zone.js problems, and common Angular runtime errors. Includes structured four-phase debugging methodology, Angular DevTools usage, console debugging utilities (ng.probe), and performance profiling strategies for modern Angular applications.
Debug SwiftUI application issues systematically. This skill helps diagnose and resolve SwiftUI-specific problems including view update failures, state management issues with @State/@Binding/@ObservedObject, NavigationStack problems, memory leaks from retain cycles, preview crashes, Combine publisher issues, and animation glitches. Provides Xcode debugger techniques, Instruments profiling, and LLDB commands for iOS/macOS development.
Debug TensorFlow and Keras issues systematically. This skill helps diagnose and resolve machine learning problems including tensor shape mismatches, GPU/CUDA detection failures, out-of-memory errors, NaN/Inf values in loss functions, vanishing/exploding gradients, SavedModel loading errors, and data pipeline bottlenecks. Provides tf.debugging assertions, TensorBoard profiling, eager execution debugging, and version compatibility guidance.
Profile and optimize Python code using cProfile, memory profilers, and performance best practices. Use when debugging slow Python code, optimizing bottlenecks, or improving application performance.
Tests in real browsers. Use when building or debugging anything that runs in a browser. Use when you need to inspect the DOM, capture console errors, analyze network requests, profile performance, or verify visual output with real runtime data via Chrome DevTools MCP.
Inspect and profile React Native component trees from agent-device. Use when debugging React Native props, state, hooks, render causes, slow components, excessive re-renders, or questions like why a component re-rendered.
Optimize the performance of your Flutter app
Systematically discover and define your Ideal Customer Profile with firmographic criteria, buyer personas, scoring matrices, anti-ICP signals, and validation methodology.