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Found 7,705 Skills
Search and filter Observability logs using ES|QL. Use when investigating log spikes, errors, or anomalies; getting volume and trends; or drilling into services or containers during incidents.
Enable and configure Kibana audit logging for saved object access, logins, and space operations. Use when setting up Kibana audit, filtering events, or correlating Kibana and ES audit logs.
Create and manage SLOs in Elastic Observability using the Kibana API. Use when defining SLIs, setting error budgets, or managing SLO lifecycle.
Manage Serverless network security (traffic filters): create, update, and delete IP filters and AWS PrivateLink VPC filters. Use when restricting network access or configuring private connectivity.
Clean up text while preserving the writer's voice - minimal edits only
Query NVIDIA PTX ISA 9.1, CUDA Runtime API 13.1, Driver API 13.1, Programming Guide v13.1, Best Practices Guide, Nsight Compute, Nsight Systems local documentation. Debug and optimize GPU kernels with nsys/ncu/compute-sanitizer workflows. Use when writing, debugging, or optimizing CUDA code, GPU kernels, PTX instructions, inline PTX, TensorCore operations (WMMA, WGMMA, TMA, tcgen05), or when the user mentions CUDA API functions, error codes, device properties, memory management, profiling, GPU performance, compute capabilities, CUDA Graphs, Cooperative Groups, Unified Memory, dynamic parallelism, or CUDA programming model concepts.
Develop, debug, and optimize SGLang LLM serving engine. Use when the user mentions SGLang, sglang, srt, sgl-kernel, LLM serving, model inference, KV cache, attention backend, FlashInfer, MLA, MoE routing, speculative decoding, disaggregated serving, TP/PP/EP, radix cache, continuous batching, chunked prefill, CUDA graph, model loading, quantization FP8/GPTQ/AWQ, JIT kernel, triton kernel SGLang, or asks about serving LLMs with SGLang.
Write, debug, and optimize CUTLASS and CuTeDSL GPU kernels using local source code, examples, and header references. Use when the user mentions CUTLASS, CuTe, CuTeDSL, cute::Layout, cute::Tensor, TiledMMA, TiledCopy, CollectiveMainloop, CollectiveEpilogue, GEMM kernel, grouped GEMM, sparse GEMM, flash attention CUTLASS, blackwell GEMM, hopper GEMM, FP8 GEMM, blockwise scaling, MoE GEMM, StreamK, warp specialization CUTLASS, TMA CUTLASS, or asks about writing high-performance CUDA kernels with CUTLASS/CuTe templates.
Write, debug, and optimize Triton and Gluon GPU kernels using local source code, tutorials, and kernel references. Use when the user mentions Triton, Gluon, tl.load, tl.store, tl.dot, triton.jit, gluon.jit, wgmma, tcgen05, TMA, tensor descriptor, persistent kernel, warp specialization, fused attention, matmul kernel, kernel fusion, tl.program_id, triton autotune, MXFP, FP8, FP4, block-scaled matmul, SwiGLU, top-k, or asks about writing GPU kernels in Python.
Use this skill when the user asks to parse, perform multi-format document conversion or spatially extract text from an unstructured file (PDF, DOCX, PPTX, XLSX, images, etc.) locally without cloud dependencies.
Review code for bugs, security issues, and best practices. Use when asked to review a PR, diff, or code snippet.
Use when converting Java source files to idiomatic Kotlin, when user mentions "java to kotlin", "j2k", "convert java", "migrate java to kotlin", or when working with .java files that need to become .kt files. Handles framework-aware conversion for Spring, Lombok, Hibernate, Jackson, Micronaut, Quarkus, Dagger/Hilt, RxJava, JUnit, Guice, Retrofit, and Mockito.