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Performs fast text search with one-shot patterns that minimize iterations by getting files, lines, and context in a single call. Use this skill to replace grep when searching for text patterns, finding specific code locations, or getting context around matches
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
Scaffold and operate an autonomous Triton GPU-kernel optimization loop on FlashInfer-Bench + Modal, modeled on the MLSys-2026 DSA contest winner (Dogacel/auto-gpu-kernel). Provides a ready-to-run template (CLAUDE.md, .claude/commands/{optimize,benchmark,log-experiment}, .claude/agents/{profiler,research,workload-inspector}, Modal benchmark + paired A/B harness, packed-solution scripts), plus customization guidance for retargeting to a different FlashInfer-Bench kernel definition. Use when the user wants to (1) bootstrap an autonomous Triton-kernel optimizer for any FlashInfer-Bench kernel, (2) set up an experiment-logged optimization loop with sub-agents for profiling, workload inspection, and plateau-breaking research, (3) run cloud-only (no local GPU) GPU benchmarking via Modal B200, or (4) reproduce or adapt the auto-gpu-kernel architecture for FlashInfer competition tracks or similar kernel-optimization workflows.
Provides guidance for writing and benchmarking optimized CUDA kernels for NVIDIA GPUs (H100, A100, T4) targeting HuggingFace diffusers and transformers libraries. Supports models like LTX-Video, Stable Diffusion, LLaMA, Mistral, and Qwen. Includes integration with HuggingFace Kernels Hub (get_kernel) for loading pre-compiled kernels. Includes benchmarking scripts to compare kernel performance against baseline implementations. Use for kernel types: attention, rmsnorm, rope, adaln, geglu, benchmark, transformers, diffusers, huggingface-kernels, get_kernel.
Investigate a consistently failing SGLang CI test to find the root cause — whether it is a code regression from a specific PR, a hardware/runner-specific issue, or an environment change. Optionally reproduces the failure on a remote GPU server over SSH + Docker. Use when an SGLang CI test is failing on main, you need to bisect which PR introduced a regression, you suspect a runner-specific or GPU-specific issue, or you want to reproduce a CI failure on a remote server.
Step-by-step tutorial for adding new CUDA kernels to FlashInfer
Write a Statement on Education (Teaching Statement / Teaching Philosophy / Teaching Vision / "onderwijsvisie") for an academic faculty application, especially in Computer Science / AI / Data Science at research universities. Covers structure (1–3 page format), evaluation rubrics, evidence-based pedagogy, supervision frameworks, US vs Dutch/EU framing, TU Delft programme curricula (BSc CSE, MSc CS, MSc DSAIT) with verified course mapping, real-world example anatomies, and a section-by-section drafting workflow that produces a credible, specific, non-generic statement. Use whenever an applicant must write or revise a teaching statement, education statement, teaching vision, teaching philosophy, or onderwijsvisie — particularly for TU Delft, ETH, KTH, Cambridge, Imperial, or any EU/NL CS faculty position.
Generate an Obsidian-format CUDA StudyVault for learning NVIDIA's GPU programming stack. Three modes: (1) Curriculum mode (default, no source files needed) builds a prereq-ordered vault around the 6-topic learning path: CUDA kernels (threads/blocks/warps, memory hierarchy, TMA, WGMMA, cp.async), CUTLASS + CuTe (Layout/Tensor, MMA atoms, GEMM pipelines), cuTile (Python tile DSL), open-gpu-kernel-modules (RM, GSP firmware, UVM, kernel-open), NCCL (collectives, NVLink-SHARP, transports), and NVSHMEM (PGAS, symmetric heap, IBGDA); (2) Codebase mode generates an onboarding vault from a real CUDA/CUTLASS/NCCL/NVSHMEM/driver source tree; (3) Document mode turns NVIDIA whitepapers, GTC slide decks, and PDFs into study notes. Mode is auto-detected from CWD markers. Use when the user wants to learn CUDA from scratch, onboard onto a GPU codebase, build an exercise list for CUDA topics, or preprocess NVIDIA PDFs/docs into structured study material. Pair with the `cuda-tutor` skill to quiz against the resulting vault.
| name | better-grep |
| description | Performs fast text search with one-shot patterns that minimize iterations by getting files, lines, and context in a single call. Use this skill to replace grep when searching for text patterns, finding specific code locations, or getting context around matches |
Always invoke ripgrep skill for text search - do not execute bash commands directly.
Invoke ripgrep skill for fast text search with one-shot patterns. Use -e 'pattern' -n -C 2 to get files, line numbers, and context in a single call.
This minimizes iterations and context usage. Always prefer getting line numbers and surrounding context over multiple search attempts.
Common workflow: ripgrep skill → other skills (fzf, bat, sd, analyzing-code-structure) for interactive selection, preview, or modification.
Grep tool (built on ripgrep) - Use for structured searches:
type parameter-F, -v, -w, or pipe composition are not neededBash(rg) - Use for one-shot searches needing special flags or composition:
-F)-v)-w)--files-without-match)-U)-P)-r)--hidden, --no-ignore)| head, | wc -l, | sort)Glob tool - Use for file name/path matching only (not content search)
Load ripgrep guide when needing:
-U), PCRE2 (-P), replace preview (-r)--files-without-match, --hidden, --no-ignore-t flags)The guide focuses on practical patterns for getting targeted results in minimal calls.