AI SummaryDrive a profile → modify → benchmark → log → commit loop on a GPU kernel until it runs faster than the reference. The user provides at minimum a kernel; everything else (reference, inputs, bench script, hints) is optional. Does NOT apply when: Before doing anything else, establish the workspace — th
Install
Copy this and paste it into Claude Code, Cursor, or any AI assistant:
I want to install the "ako4all" skill in my project. Please run this command in my terminal: # Install skill into your project mkdir -p .claude/skills/AKO4ALL && curl --retry 3 --retry-delay 2 --retry-all-errors -o .claude/skills/AKO4ALL/SKILL.md "https://raw.githubusercontent.com/TongmingLAIC/AKO4ALL/main/SKILL.md" Then restart Claude Code (or reload the window in Cursor) so the skill is picked up.
Description
Drive an agentic loop that iteratively optimizes a GPU kernel for maximum speedup. Use this skill whenever the user wants to optimize / speed up / benchmark a GPU kernel (CUDA, Triton, TileLang, C++, Python), mentions AKO / AKO4ALL / AKO4X / agentic kernel optimization, asks to "make this kernel faster", or has a kernel they want measured against a PyTorch reference. The skill handles setup, profiling (ncu), correctness checking, iteration logging, and git commits. Bootstraps a workspace in any directory the user points at.
AKO4ALL — Agentic Kernel Optimization
Drive a profile → modify → benchmark → log → commit loop on a GPU kernel until it runs faster than the reference. The user provides at minimum a kernel; everything else (reference, inputs, bench script, hints) is optional.
When this skill applies
• "optimize this kernel" / "speed up this CUDA / Triton / TileLang kernel" • "run AKO / AKO4ALL on ..." • "benchmark this kernel against PyTorch" • "iterate on this kernel until it's faster" • mentions of ncu, kernel profiling, GPU speedup target Does NOT apply when: • User wants to write a new kernel from scratch with no optimization target — just write code, no loop. • User wants Codex / GPT to review or implement — use codex:rescue instead. • User wants generic performance advice for code that isn't a GPU kernel.
First action
Before doing anything else, establish the workspace — the directory the loop runs in. It is typically the user's CWD, or a subdirectory / path they name in the prompt.
Inventory the workspace + prompt
Browse the workspace (don't run a fixed checklist — look around) and read the user's prompt to identify what the loop needs: • Kernel (required) — the code to optimize • Reference (optional) — correctness golden • Input data (optional) — data files the kernel consumes (.npz, .bin, shape lists, custom formats, etc.) • Knowledge (optional) — reference materials the user wants you to consult: algorithm notes, papers, design docs, prior PRs. Typically under knowledge/ but anywhere the user points at. • Bench mode — user-provided bench script vs. default bench/kernelbench/ evaluator • Scaffold presence — whether bench-wrapper.sh, HINTS.md, ITERATIONS.md, bench/kernelbench/ are already at workspace root Whether the workspace follows AKO4ALL's source/ / knowledge/ / bench/ naming or some entirely different layout is not the signal. What matters is whether you can identify each item above with confidence.
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