What is amk-megakernel-optimizer?

An agent that searches for faster settings for an AutoMegaKernel running a Llama-family model on a GPU. AutoMegaKernel compiles a model into one persistent CUDA kernel.

It helps propose, evaluate, and keep or reject GPU scheduling changes, including tiling, fusion, memory, threading, and asynchronous-copy settings. It reports measured speed improvements over AutoMegaKernel's own default schedule.

It automates repeated performance experiments while requiring each candidate to remain correct. It restricts tuning to approved schedule and kernel settings, protecting the model's core code and interface.

How RightNow-AI/AutoMegaKernel configures Claude Code

Reuse

Borrowing it

Nothing to install: this file belongs to RightNow-AI/AutoMegaKernel. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file

curl -O https://raw.githubusercontent.com/RightNow-AI/AutoMegaKernel/main/.claude/agents/amk-megakernel-optimizer.md

Clone the repo

git clone --depth 1 https://github.com/RightNow-AI/AutoMegaKernel

Made for: Claude Code.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

README.md

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Your own site

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Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

Your own site · 80×15

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Per session 62 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.

When invoked 1,759 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

Origin original No closer match found in the catalogue.

Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

Model	Per session	Once invoked
Fable 5.1	$0.00062	$0.01759
Opus 5.5	$0.00025	$0.00704
Sonnet 5.5	$0.00012	$0.00352
Haiku 4.5	$0.00006	$0.00176

Measured 2mo ago against content hash 94f65ca03a68, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

amk-megakernel-optimizer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.claude/agents/amk-megakernel-optimizer.md · 110 lines

How it starts

The opening of the file, as written. The whole thing — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.

AMK Megakernel Optimizer, operating manual

You optimize a HuggingFace Llama-family model's AutoMegaKernel (AMK) schedule. AMK compiles the model into ONE persistent CUDA megakernel; your job is to search the edit surface for a schedule that is correct and measurably faster than AMK's own default, then record it to the campaign orchestrator. Read HARNESS.md (the full contract) before you start.

What you may edit (and ONLY this)

The edit surface is a ScheduleConfig (a JSON dict of typed knobs) plus an optional kernel_knobs sub-object. Never raw kernel code, never vm/, never Task.sm, never the frozen ABI.

ScheduleConfig knobs (see HARNESS.md §2 for choices): tiling.gemv.N_tile, tiling.attention.kv_block, fusion_grouping, sm_assignment, pipelining_depth, page_allocation, threads_per_block, smem_bytes_per_block.

kernel_knobs (the MegakernelVM build levers that actually move measured latency): cols_per_warp, cpasync, cpa_stages, cpa_cols. Embed them under the reserved "kernel_knobs" key inside the config JSON.

Read the live surface programmatically, do not guess:

uv run python amk_cli.py propose <model> --gpu <gpu>   # incumbent config + search_space (with choices)

The HARD honesty rules (you MUST obey and state these)

Correctness FIRST. A latency is NEVER reported without a correctness PASS vs the CPU ReferenceVM. Keep a candidate only if it is correct AND >=1% faster than the incumbent.
validate-before-launch. An unsafe ScheduleConfig is a clean REJECTED (proven deadlock/race-free), never a hung GPU. A rejected/incorrect config has NO latency.
Edit surface = ScheduleConfig + kernel_knobs ONLY, never kernel code, never vm/, never the frozen ABI.
Measured latency is drift-robust (interleaved keep/revert vs the resident incumbent); physically-impossible sub-roofline latencies are withheld as artifacts.
All speedups are vs AMK's OWN baseline, NOT a claim of beating cuBLAS/vLLM (AMK is currently within ~13% of cuBLAS at batch-1, behind it). Report it this way, honestly, even at ~1.0x.

Read the full file on GitHub · 110 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

2mo ago First seen · 110 lines · 62 tokens per session scan A 94f65ca03a68

Subscribe to this mod's changes

amk-megakernel-optimizer is an agent published in the GitHub repository RightNow-AI/AutoMegaKernel (161 stars, last pushed 1mo ago), licensed MIT. It adds 62 tokens to every session and 1,759 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Data and AI GPU Hardware and IoT agent-harness cuda gpu-programming kernel-fusion llm-inference

amk-megakernel-optimizer next to 6 agents from other repositories
Mod	Type	Stars	Last push	Licence	Scan	Tokens	Works with
amk-megakernel-optimizerRightNow-AI/AutoMegaKernel	Agent	161	1mo ago	MIT	A	62	Claude Code
edge-ai-engineerpjt222/agent-almanac	Agent	43	yesterday	MIT	A	38	Claude Code
kernel-optimizerpjt222/agent-almanac	Agent	43	yesterday	MIT	A	30	Claude Code
google-colab-expertandisab/swe-marketplace	Agent	21	yesterday	MIT	A	282	Claude Code
intel-gpu-ai-skills-agentintel/gpu-ai-skills	Agent	49	yesterday	Apache-2.0	A	37	Claude Code
iot-data-specialistatretyak1985/swarmery	Agent	7	yesterday	Apache-2.0	A	20	Claude Code
kpzpunt-labs/vox	Agent	3	today	MIT	A	37	Claude Code

AutoMegaKernel: Agent for Claude Code