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.
curl -O https://raw.githubusercontent.com/RightNow-AI/AutoMegaKernel/main/.claude/commands/amk-autoresearch.mdgit clone --depth 1 https://github.com/RightNow-AI/AutoMegaKernelWrote 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.
[](https://agentmods.dev/commands/rightnow-ai/automegakernel/amk-autoresearch)<a href="https://agentmods.dev/commands/rightnow-ai/automegakernel/amk-autoresearch"><img src="https://agentmods.dev/badge/commands/rightnow-ai/automegakernel/amk-autoresearch/github.svg" alt="Measured on agentmods" height="20"></a>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.
<a href="https://agentmods.dev/commands/rightnow-ai/automegakernel/amk-autoresearch"><img src="https://agentmods.dev/badge/commands/rightnow-ai/automegakernel/amk-autoresearch.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00025 | $0.00764 |
| Opus 5 | $0.00013 | $0.00382 |
| Sonnet 5 | $0.00005 | $0.00153 |
| Haiku 4.5 | $0.00003 | $0.00076 |
Grade A, and why
amk-autoresearch 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 11d 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.
What it actually says
Launch the unattended AutoMegaKernel (AMK) autoresearch driver on $ARGUMENTS (parse as
model [gpu] [minutes|iters]; default gpu = rtx5090; default model = toy). This is the
headless keep/revert campaign, point it at a (model, gpu), give it a budget, and it runs the
whole correctness-gated methodology unattended, resumable and crash-proof, growing the flywheel.
HARD HONESTY RULES (state them, obey them):
- Correctness FIRST: NEVER a latency without a correctness PASS vs the CPU ReferenceVM. Keep iff correct AND >= 1% faster than the incumbent.
- validate-before-launch: an unsafe ScheduleConfig is a clean REJECTED, never a hung GPU.
- Edit surface is ScheduleConfig + kernel_knobs ONLY, never kernel code, never vm/ or the ABI.
- Measured-gpu latency is drift-robust; impossible sub-roofline latencies are withheld.
- The morning "best" is a speedup vs AMK's OWN default schedule, NOT a claim of beating cuBLAS/vLLM (AMK is within ~13% of cuBLAS at batch-1, behind it).
Do this:
- Confirm the environment first with
amk_doctor()(CLI:amk doctor), torch/cuda availability, device name, registered targets. For a realmeasured-gpucampaign you need a CUDA GPU; otherwise the fitness is analyticpredicted(usedevice="cpu"). - Launch the driver with
amk_autoresearch(model, gpu, minutes=<M>, iters=<N>, device="auto", overnight=<bool>, cold=<bool>). CLI fallback:amk autoresearch <model> --gpu <gpu> --minutes <M> --device cuda [--overnight] [--cold](or--iters <N> --device cpufor a fast deterministic run). Useovernight=truewith a longminutes(e.g. 480 for ~8h): no plateau-stop, basin-hops to fresh regions while always preserving the global best, bounded memory, checkpointed every iteration (re-run the same command to continue), and writes a wake-up report. - It is resumable + crash-proof: a CUDA error / timeout in one iteration is logged and the run continues. Re-running the same command continues the same campaign.
- When it finishes (or to inspect progress), report from the orchestrator:
amk_orchestrate_status(),amk_orchestrate_report()(CLI:python amk_orchestrate.py status/report), and for an overnight run readworkspace/amk_overnight_report.md(best config + speedup-vs-baseline + milestones + restart count).
Report: best schedule_id, best latency_us + latency_kind, pct_of_roofline, and the
speedup vs AMK's own default schedule (NOT vs cuBLAS/vLLM).
Use only the canonical names: MCP amk_doctor/amk_autoresearch/amk_orchestrate_status/
amk_orchestrate_report; CLI amk doctor|autoresearch and python amk_orchestrate.py status|report.
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.
- 11d ago First seen · 43 lines · 25 tokens per session scan A dad98d17208f
amk-autoresearch is a command published in the GitHub repository RightNow-AI/AutoMegaKernel (138 stars, last pushed 2mo ago), licensed MIT. It adds 25 tokens to every session and 764 once invoked, about $0.0001 per session on Opus 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.
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