Borrowing it
Nothing to install: this file belongs to ROCm/aiter. 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/ROCm/aiter/main/.claude/skills/aiter-op-test/SKILL.mdgit clone --depth 1 https://github.com/ROCm/aiterWrote 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/skills/rocm/aiter/aiter-op-test)<a href="https://agentmods.dev/skills/rocm/aiter/aiter-op-test"><img src="https://agentmods.dev/badge/skills/rocm/aiter/aiter-op-test/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/skills/rocm/aiter/aiter-op-test"><img src="https://agentmods.dev/badge/skills/rocm/aiter/aiter-op-test.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00103 | $0.04492 |
| Opus 5.5 | $0.00041 | $0.01797 |
| Sonnet 5.5 | $0.00021 | $0.00898 |
| Haiku 4.5 | $0.00010 | $0.00449 |
Grade A, and why
aiter-op-test 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.
How it starts
The opening of the file, as written. The whole thing — 317 lines — stays where its author put it; the contents beside it link to each section on GitHub.
aiter op_test standard
How every aiter op test in op_tests/test_*.py must be built. The canonical
reference in-tree is op_tests/test_quant.py — match its shape. A test is
both a correctness check (vs a torch reference) and a perf sweep that
ends in a markdown summary table.
Follow this whenever you create a new test_*.py, rewrite an old one, or add
shapes/candidates to an existing one.
The hard rules
- Mirror
test_quant.py. Same imports, same decorator, same table-at-the-end flow. Don't invent a different structure. @benchmark()on the test fn. It logs the function's call args (the shape params) as table columns automatically and merges the dict youreturn. So the test fn signature is the table's left-hand columns — name params accordingly.- Candidates live in a dict; build
retin a loop. Per candidate record rawus, plusTFLOPSandTB/s, pluserr—ret[f"{name} us"],ret[f"{name} TFLOPS"],ret[f"{name} TB/s"],ret[f"{name} err"]. Never hand-write ratio columns. (TFLOPS/TB-s section below.) - torch is the reference only — compute it, compare against it, but do not
time it and do not put it in the table. (A pure-torch candidate is allowed
only when torch is one of the kernels under test, e.g.
torch.einsum.) - Time with
run_perftest, check withcheckAllclose— both, for every candidate. Compare in fp32 (.to(dtypes.fp32)). - End with a markdown summary table — one per test function. Sweep the shape
lists with
itertools.product, collect per-shape dicts into apd.DataFrame, print viaaiter.logger.info("... :\n%s", df.to_markdown(index=False)). A file with several test fns of different arg signatures emits one table each — never force-merge them (it scatters NaN columns). Mandatory — a test with no summary table is incomplete. __main__guard. All argparse + the sweep loop go insidemain(), called underif __name__ == "__main__": main(). The reference (run_torch) and the@benchmarktest fn stay at module top level so other scripts canimportthem for combination testing.- Standard argparse only. Use
-d/--dtype,-b/--batch,-s/--mnkplus op-specific sweep axes as needed (e.g.--layout,--modes,--mtp). Those are legitimate data lists. Do not add bespoke behavior-toggle flags (no--dsv4, no--only-*) — every flag is a list the sweep iterates. - Run clean on every supported card. Gate on
get_gfx()inmain()so the test passes on all supported archs; arch-unsupported ops/candidates are filtered out before launch. Prefer the kernel's arch-dispatching wrapper over a file-per-arch (full section below).
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 · 317 lines · 103 tokens per session scan A b0b2b9ad8666
aiter-op-test is a skill published in the GitHub repository ROCm/aiter (598 stars, last pushed today), licensed MIT. It adds 103 tokens to every session and 4,492 once invoked, about $0.0004 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.
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