aiter: Skill for Claude Code

.claude/skills/aiter-op-test/SKILL.md

aiter-op-test is a skill for Claude Code from ROCm/aiter. It costs 103 tokens per session (4,492 once invoked), scanned A, original, MIT.

A standard for AITER operation tests that check results against a PyTorch reference and measure performance across candidate implementations. It also defines the final Markdown summary table.

In plain words
What is it for?
Use it when creating or rewriting AITER operation tests, adding candidate implementations, testing new shapes, or reporting runtime, throughput, and error results.
Why use it?
It keeps tests consistent and makes them both correctness checks and performance comparisons without timing the reference implementation.

Skill for Claude Code

Written for Claude Code: argument-hint in frontmatter.

This is ROCm/aiter's own configuration. It tells Claude Code how to work on aiter itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything aiter configures →

Reuse

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.

Copy the file
curl -O https://raw.githubusercontent.com/ROCm/aiter/main/.claude/skills/aiter-op-test/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/ROCm/aiter

Made for: Claude Code.

Wrote this? Show the measurements

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README.md
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Per session 103 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 4,492 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. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
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.

ModelPer sessionOnce 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

Measured 2mo ago against content hash b0b2b9ad8666, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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.

.claude/skills/aiter-op-test/SKILL.md · 317 lines

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

  1. Mirror test_quant.py. Same imports, same decorator, same table-at-the-end flow. Don't invent a different structure.
  2. @benchmark() on the test fn. It logs the function's call args (the shape params) as table columns automatically and merges the dict you return. So the test fn signature is the table's left-hand columns — name params accordingly.
  3. Candidates live in a dict; build ret in a loop. Per candidate record raw us, plus TFLOPS and TB/s, plus err — 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.)
  4. 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.)
  5. Time with run_perftest, check with checkAllclose — both, for every candidate. Compare in fp32 (.to(dtypes.fp32)).
  6. End with a markdown summary table — one per test function. Sweep the shape lists with itertools.product, collect per-shape dicts into a pd.DataFrame, print via aiter.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.
  7. __main__ guard. All argparse + the sweep loop go inside main(), called under if __name__ == "__main__": main(). The reference (run_torch) and the @benchmark test fn stay at module top level so other scripts can import them for combination testing.
  8. Standard argparse only. Use -d/--dtype, -b/--batch, -s/--mnk plus 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.
  9. Run clean on every supported card. Gate on get_gfx() in main() 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).

Read the full file on GitHub · 317 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.

  1. 2mo ago First seen · 317 lines · 103 tokens per session scan A b0b2b9ad8666

Subscribe to this mod's changes

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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