Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add mindspore-ai/akg --skill pypto-case-matvecgit clone --depth 1 https://github.com/mindspore-ai/akgWrote 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/mindspore-ai/akg/pypto-case-matvec)<a href="https://agentmods.dev/skills/mindspore-ai/akg/pypto-case-matvec"><img src="https://agentmods.dev/badge/skills/mindspore-ai/akg/pypto-case-matvec/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/mindspore-ai/akg/pypto-case-matvec"><img src="https://agentmods.dev/badge/skills/mindspore-ai/akg/pypto-case-matvec.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 | $0.00032 | $0.00477 |
| Opus 5 | $0.00016 | $0.00238 |
| Sonnet 5 | $0.00006 | $0.00095 |
| Haiku 4.5 | $0.00003 | $0.00048 |
Grade A, and why
pypto-case-matvec 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 9d 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
Matrix-Vector Multiplication (K > 65535)
A: (256, 131072), B: (131072, 1) -> C: (256, 1)
K=131072 超过 pypto.matmul 限制（最后一维 <= 65535），用 sum(a * b_row, dim=1) 替代。
def create_matvec_sum_kernel(a_shape, b_shape):
out_shape = (a_shape[0], 1)
@pypto.frontend.jit(...)
def matvec_sum_kernel(
a: pypto.Tensor(a_shape, pypto.DT_FP32),
b_row: pypto.Tensor(b_shape, pypto.DT_FP32),
) -> pypto.Tensor(out_shape, pypto.DT_FP32):
output = pypto.tensor(list(out_shape), pypto.DT_FP32)
pypto.set_vec_tile_shapes(1, 8192)
output[:] = pypto.sum(a * b_row, dim=1, keepdim=True)
return output
return matvec_sum_kernel
class ModelNew(torch.nn.Module):
def forward(self, A, B):
assert A.dim() == 2
assert tuple(A.shape) == (256, 131072)
assert B.dim() == 2
assert tuple(B.shape) == (131072, 1)
A = A.contiguous()
# B: (K, 1) -> (1, K) 用于广播乘法
B_row = B.contiguous().reshape(1, -1)
return create_matvec_sum_kernel(tuple(A.shape), tuple(B_row.shape))(A, B_row)
关键点：forward 中 B.reshape(1, -1) 将列向量转为行向量，使 a * b_row 可广播。
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.
- 9d ago First seen · 46 lines · 32 tokens per session scan A a6f04d75b6f2
pypto-case-matvec is a skill published in the GitHub repository mindspore-ai/akg (259 stars, last pushed 29d ago), licensed Apache-2.0. It adds 32 tokens to every session and 477 once invoked, about $0.0002 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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