Borrowing it
Nothing to install: this file belongs to tile-ai/tilelang-ascend. 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/tile-ai/tilelang-ascend/ascendc_pto/.agents/skills/tilelang-a5-sim-convert/SKILL.mdgit clone --depth 1 https://github.com/tile-ai/tilelang-ascendWrote 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/tile-ai/tilelang-ascend/tilelang-a5-sim-convert)<a href="https://agentmods.dev/skills/tile-ai/tilelang-ascend/tilelang-a5-sim-convert"><img src="https://agentmods.dev/badge/skills/tile-ai/tilelang-ascend/tilelang-a5-sim-convert/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/tile-ai/tilelang-ascend/tilelang-a5-sim-convert"><img src="https://agentmods.dev/badge/skills/tile-ai/tilelang-ascend/tilelang-a5-sim-convert.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 | $0.00093 | $0.01864 |
| Opus 5 | $0.00046 | $0.00932 |
| Sonnet 5 | $0.00019 | $0.00373 |
| Haiku 4.5 | $0.00009 | $0.00186 |
Grade A, and why
tilelang-a5-sim-convert 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 10d 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 — 173 lines — stays where its author put it; the contents beside it link to each section on GitHub.
TileLang A5 Camodel 仿真脚本转换
将任意 tilelang DSL 脚本转换为 A5 camodel 仿真可运行的独立脚本。
模板结构（260 行，只改两处）
模板文件：.agents/skills/tilelang-a5-sim-convert/scripts/run_a5_sim_template.py
行 1-24 import 语句 ← 不动
行 25-96 环境自动设置 ← 不动（_find_ascend_home, _source_cann, _find_sim_lib, setup）
行 99-133 加载 camodel 运行时 ← 不动（load_runtime, dev_malloc）
行 136-166 kernel 定义 ← ★ 第 1 处要改
行 169-260 main() 编译+运行+验证 ← 部分要改（详见下方）
工作流程
收到脚本路径后，按以下步骤执行：
Step 1: 运行解析脚本获取 kernel 信息
cd <tilelang-ascend-root>
python .agents/skills/tilelang-a5-sim-convert/scripts/parse_example.py <target_script>
输出 JSON，包含 kernel_name、buffers（shape/dtype 列表）。
Step 2: 读取模板 + 原始脚本
- 读取
.agents/skills/tilelang-a5-sim-convert/scripts/run_a5_sim_template.py - Read 目标脚本，找到 kernel 定义部分（
@T.prim_func或@tilelang.jit装饰的函数体）
Step 3: 生成 *_sim.py
输出路径：<原路径>/<原名>_sim.py（绝不覆盖原始文件）。
改动清单
改动 1：kernel 定义（模板 136-166 行）
| 原始脚本 | 仿真脚本 |
|---|---|
@tilelang.jit(out_idx=[-1]) |
删掉 |
def matmul(M, N, K, ...): |
def make_kernel(): |
T.Tensor((M, K), dtype) |
T.Tensor((1024, 256), "float16") ← 用 Step1 解析出的具体数值 |
T.alloc_L0C(..., "float16") |
T.alloc_L0C(..., "float") ← A5 pto-isa 要求 float32 |
func = matmul(...) 触发编译 |
删掉，编译在 main() 里统一处理 |
生成的代码结构：
def make_kernel():
import tilelang.language as T
@T.prim_func
def main(
A: T.Tensor((1024, 256), "float16"), # ← 具体数值
B: T.Tensor((256, 1024), "float16"),
C: T.Tensor((1024, 1024), "float16"),
):
# ... kernel 逻辑（和原始脚本一模一样）...
return main
改动 2：数据准备（模板 214-233 行）
a) 维度变量（第 215 行）
根据 Step1 的 buffers 设置：
# 原始模板（gemm 专用）
M, N, K = 1024, 512, 256
# 通用写法：从 buffers 提取
# buffers[0].shape = [M, K] → M = shape[0], K = shape[1]
# buffers[1].shape = [K, N] → N = shape[1]
# buffers[2].shape = [M, N]
如果不是矩阵（比如 1D/3D tensor），按实际 shape 处理。
b) 数据 dtype（第 216-218 行）
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 10d ago First seen · 173 lines · 93 tokens per session scan A 8025c7c38594
tilelang-a5-sim-convert is a skill published in the GitHub repository tile-ai/tilelang-ascend (364 stars, last pushed today), licensed MIT. It adds 93 tokens to every session and 1,864 once invoked, about $0.0005 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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