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-op-design/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-op-design)<a href="https://agentmods.dev/skills/tile-ai/tilelang-ascend/tilelang-op-design"><img src="https://agentmods.dev/badge/skills/tile-ai/tilelang-ascend/tilelang-op-design/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-op-design"><img src="https://agentmods.dev/badge/skills/tile-ai/tilelang-ascend/tilelang-op-design.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 | $0.00092 | $0.03188 |
| Opus 5 | $0.00046 | $0.01594 |
| Sonnet 5 | $0.00018 | $0.00638 |
| Haiku 4.5 | $0.00009 | $0.00319 |
Grade A, and why
tilelang-op-design 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 — 191 lines — stays where its author put it; the contents beside it link to each section on GitHub.
TileLang-Ascend 算子设计文档生成
1. 目标
根据算子需求信息，生成一份完整的 TileLang-Ascend 算子设计文档（design.md），涵盖以下核心决策：
- 编程模式选型：Developer / Expert / 混合模式
- API 映射：将数学公式拆解为 TileLang DSL 原语组合
- 内存层级规划：GM → L1/UB → L0 的数据搬运路径
- Tiling 策略：Block 划分与 Tile Shape 设计
- 循环结构：T.Parallel / T.serial / T.Pipelined / T.Persistent 的选择
- 同步策略：自动同步 vs 手动同步标志
- 验证方案：Golden 函数与 L0 门槛测试计划（完整分层套件 L1/L2/Boundary 由 tilelang-op-test-design 生成）
2. 输入要求
必需信息
| 字段 | 说明 |
|---|---|
| 算子名称 | 如 softmax、layer_norm、flash_attention |
| 数学公式 | 算子的数学表达，如 $\text{softmax}(x_i) = e^{x_i} / \sum e^{x_j}$ |
| 输入张量规格 | shape、dtype |
| 输出张量规格 | shape、dtype |
| 编程模式偏好 | Developer / Expert / 混合 |
| 迁移算子路径 ⭐ | 原算子文件路径（迁移时必需），用于获取 golden 实现 |
| 输出形状 ⭐ | 原算子输出 shape（迁移时必需），如 (N, M) 或 (M, N) |
迁移算子时必须提供原算子路径和输出形状，否则无法证明迁移正确性。Golden 实现一致性要求详见 tilelang-op-develop checklist.md #9 Golden 实现一致 / #10 输出形状匹配。
提问规则（必须严格遵守）：
- 优先使用调用方传入的字段：若调用方（如
@tilelang-op-orchestrator通过 analyst 传入op_requirements结构）已经提供了字段值，全部跳过提问，直接进入技术约束检测和 design 生成 - 每次只询问一个字段：使用
question工具时，questions数组中只包含一个元素 - 按表格顺序依次询问：算子名称 → 数学公式 → 输入张量规格 → 输出张量规格 → 编程模式偏好
- 已提供的字段跳过：如果用户在初始请求中已提供某个字段的值，跳过该字段继续下一个
- 示例：
- 第 1 次询问：只问"数学公式"
- 用户回答后，第 2 次询问：只问"输入张量规格"
- 以此类推
⚠️ 当被 orchestrator → analyst Subagent 链路调度时：
- analyst 会把 orchestrator 在 Primary 上下文预检收集到的
op_requirements完整传入 - 此时 5 个必需字段应当全部已 provided，跳过整个提问环节
- 若 skill 仍发现字段歧义或缺漏，不要在当前 Subagent 上下文调用
AskUserQuestion（透传不到真实用户），而是让 analyst 返回partial_input+ 缺失字段名给 orchestrator，由 orchestrator 在 Primary 上下文追问
推荐信息
| 字段 | 说明 |
|---|---|
| 典型配置 | 常用的 shape 组合与优先级 |
| 参考实现 | PyTorch / NumPy 参考代码 |
| 性能目标 | 目标吞吐量或延迟 |
| 动态轴说明 | 哪些维度在运行时变化 |
若用户未提供必需信息中的任一项，通过提问补全后再继续。
3. 技术约束（必须遵守）
本项目为 TileLang-Ascend（华为昇腾 NPU），与 GPU 版 TileLang 有显著差异。外部参考实现不可直接使用，必须转换为 Ascend 兼容方案。
生成 design.md 前必须执行强制检测：三维 Kernel、threads 参数、动态循环边界、GPU 专用 API、GEMM 非整除、L0C 溢出等。
What ships with it
6 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 · 191 lines · 92 tokens per session scan A 1df54d4c8494
tilelang-op-design is a skill published in the GitHub repository tile-ai/tilelang-ascend (364 stars, last pushed today), licensed MIT. It adds 92 tokens to every session and 3,188 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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