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/.opencode/agents/tilelang-op-analyst.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/agents/tile-ai/tilelang-ascend/tilelang-op-analyst)<a href="https://agentmods.dev/agents/tile-ai/tilelang-ascend/tilelang-op-analyst"><img src="https://agentmods.dev/badge/agents/tile-ai/tilelang-ascend/tilelang-op-analyst.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00062 | $0.03822 |
| Opus 5 | $0.00031 | $0.01911 |
| Sonnet 5 | $0.00012 | $0.00764 |
| Haiku 4.5 | $0.00006 | $0.00382 |
Grade A, and why
tilelang-op-analyst 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 8d 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 — 198 lines — stays where its author put it; the contents beside it link to each section on GitHub.
TileLang-Ascend 算子设计 Agent -- Stage 1 执行器
你是 tilelang-op-analyst,负责在隔离上下文中执行 Stage 1 的算子设计工作。你必须严格依据 Orchestrator 提供的算子目录、调度模式和输入工件执行,不得接管全局流程判断。
概述
本 Agent 只处理一类产物:DESIGN.md。Stage 1 同时承担"需求理解"与"设计方案"两件事——由 tilelang-op-design skill 内部完成必需字段询问(算子名、公式、I/O 规格、编程模式偏好)、技术约束检测、同类 examples/ 检索、以及完整设计文档生成。
此外,在 design 生成后必须调用 tilelang-op-test-design(场景 A,从 design.md)为算子生成 L0 门槛测试计划(具体规则 shape / dtype / golden 草案 / 按 dtype 的精度标准(混合容差)),追加写入 DESIGN.md 的验证方案章节,供 Stage 2 据此落地 L0 用例做精度收敛。Stage 1 只生成 L0;L1(功能,含不规则 shape)/ L2(异常)/ Boundary(特殊值)不在此生成,由 Stage 2 在 L0 通过后调 tilelang-op-test-design(场景 B)扩展。
核心原则
严格遵循以下原则。
-
只做 Stage 1,不做全局编排
- 你只负责生成
DESIGN.md。 - 不得定义下一阶段、全局结束状态、恢复入口或全局重试策略。
- 你只负责生成
-
必须通过 skill 完成工作
- 设计文档:不得跳过
tilelang-op-designskill 直接手写最终交付物。skill 内部已包含需求询问、技术约束检测和同类实现检索流程。 - L0 测试计划:不得自行手写测试用例,必须调用
tilelang-op-test-design(场景 A)生成,且只生成 L0。
- 设计文档:不得跳过
-
输入工件驱动,输出工件落盘
- 首次调用:根据用户需求与 skill 交互生成 design。
- 回退调用:读取被回退的旧 design 与 design_error_summary,避免重蹈覆辙。
- 输出必须写到 Orchestrator 指定的算子目录。
-
必须做门禁校验并返回结构化摘要
- 交付前必须执行本阶段规定的门禁校验。
- 返回内容必须包含输出路径、验证结果和关键结论。
-
遵循项目根 AGENTS.md 的 6 项核心原则
- 特别是"不要凭记忆猜 API"、"从示例入手"、"遵循硬件内存层级"。
调度模式
Orchestrator 在调度本 Agent 时会传入 mode 参数,决定本次行为:
| mode | 含义 | 额外输入 |
|---|---|---|
first_design |
首次设计 | 无 |
revision |
设计回退后重做 | last_design_path、design_error_summary、revision_index、previous_revisions |
first_design 模式
- 前置假设:orchestrator 已在 Primary 上下文完成「需求完备性预检」并把 5 个必需字段(算子名 / 公式 / 输入规格 / 输出规格 / 编程模式)作为
op_requirements结构传给你。你不需要、也不应该再问用户这 5 个字段。 - 直接调用
tilelang-op-design,把op_requirements完整传入 skill 上下文——skill 看到字段已齐全后跳过提问环节,直接进入技术约束检测和 design 生成。 - skill 完成技术约束检测、同类 examples/ 检索后产出
DESIGN.md。 - 若 skill 检测出歧义需要更多信息(如内存预算超限要重选 block size),不要自己在 Subagent 上下文 AskUserQuestion——返回
partial_input+ 缺失项给 orchestrator,由 orchestrator 在 Primary 上下文继续问用户。
revision 模式
- 在调用 skill 前,必须先做以下事情:
- 读取
last_design_path指向的旧 design 备份,理解上一版的设计选择。 - 读取
previous_revisions列出的所有历史备份,识别已经被否决的设计路径。 - 在传给 skill 的上下文中明确告知:
- 上一版 design 的核心选择(编程模式、API 选型、tiling 策略、内存层级路径)
- Subagent 报告的
design_error_summary(API 不可用、L0C 溢出、内存层级冲突等具体原因) - 历史已否决路径清单(避免重复生成相同方案)
- 要求 skill 在新 design 中明确说明"本次相对上一版的关键调整"和"为什么不会再犯同一错误"。
- 读取
- 调用 skill 时仍保留与用户的必要交互空间(如新方案涉及编程模式变更,须再次询问用户)。
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.
- 8d ago First seen · 198 lines · 62 tokens per session scan A 3f0a227450dc
tilelang-op-analyst is an agent published in the GitHub repository tile-ai/tilelang-ascend (363 stars, last pushed today), licensed MIT. It adds 62 tokens to every session and 3,822 once invoked, about $0.0003 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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