Triton Operator Precision Eval

Q: Why use Triton Operator Precision Eval on TypingMind?

Because you install it once and use it with any model. Triton Operator Precision Eval is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

Q: How do I install Triton Operator Precision Eval in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Ascend/agent-skills/tree/master/skills/triton-operator-precision-eval. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Q: Which AI models can use Triton Operator Precision Eval?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

Q: How many AI models can I use with Triton Operator Precision Eval?

As many as you like. As long as a model supports skills, you can use Triton Operator Precision Eval with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Q: Is the Triton Operator Precision Eval AI skill free?

Yes. It is published on GitHub by Ascend under the MulanPSL-2.0 license. You only pay your own AI provider for the tokens you use.

Q: What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

Q: How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

Organization

Ascend

triton-operator-precision-eval

Triton 算子精度评估。与 PyTorch 参考实现对比，自动计算误差指标，生成标准化精度报告。关键词：精度测试、precision evaluation、精度报告、accuracy verification。

Overview

Publisher	Ascend
Repository	`agent-skills`
Skill name	`triton-operator-precision-eval`
Stars	54
Forks	4
Bundled files	3
License	MulanPSL-2.0
Links	Skill source Repository

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
3 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by Ascend on GitHub. Read the source before you install it.

Installation

Install the Triton Operator Precision Eval AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/Ascend/agent-skills/tree/master/skills/triton-operator-precision-eval
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/Ascend/agent-skills/tree/master/skills/triton-operator-precision-eval

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/Ascend/agent-skills.git /tmp/agent-skills
mkdir -p .claude/skills
cp -r /tmp/agent-skills/skills/triton-operator-precision-eval .claude/skills/triton-operator-precision-eval

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Triton Operator Precision Eval in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Triton Operator Precision Eval on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Triton Operator Precision Eval is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Triton 算子精度评估

核心原则

精度是算子正确性的底线。验证通过前不做性能优化。

工作流

编写 Torch 参考实现（标杆）
编写测试用例（覆盖多种 shape × dtype × 边界情况）
在 NPU 上执行 Triton 算子和 Torch 参考实现
用 test_common.validate_cmp() 比对，生成精度报告

MANDATORY - READ ENTIRE FILE：编写测试前，完整阅读 scripts/test_common.py。

误差阈值

判定条件：MERE < 阈值且 MARE < 10 × 阈值

数据类型	阈值	MERE 上限	MARE 上限
float16	2⁻¹⁰ ≈ 9.77e-4	9.77e-4	9.77e-3
float32	2⁻¹³ ≈ 1.22e-4	1.22e-4	1.22e-3
bfloat16	2⁻⁷ ≈ 7.81e-3	7.81e-3	7.81e-2
int8/uint8/int16/uint16/int32/uint32/int64/uint64	完全相等	—	—
bool	完全相等	—	—

其中：

MERE = mean(|y_cal - y_ref| / |y_ref|)，即平均相对误差
MARE = max(|y_cal - y_ref| / |y_ref|)，即最大相对误差

报告必须包含

验证配置：算子名称、测试形状、dtype、核心数
精度标准：所有 dtype 的阈值及判定条件（MERE < 阈值且 MARE < 10 × 阈值）
验证结果：通过/失败总数
误差指标：MERE（平均相对误差）、MARE（最大相对误差）
判定条件

输出约定

精度报告写入算子目录下 precision_report.md（Markdown 格式）
精度评估脚本写入算子目录下 precision_eval.py
参考 examples/ 目录下的示例脚本和报告格式

反模式清单（NEVER）

❌ 不提供 Torch 参考实现就做精度验证
❌ 用错误阈值（如 FP16 用 FP32 的阈值）
❌ 不用 MERE/MARE 判定条件（仅用 rtol/atol 不够严格）
❌ 归约操作不升精度到 FP32
❌ 只测一种 dtype/shape 就断言正确
❌ 跳过边界情况（非对齐维度）
❌ 精度通过前做性能优化

检查清单

Torch 参考实现正确且在 NPU 上验证
覆盖多种 shape × dtype
归约操作用 FP32
生成标准化精度报告

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Triton Operator Precision Eval AI skill do?

Why use Triton Operator Precision Eval on TypingMind?

Because you install it once and use it with any model. Triton Operator Precision Eval is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Triton Operator Precision Eval in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Ascend/agent-skills/tree/master/skills/triton-operator-precision-eval. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Triton Operator Precision Eval?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Triton Operator Precision Eval?

As many as you like. As long as a model supports skills, you can use Triton Operator Precision Eval with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Triton Operator Precision Eval AI skill free?

Yes. It is published on GitHub by Ascend under the MulanPSL-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Triton 算子精度评估

核心原则

工作流

误差阈值

报告必须包含

输出约定

反模式清单（NEVER）

检查清单

Bundled files

Frequently asked questions

Related AI Skills

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