Agent skill

Doc Quality Checker Mindspore

by mindspore-ai in mindspore-ai/docs

检查文档质量的工具。当用户提到检查文档质量、审查文档、文档检查、文档审查、lint文档, 或者提供文档URL/PR链接/本地文件路径要求检查时触发。支持通用性检查、教程检查和API文档检查。

Apache-2.0Auto-check passedDevelopment

Install Doc Quality Checker Mindspore

skills CLI
$ npx skills add mindspore-ai/docs --skill doc-quality-checker-mindspore -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install mindspore-ai/docs doc-quality-checker-mindspore --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/mindspore-ai/docs.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/doc-quality-checker-mindspore .claude/skills/doc-quality-checker-mindspore && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
doc-quality-checker-mindspore
GitHub stars
167
Token cost
~2.1k tokens
SKILL.md length
293 words
Files
10 (incl. scripts)
Skills in repo
3
Repo updated
First seen
Licence
Apache-2.0

At a glance

检查文档质量的工具。当用户提到检查文档质量、审查文档、文档检查、文档审查、lint文档, 或者提供文档URL/PR链接/本地文件路径要求检查时触发。支持通用性检查、教程检查和API文档检查。

  • Works in 4 steps: 通用性检查(所有文档必须通过) → 教程检查(教程类文档额外检查) → API文档检查(API类文档额外检查) → …
  • Development work in your project
  • SKILL.md covers 输入解析, 检查类型判断, 检查规则 and 输出格式, plus 1 more section
  • Runs PowerShell and Shell scripts from its folder; calls bash; reaches api.atomgit.com

What it does

Doc Quality Checker Mindspore is an agent skill from mindspore-ai/docs. 检查文档质量的工具。当用户提到检查文档质量、审查文档、文档检查、文档审查、lint文档, 或者提供文档URL/PR链接/本地文件路径要求检查时触发。支持通用性检查、教程检查和API文档检查。 输入类型:1) 可访问的HTML页面URL;2) AtomGit PR链接(分析PR中的文档diff); 3) 本地文件夹路径。自动根据内容判断执行 通用+教程 或 通用+API 的检查组合。

Its SKILL.md is about 2.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 11 other files, including scripts (for example `rules/api_cpp_en_rules.md`, `rules/api_cpp_zh_rules.md` and `rules/api_python_consistency_rules.md`).

It sits in Development. It works with C++ and Python. The licence is Apache-2.0.

When your agent uses it

  • Development work in your project

Example prompts

  • “/doc-quality-checker-mindspore”

Requirements

  • Python 3
  • A Bash shell
  • PowerShell

Workflow steps

4 steps, taken from the step headings in SKILL.md.

  1. 通用性检查(所有文档必须通过)
  2. 教程检查(教程类文档额外检查)
  3. API文档检查(API类文档额外检查)
  4. 生成并保存报告

What it can do on your machine

Read from SKILL.md and the folder at commit b375e48. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 2 files in scripts/ (PowerShell and Shell), which the agent can run.

    Shell commands in SKILL.md call:

    • bash

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • api.atomgit.com

    Also links to:

    • atomgit.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Doc Quality Checker Mindspore loads about 2.1k tokens when it runs. Until then it costs about 56 tokens; SKILL.md has 293 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~56
When it runs · the whole SKILL.md, loaded when a task matches
~2.1k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from mindspore-ai/docs at commit b375e48, republished under its Apache-2.0 licence (© mindspore-ai). 293 words, ~2,119 tokens.

Download SKILL.mdSave it as .claude/skills/doc-quality-checker-mindspore/SKILL.md (or your agent's skills folder). This skill also uses 9 other files; get the full folder from GitHub.
name
doc-quality-checker-mindspore
description
检查文档质量的工具。当用户提到检查文档质量、审查文档、文档检查、文档审查、lint文档, 或者提供文档URL/PR链接/本地文件路径要求检查时触发。支持通用性检查、教程检查和API文档检查。 输入类型:1) 可访问的HTML页面URL;2) AtomGit PR链接(分析PR中的文档diff); 3) 本地文件夹路径。自动根据内容判断执行 通用+教程 或 通用+API 的检查组合。

文档质量检查器

输入解析

解析用户提供的输入,判断输入类型并获取内容:

输入类型识别方式获取内容方法
HTML页面URL以 http:// 或 https:// 开头且非Git链接使用 WebFetch 工具获取页面内容
AtomGit PR链接包含 atomgit.com 的PR URL使用 AtomGit API + Token 获取PR内容
本地文件夹本地路径使用 read 工具读取文件内容
PR链接处理流程

对于 AtomGit PR链接:

  1. 获取Token → 提示用户输入 AtomGit/Gitee 私人令牌(用于API认证)
  2. 解析PR链接获取仓库信息(owner)、仓库名(repo)和PR编号
  3. 调用脚本 scripts/fetch_pr.ps1 或 scripts/fetch_pr.sh 获取PR详情和diff内容:
    • 脚本路径:scripts/fetch_pr.ps1(Windows)或 scripts/fetch_pr.sh(Linux/Mac)
    • 使用 PRIVATE-TOKEN header 认证:--header 'PRIVATE-TOKEN: {token}'
    • API格式: https://api.atomgit.com/api/v5/repos/{owner}/{repo}/pulls/{pr_number}
  4. 识别diff中的文档文件(.md, .rst, .txt等)
  5. 分析文档变更内容

脚本用法:

powershell
# Windows
.\scripts\fetch_pr.ps1 -Token <token> -Owner <owner> -Repo <repo> -PrNumber <pr_number>

# Linux/Mac
bash scripts/fetch_pr.sh <token> <owner> <repo> <pr_number>

Token获取方式:

检查类型判断

根据输入内容特征自动选择检查组合:

特征检查组合
包含"安装"、"快速开始"、"教程"、"开发指南"等通用性检查 + 教程检查
包含"API"、"参数"、"返回值"、"函数"等通用性检查 + API文档检查
包含代码示例、函数签名、参数说明通用性检查 + API文档检查
包含步骤说明、截图指引、操作流程通用性检查 + 教程检查
无法判断时默认通用性检查 + 教程检查

检查规则

1. 通用性检查(所有文档必须通过)

规则文件为rules/doc_general_rules.md,支持所有格式文件的内容检查。

2. 教程检查(教程类文档额外检查)

规则文件为rules/doc_tutorials_rules.md,支持Markdown、reStructuredText、Jupyter Notebook等格式文件的内容检查。

3. API文档检查(API类文档额外检查)

重要:API文档检查必须同时包含以下两部分,缺一不可:

  1. 格式检查:检查文档本身的格式和质量(如参数说明、返回值、异常等)
  2. 一致性检查:检查中文API文档与英文源文件(Python/C++/YAML/RST目录)的对应关系
API文档检查流程
  1. 执行格式检查 → 按文档语言类型应用对应规则文件:

    • Python API中文文档(RST格式):rules/api_python_zh_rules.md
    • Python API英文注释(Python docstring/YAML):rules/api_python_en_rules.md
    • C++ API中文文档(MD格式):rules/api_cpp_zh_rules.md
    • C++ API英文注释:rules/api_cpp_en_rules.md
    • PR 场景:对每个修改的文件分别应用对应规则
    • 混合输入:同时存在多种类型时,分别按各自规则执行
  2. 查找对应源文件 → 按方向查找配对文件:

    • 中文文档 → 找英文源:按下文"英文源文件查找"方法定位(路径推断、YAML 搜索、别名链追踪、op 定义文件反查)
    • 英文源 → 找中文 RST/MD:
      • 直接映射:原名按模块路径推断中文路径
      • 别名场景:搜索各模块 __init__.py 中 from X import 原名 as 别名,所有别名的中文路径一并查找
  3. 执行一致性检查 → 使用 rules/api_python_consistency_rules.md,按输入类型选择子检查项:

    • 中文RST ↔ 英文源码:检查 Summary/Args/Returns/Raises 等字段的中英文对应关系(英文源可以是 Python docstring 或 YAML)
    • 别名场景:检查 AL-01~04(名称差异、重导出内容、YAML 名称匹配、废弃标注)
    • 英文RST目录:检查接口列表/数量/分组与中文RST一致
    • PR配对:中文修改需有英文对应,英文修改需有中文对应
英文源文件查找

当输入为中文API文档时,需要查找对应的英文源文件:

英文源类型后缀说明
Python代码.py包含docstrings的Python代码
C++代码.h, .cpp包含 /// 注释的C++代码
YAML配置.yaml, .ymlMindSpore算子/接口定义文件(Tensor方法等)
英文RST目录.rstAPI索引/目录文件(如 mindspore.nn.rst)

典型路径映射(含别名场景):

text
=== 直接映射 ===
中文RST(Python): docs/api/api_python/mindspore/nn/tanh.rst
英文Python代码: mindspore/python/mindspore/nn/tanh.py

中文RST(Python): docs/api/api_python/mindspore/Tensor/mindspore.Tensor.gather.rst
英文YAML文档: mindspore/ops/op_def/yaml/doc/gather_doc.yaml

中文RST(Python): docs/api/api_python/ops/mindspore.ops.func_strided_slice.rst
英文YAML文档: mindspore/ops/op_def/yaml/doc/strided_slice_doc.yaml

中文RST(Python): docs/api/api_python/mint/mindspore.mint.func_empty.rst
英文YAML文档: mindspore/ops/api_def/function_doc/empty_doc.yaml

中文RST(Python): docs/api/api_python/mindspore.nn.rst
英文RST目录: docs/api/api_python_en/mindspore.nn.rst

中文MD(C++): mindspore/docs/api/cpp_api/classmindspore_1_1Tensor.md
英文C++头文件: mindspore/core/include/mindspore/core/ops/tensor_impl.h

=== 别名追踪映射 ===
中文RST(Python): docs/api/api_python/mint/mindspore.mint.nn.Hardshrink.rst
    ↓ 解析 mindspore.mint.nn.__init__.py:
    from mindspore.nn.layer import HShrink as Hardshrink
    ↓ 再解析 mindspore.nn.layer.__init__.py 或 activation.py:
    class HShrink(Cell) 定义于 mindspore/python/mindspore/nn/layer/activation.py
实际英文Python代码: mindspore/python/mindspore/nn/layer/activation.py (class HShrink)

中文RST(Python): docs/api/api_python/mint/mindspore.mint.nn.functional.linear.rst
    ↓ 解析 mindspore.mint.nn.functional.py:
    from mindspore.ops.functional import dense as linear
    ↓ dense 是 auto_generate 算子,对应 YAML doc 以原名命名
实际英文YAML文档: mindspore/ops/op_def/yaml/doc/dense_doc.yaml

中文RST(Python): docs/api/api_python/mint/mindspore.mint.nn.functional.fold.rst
    ↓ 解析 mindspore.mint.nn.functional.py:
    from mindspore.ops.auto_generate import fold_ext as fold
    ↓ 按函数名 fold_ext 搜 doc 找不到,反查 op 定义文件:
    col2im_ext_op.yaml 中 name: fold_ext
实际英文YAML文档: mindspore/ops/op_def/yaml/doc/col2im_ext_doc.yaml

YAML文档类型说明:

类型路径用途
function_docmindspore/ops/op_def/yaml/doc/函数接口文档
method_docmindspore/ops/op_def/yaml/doc/方法接口文档
function_doc(alternative)mindspore/ops/api_def/function_doc/函数接口文档(备选)
method_doc(alternative)mindspore/ops/api_def/method_doc/方法接口文档(备选)

查找策略(优先级从高到低):

  1. 直接路径推断:根据模块路径映射到对应 Python/YAML 文件

  2. YAML 文档搜索:根据 API 名称搜索 op_def/yaml/doc/ 和 api_def/function_doc/ 等目录

  3. 别名导入链追踪:当直接推断和 YAML 搜索均未找到,或找到的内容不足以进行完整一致性检查时,必须追溯 import 别名链。具体做法:

    • 解析 RST 文件名提取完整模块路径(如 mindspore.mint.nn.Hardshrink)
    • 从最内层模块开始,读取其 __init__.py(如 mindspore/mint/nn/__init__.py)
    • 搜索 from X import Y as Z 或 from X import Z 语句,匹配别名 Z 是否等于 API 名称
    • 如果匹配,记录映射关系(别名 → 真实类/函数名),并继续解析真实导入路径的 __init__.py
    • 递归追踪直到找到实际的类/函数定义所在文件(class HShrink 或 def hardshrink)
    • 同时记录别名路径上的 YAML doc 文件(如 hardshrink_doc.yaml)用于辅助检查
  4. YAML op 定义文件反查:当第 3 步得到 auto_generate 函数名后,若按函数名搜 *_doc.yaml 找不到,搜索 op_def/yaml/*_op.yaml 中 name: 字段等于函数名的文件,其文件名前缀即为 doc 文件名:

    text
    例: col2im_ext_op.yaml 中 name: fold_ext
        → doc 文件为 col2im_ext_doc.yaml
  5. 用户指定路径

4. 生成并保存报告

汇总所有检查结果,按照"输出格式"生成 Markdown 报告保存到本地,在"基本信息"中列出本次使用的规则文件,告知用户路径。

  • 多输入或混合输入都合并到 report_{场景}.md(如 report_PR_1234.md),文件已存在则覆盖
  • 报告内按输入源分章节(如 ## PR #1234、## 本地文件: xxx.rst)
  • 默认保存到当前目录,或用户指定路径

输出格式

生成Markdown格式的检查报告。

报告结构
markdown
# 文档质量检查报告

## 基本信息

| 项目 | 内容 |
|------|------|
| 检查时间 | [时间戳] |
| 输入类型 | [HTML页面/PR链接/本地文件] |
| 输入来源 | [具体URL或路径] |
| 检查组合 | [通用+教程/通用+API] |
| 使用规则 | [本次使用的规则文件列表,如 `api_python_zh_rules.md`、`api_python_en_rules.md`] |

## 检查结果概览

| 问题等级 | 数量 |
|----------|------|
| 严重问题 | X |
| 一般问题 | X |
| 建议优化 | X |
| **总计** | **X** |

## 详细问题列表

- 以下仅给出输出格式参考,编号以规则文件中列举的实际编号为准。
- 本章节的问题分类不需要给出具体的规则文件来源。

### 通用性问题

| 编号 | 问题标题 | 优先级 | 位置 | 问题描述 | 建议修复 |
|------|----------|--------|------|----------|----------|
| G-L-01 | 单词拼写问题 | 严重 | 第X行 | XX单词拼写错误 | XX单词应改为XX |
| G-U-01 | 使用被动语态 | 一般 | 第X行 | XXX这句话使用了被动语态 | 建议将这句话改为XXX |

### 教程相关问题(如适用)

| 编号 | 问题标题 | 优先级 | 位置 | 问题描述 | 建议修复 |
|------|----------|--------|------|----------|----------|
| T-C-04 | 运行结果为 | 建议 | 第X行 | 运行结果中出现WARNING信息 | 建议删除运行结果中的WARNING信息 |

### API文档问题(如适用)

| 编号 | 问题标题 | 优先级 | 位置 | 问题描述 | 建议修复 |
|------|----------|--------|------|----------|----------|
| I-PY-R-01 | 缺失Summary描述 | 严重 | 第X行 | 函数/类缺少描述接口功能的Summary | 添加Summary描述 |
| I-PY-A-01 | 参数类型错误 | 严重 | 第X行 | 参数类型格式不正确 | 使用 `arg (int): description` 格式 |
| I-CPP-F-01 | 文件命名错误 | 严重 | 文件名 | class文件命名不符合规范 | 使用 `class{namespace}_{classname}.md` 格式 |
| I-YA-P-01 | 位置参数缺少类型 | 一般 | 第X行 | 缺少参数类型说明 | 使用 `input (Tensor): 描述` 格式 |
| I-PY-EF-02 | 示例缺少续行符 | 一般 | 第X行 | 多行代码缺少 `...` 续行符 | 类/函数定义后使用 `...` 续行 |
| I-YA-M-01 | 公式格式错误 | 建议 | 第X行 | 行公式未缩进 | 使用 `.. math::` + 缩进格式 |

### 一致性问题(如适用)

| 编号 | 问题类型 | 优先级 | 位置 | 中文文档 | 英文源码 | 建议修复 |
|------|----------|--------|------|----------|----------|----------|
| I-CN-D-03 | 参数名不一致 | 严重 | 中文RST第X行 / 英文源第Y行 | `**input_data**` | `input_data` | 确保参数名完全一致 |
| I-CN-A-03 | 参数类型不一致 | 严重 | 中文RST第X行 / 英文源第Y行 | `(Tensor)` | `(int)` | 确保参数类型一致 |
| I-CN-S-01 | 描述语义差异 | 一般 | 中文RST第X行 / 英文源第Y行 | 中文描述... | 英文描述... | 保持描述语义一致 |
| I-CN-T-02 | 返回值类型不一致 | 一般 | 中文RST第X行 / 英文源第Y行 | 返回:Tensor | Returns: int | 确保返回值类型一致 |
| I-PR-M-01 | 中文新增无英文对应 | 建议 | PR #123 | 新增 `xxx.rst` 文档 | 需在对应文件中添加注释 |

## 改进建议

1. [🔴高] 建议...
2. [🟠中] 建议...
3. [🟢低] 建议...

注意事项

  • 对于PR链接,会分析PR中的文档变更部分
  • 检查过程会尽量获取页面完整内容
  • 图片使用本地路径时无法验证,会标记为"需人工确认"
  • 中文RST不包含示例,不需要检查样例部分

© mindspore-ai, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 9 other files (scripts) in skills/doc-quality-checker-mindspore of mindspore-ai/docs.

  • SKILL.md
  • rules/api_cpp_en_rules.md
  • rules/api_cpp_zh_rules.md
  • rules/api_python_consistency_rules.md
  • rules/api_python_en_rules.md
  • rules/api_python_zh_rules.md
  • rules/doc_general_rules.md
  • rules/doc_tutorials_rules.md
  • scripts/fetch_pr.ps1
  • scripts/fetch_pr.sh

Open the folder on GitHubat commit b375e48

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Works with

Categories

Questions about Doc Quality Checker Mindspore

What does Doc Quality Checker Mindspore do?

检查文档质量的工具。当用户提到检查文档质量、审查文档、文档检查、文档审查、lint文档, 或者提供文档URL/PR链接/本地文件路径要求检查时触发。支持通用性检查、教程检查和API文档检查。. Doc Quality Checker Mindspore is an agent skill from mindspore-ai/docs.

When should I use Doc Quality Checker Mindspore?

Doc Quality Checker Mindspore fits situations like: development work in your project.

How do I install Doc Quality Checker Mindspore in Claude Code?

Run `npx skills add mindspore-ai/docs --skill doc-quality-checker-mindspore -a claude-code`. Or copy the skill folder (skills/doc-quality-checker-mindspore in mindspore-ai/docs) into .claude/skills/doc-quality-checker-mindspore in your project. Claude Code loads it when a task matches its description.

How do I install Doc Quality Checker Mindspore in Codex?

Run `npx skills add mindspore-ai/docs --skill doc-quality-checker-mindspore -a codex`. Or copy the skill folder (skills/doc-quality-checker-mindspore in mindspore-ai/docs) into .agents/skills/doc-quality-checker-mindspore in your project. Codex loads it when a task matches its description.

Can I use Doc Quality Checker Mindspore in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add mindspore-ai/docs --skill doc-quality-checker-mindspore -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/doc-quality-checker-mindspore, .gemini/skills/doc-quality-checker-mindspore, .github/skills/doc-quality-checker-mindspore and .opencode/skills/doc-quality-checker-mindspore in your project.

What does Doc Quality Checker Mindspore need to run?

Going by SKILL.md and its folder, Doc Quality Checker Mindspore needs PowerShell and a shell for the scripts in its folder and the command-line tools its instructions call (bash). Our summary lists: Python 3; A Bash shell; PowerShell.

Does Doc Quality Checker Mindspore access the network?

SKILL.md names 2 domains. In commands or code: api.atomgit.com; the agent is likely to contact it when it follows the instructions. As links in the text: atomgit.com. This is read from the text; nothing was executed.

Is Doc Quality Checker Mindspore safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Doc Quality Checker Mindspore use?

Doc Quality Checker Mindspore is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Doc Quality Checker Mindspore use?

About 2.1k tokens (SKILL.md is roughly 8.5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Doc Quality Checker Mindspore?

Skills that share tags, products or a category with Doc Quality Checker Mindspore: pybind11 Release Preparation (pybind/pybind11, 18k stars), Paddle Eager Graph (PaddlePaddle/Paddle, 24k stars), pybind11 Release Publication (pybind/pybind11, 18k stars) and ExecuTorch Build Guide (pytorch/executorch, 5.1k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Doc Quality Checker Mindspore?

mindspore-ai (a GitHub organization) maintains it in mindspore-ai/docs, which has 167 GitHub stars. The repository holds 3 skills in this directory. The repository was last updated on October 10, 2026.

Source: mindspore-ai/docs on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.