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Agent Skill · 待分类

casely(casely 文档)

casely 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

casely 是待分类类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为需要联网;许可证信息还不明确,第三方扫描已通过,按安装量在待分类类的 11,209 个 Skill 里排第 7,545。

总安装 356周安装 15GitHub Stars 2下载 125核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:casely(casely 文档)
来源仓库:https://github.com/johnwayneeee/casely-qa-skill
仓库路径:skills/casely
安装命令:
npx skills add https://github.com/johnwayneeee/casely-qa-skill --skill casely
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/johnwayneeee/casely-qa-skill --skill casely

这个 Skill 是做什么的

这个 Skill 的分类还没最终确定,用法以来源仓库的说明为准。

casely 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合整理项目状态和变更事项。适用于围绕仓库状态、代码变更或协作事项进行信息整理。通过 npx skills add 命令从指定仓库安装并使用。安装前需确认权限范围和维护状态,注意可能触发联网或文件操作。

casely 属于待分类类 Skill,可作为该场景下的辅助能力补充。

来源仓库内路径:skills/casely

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/johnwayneeee/casely-qa-skill --skill casely;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要需要联网,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

casely 怎么安装?

在终端执行 npx skills add https://github.com/johnwayneeee/casely-qa-skill --skill casely。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

casely 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

casely 需要什么权限?

权限级别是需要联网。该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

casely 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

casely 用的人多吗?

公开数据是累计安装 356 次,近一周安装 15 次,下载 125 次,GitHub 2 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

casely 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理待分类类任务、又不想每次都手写提示词的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

待分类类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
infsh-cli

infsh CLI · Codex、Claude

4,374,576382权限未标注未明确未收录
ai-video-generation

AI 视频生成 · Codex、Claude

4,313,376382调用外部服务未明确有提醒
lark-openapi-explorer

云雀 OpenAPI 浏览器 · Codex、Claude

2,064,1209,011需要联网未明确有提醒
lark-workflow-standup-report

Lark 工作流程站立报告 · Codex、Claude

2,041,6328,978需要联网未明确通过
use-my-browser

使用我的浏览器 · Codex、Claude

1,848,49140操作浏览器未明确未通过
casely

casely 文档 · 本页

3562需要联网未明确通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

Casely — QA Test Case Generator

Casely automates the most time-consuming part of a QA engineer's job: writing test cases. It reads requirement documents and learns from your team's existing test case examples to produce structured, style-consistent test suites ready for import into any Test Management System.

Why this matters

Manual test case writing accounts for ~40% of a QA engineer's time. Requirements come in fragmented formats (PDF, DOCX, XLSX). Every team has its own column structure, naming conventions, and writing style. Casely solves this by:

  • Converting any document format to clean Markdown via docling.
  • Extracting formal style rules from your team's example test cases.
  • Generating test cases that match your team's exact structure and tone.
  • Exporting to Excel with correct column mapping for TMS import.

Commands

/init [ProjectName]

Creates a new isolated project workspace and verifies the environment.

/parse

Runs the CaselyParser to convert all raw assets (requirements and examples) to Markdown.

/style

Analyzes example test cases and generates a persistent test_style_guide.md.

/plan

Scans parsed requirements and suggests a testing plan with modules and test types.

/generate [type]

Generates atomic test cases of the specified type (functional, negative, integration, boundary, etc.).

/export

Converts generated Markdown test cases into a formatted .xlsx file.


Full Workflow

Phase 1: Project Initialization & Environment Setup (/init)

When the user runs /init [ProjectName] (or asks to start a new testing project):

  1. Create Directories: Create the project directory structure under projects/ in the repository root:

- input/requirements/ - input/examples/ - processed/requirements/ - processed/examples/ - results/ - exports/

  1. Environment Setup via uv:

- Location: Dependencies are defined in pyproject.toml at the repository root (not inside the skill folder). Scripts expect uv sync to have been run from that root. - Check if pyproject.toml exists at the repo root. If not, run uv init there. - Install/verify dependencies: uv add docling openpyxl (or uv sync from repo root). - This ensures a lightning-fast setup and handles all sub-dependencies (e.g. torch for docling) automatically.

  1. Confirm to the user:

- "Project {project_name} initialized via UV. Environment and dependencies (docling, openpyxl) are ready." - "Place your requirement documents into projects/{project_name}/input/requirements/ and examples into projects/{project_name}/input/examples/."

Phase 2: Document Parsing (/parse)

When the user runs /parse (or asks to parse/process documents):

  1. Locate the project. If there's only one project under projects/, use it automatically. If multiple exist, ask the user which one.
  2. Run CaselyParser — The parser is located at scripts/casely_parser.py within this skill. It uses docling and supports all major formats. Via CLI (optional arguments, auto-detects latest project if omitted): uv run python <skill-path>/scripts/casely_parser.py *(Or manual path if needed)* uv run python <skill-path>/scripts/casely_parser.py "projects/{name}/input/requirements" "projects/{name}/processed/requirements"
  3. Report results to the user: how many files were parsed, any errors, and summary of processed files.

Phase 3: Style Guide Creation (/style)

  1. Read all parsed example files from processed/examples/.
  2. Analyze the table structure to extract headers, data types, and mandatory fields.

- CRITICAL: The style guide MUST be an exact replica of the example's column structure. - MANDATORY: Transfer ALL headers from the example files to the test_style_guide.md in their exact order. Do not rename, omit (e.g., "Comments", "Author"), or add new columns unless explicitly requested.

  1. Analyze the writing style to extract language, tone, and formatting patterns (e.g., how steps are phrased).
  2. Generate test_style_guide.md in the project root. This file acts as the "source of truth" and must explicitly define the horizontal table row structure.
  3. Present the style guide to the user for review. Any manual adjustments to this file will be respected by the generator.

Phase 4: Professional Test Design & Planning (/plan)

  1. Load Context & Analysis:

- Read parsed requirements from processed/requirements/. - Load test_style_guide.md to match example structure (columns → test complexity).

  1. Structural Breakdown:

- Extract modules/endpoints/logic blocks from requirements. - Categorize by Level: API (fields/status), Integration (flows), E2E (scenarios).[web:8]

  1. Smart Estimation (Style-Driven):

- Metrics from Style Guide: Fields per test (from columns), branches from logic. - Coverage Tiers (total cases based on examples): Tier Cases/Module Coverage Focus Smoke 1-3 Min Golden Path[web:13] Critical (80%) N (fields*0.8) Key paths High-risk (finance/auth) Full All perms 100% Edges/negatives - Risk Scoring: High (security), Med (logic), Low (UI).[web:8]

  1. Traceability & Prep:

- Quick RTM Preview: Req ID → Planned Cases (e.g., "REQ-001 → 5 cases"). - Data/Deps: Test data rules (valid/edge), mocks needed.

  1. Output Plan:

- Table by Module: *Module | Level | Est. Cases (80%) | Type | Tools*. - MANDATORY: Provide ready-to-copy commands for each module. - Save test_plan.md (importable to TMS). - Ask: *"Generate Critical Path? /generate functional MODULE_NAME"* or *"/generate negative MODULE_NAME"*.

Next: "/generate [type] will create exactly the estimated number of files, with each file containing one atomic test case matching your style guide."

Phase 5: Test Case Generation (/generate [type])

  1. Load context:

- BIDING: Read test_style_guide.md (Mandatory Source of Truth). - Read relevant parsed requirement files. - Target specific module and test type.

  1. Generate ATOMIC test cases:

- One File = One Test Case (1 ID = 1 Scenario): Each test case MUST be saved as a separate Markdown file in results/. - Horizontal Structure: Each file MUST contain exactly ONE horizontal table row (header row + data row). Do NOT use vertical "key-value" lists. - Naming Convention: {type}_{id}_{short_description}.md. - Match the style guide exactly — same columns (1:1 with example), same tone, same structure. - No Hallucinations — only use columns and data points supported by the guide and requirements.

  1. Proactive Report:

- Notify the user of created files. - Mandatory Next Step: Always advise the user on what else they can generate. Example: *"I've generated functional cases. You can now run /generate negative to check error handling or /generate security for device metadata."*

Phase 6: Export to Excel (/export)

  1. Convert Markdown files to Excel using scripts/export_to_xlsx.py.

- Smart Execution: The script automatically detects the most recently modified project in the projects/ directory if no paths are provided.

  1. Atomic One-to-One Export: For every .md file in results/, the tool creates exactly one corresponding .xlsx file in exports/.

- Behavior: Direct format conversion preserving the file count. - Naming: Files are named identically to their source: {type}_{id}_{short_description}.xlsx.

  1. Internal Structure: Each Excel file contains a single sheet called "Test Case" with the columns exactly matching the project's style guide.
  2. Plain Text Export: Content is exported as plain text with support for multi-line cells (using <br>).
  3. Save to exports/.

Important Guidelines

Proactive Guidance (Crucial)

After every command, Casely MUST provide a "Next Step" block.

  • After /init -> suggest /parse.
  • After /parse -> suggest /style.
  • After /style -> suggest /plan.
  • After /plan -> list specific commands like /generate functional or /generate negative.
  • After /generate -> suggest /export OR other generation types.

Language Awareness

Casely is language-agnostic for data. It will detect the language of the provided examples (e.g., Russian) and generate test cases in that same language. The internal logic and style guide should bridge this gap.

Atomic over Composite

Validators should always prefer multiple specialized test cases over one "all-in-one" case. This ensures clearer test results and easier bug localization.

Style Guide is King

The style guide is the single source of truth. Do not invent new columns or change formatting unless the style guide is updated first.


Skill Files

Scripts (scripts/)

  • scripts/casely_parser.py — Document-to-Markdown converter (Docling).
  • scripts/export_to_xlsx.py — Markdown-to-Excel exporter.

References (references/)

  • references/parser_usage.md — Technical details on calling the parser.
  • references/export_guide.md — Details on the MD-to-Excel conversion logic.
  • references/style_analysis_prompts.md — Methodologies for style extraction.

平台分布

Codex

34.61%
按下载量换算43

Claude

31.09%
按下载量换算39

Cursor

19.24%
按下载量换算24

Gemini CLI

10.28%
按下载量换算13

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

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