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Agent Skill · 效率

casely(casely 文档)

casely 用于辅助前端页面、组件、样式和交互逻辑开发,适合在 OpenClaw 中需要维护前端项目、生成组件或检查界面实现时使用。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 是效率类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为需要联网;许可证为 MIT-0,第三方扫描有提醒项,按安装量在效率类的 21,003 个 Skill 里排第 6,390。

总安装 9,429周安装 389GitHub Stars 公开资料未说明下载 3,081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
MIT-0
版本
0.1.0

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
安装命令:
openclaw skills install 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 OpenClaw 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。

ClawHubOpenClaw
openclaw skills install casely

这个 Skill 是做什么的

效率类 Skill 把重复的整理、汇总、转换工作交给助手完成,减少手工复制粘贴。

casely 智能生成测试用例,依据项目文档自动推导边界条件与异常路径。支持单元测试、集成测试等多层级脚本编写辅助。显著减少 QA 人力投入,提升软件交付速度与可靠性保障。生成逻辑依赖输入文档完整性,模糊描述可能导致用例覆盖不全。casely 属于效率类 Skill,可作为该场景下的辅助能力补充。

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 OpenClaw;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 openclaw skills install 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 openclaw skills install 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 OpenClaw。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?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

casely 用的人多吗?

公开数据是累计安装 9,429 次,近一周安装 389 次,下载 3,081 次。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?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。

先想清楚的地方

  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 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 OpenClaw,想直接扩展它能力的人。
  • 需要处理效率类任务、又不想每次都手写提示词的人。

不太适合

  • 需要厂商级技术支持和 SLA 的生产系统。

效率类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
github

GitHub 代码协作 · OpenClaw

7,632,918545需要联网MIT-0通过
nano-pdf

Nano PDF 文档 · OpenClaw

4,249,476231需要联网MIT-0通过
auto-updater

自动更新程序 · OpenClaw

2,964,063386操作浏览器MIT-0通过
video-frames

视频帧 · OpenClaw

2,621,304112需要联网MIT-0通过
gemini

Gemini 助手 · OpenClaw

2,547,39648需要联网MIT-0有提醒
casely

casely 文档 · 本页

9,429—需要联网MIT-0有提醒

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

name
casely
description
>
license
MIT
metadata
author
John Wayne
version
1.5.0
category
QA Automation
repository
https://github.com/JohnWayneeee/casely-qa-skill

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.

  1. 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"
  1. 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/.
  1. 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).
  1. 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.
  1. 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):

TierCases/ModuleCoverageFocus
Smoke1-3MinGolden Path[web:13]
Critical (80%)N (fields*0.8)Key pathsHigh-risk (finance/auth)
FullAll perms100%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

OpenClaw

92.46%
按下载量换算2,849

安全审计

VirusTotal

通过

ClawScan

可疑

Static analysis

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。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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