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Agent Skill · 开发

pict-test-designer(pict 测试设计师)

用于辅助测试设计、自动化测试、用例整理和回归验证。它适合让 Agent 编写单元测试、端到端测试、测试计划或根据失败日志定位问题。使用时需要确认项目测试框架、运行命令和夹具数据,避免为了通过测试而改坏真实逻辑;涉及浏览器或外部服务时,应区分本地模拟、测试环境和生产环境。

pict-test-designer 是开发类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为操作浏览器;许可证为 MIT,第三方扫描有提醒项,按安装量在开发类的 21,746 个 Skill 里排第 16,891。

总安装 461周安装 19GitHub Stars 7下载 150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

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:pict-test-designer(pict 测试设计师)
来源仓库:https://github.com/jst-well-dan/skill-box
仓库路径:skills/pict-test-designer
安装命令:
npx skills add https://github.com/jst-well-dan/skill-box --skill pict-test-designer
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。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/jst-well-dan/skill-box --skill pict-test-designer

这个 Skill 是做什么的

开发类 Skill 面向写代码、改代码和排查问题,一般会读写仓库文件,部分还会执行构建或测试命令。

用于辅助测试设计、自动化测试和回归验证,适合编写测试用例或定位问题。适用于确认项目测试框架、运行命令和夹具数据,避免为了通过测试而改坏逻辑。涉及浏览器或外部服务时,应区分本地模拟、测试环境和生产环境。安装方式:github;建议确认权限范围和维护状态。

pict-test-designer 属于开发类 Skill,可作为该场景下的辅助能力补充。

来源仓库内路径:skills/pict-test-designer

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/jst-well-dan/skill-box --skill pict-test-desig…;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

    它需要操作浏览器,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

pict-test-designer 怎么安装?

在终端执行 npx skills add https://github.com/jst-well-dan/skill-box --skill pict-test-designer。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

pict-test-designer 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。

pict-test-designer 需要什么权限?

权限级别是操作浏览器。该 Skill 可能涉及浏览器控制能力,使用时可能读取或操作网页内容,需要在受控环境中确认权限边界。

pict-test-designer 是免费的吗?

来源标注的许可证是 MIT。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。

pict-test-designer 用的人多吗?

公开数据是累计安装 461 次,近一周安装 19 次,下载 150 次,GitHub 7 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。

pict-test-designer 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

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 3 个目录同时收录,说明这个 Skill 流传比较广。
  • 许可证明确为 MIT,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 Codex,想直接扩展它能力的人。
  • 需要处理开发类任务、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。

开发类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
skill-vetter

技能审查员 · OpenClaw

8,779,018986操作浏览器MIT-0通过
ontology

本体论 · OpenClaw

6,636,960576只读MIT-0通过
entra-app-registration

内部应用程序注册 · Codex、Claude

6,631,872764接触敏感数据未明确通过
entra-app-registration

内部应用程序注册 · Codex、Claude

3,757,680201接触敏感数据未明确通过
nano-banana-pro

纳米香蕉专业版 · OpenClaw

3,526,898364接触敏感数据MIT-0有提醒
pict-test-designer

pict 测试设计师 · 本页

4617操作浏览器MIT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

PICT Test Designer

This skill enables systematic test case design using PICT (Pairwise Independent Combinatorial Testing). Given requirements or code, it analyzes the system to identify test parameters, generates a PICT model with appropriate constraints, executes the model to generate pairwise test cases, and formats the results with expected outputs.

When to Use This Skill

Use this skill when:

  • Designing test cases for a feature, function, or system with multiple input parameters
  • Creating test suites for configurations with many combinations
  • Needing comprehensive coverage with minimal test cases
  • Analyzing requirements to identify test scenarios
  • Working with code that has multiple conditional paths
  • Building test matrices for API endpoints, web forms, or system configurations

Workflow

Follow this process for test design:

1. Analyze Requirements or Code

From the user's requirements or code, identify:

  • Parameters: Input variables, configuration options, environmental factors
  • Values: Possible values for each parameter (using equivalence partitioning)
  • Constraints: Business rules, technical limitations, dependencies between parameters
  • Expected Outcomes: What should happen for different combinations

Example Analysis:

For a login function with requirements:

  • Users can login with username/password
  • Supports 2FA (on/off)
  • Remembers login on trusted devices
  • Rate limits after 3 failed attempts

Identified parameters:

  • Credentials: Valid, Invalid
  • TwoFactorAuth: Enabled, Disabled
  • RememberMe: Checked, Unchecked
  • PreviousFailures: 0, 1, 2, 3, 4

2. Generate PICT Model

Create a PICT model with:

  • Clear parameter names
  • Well-defined value sets (using equivalence partitioning and boundary values)
  • Constraints for invalid combinations
  • Comments explaining business rules

Model Structure:

# Parameter definitions
ParameterName: Value1, Value2, Value3

# Constraints (if any)
IF [Parameter1] = "Value" THEN [Parameter2] <> "OtherValue";

Refer to docs/pict_syntax.md for:

  • Complete syntax reference
  • Constraint grammar and operators
  • Advanced features (sub-models, aliasing, negative testing)
  • Command-line options
  • Detailed constraint patterns

Refer to docs/examples.md for:

  • Complete real-world examples by domain
  • Software function testing examples
  • Web application, API, and mobile testing examples
  • Database and configuration testing patterns
  • Common patterns for authentication, resource access, error handling

3. Execute PICT Model

Generate the PICT model text and format it for the user. You can use Python code directly to work with the model:

# Define parameters and constraints
parameters = {
    "OS": ["Windows", "Linux", "MacOS"],
    "Browser": ["Chrome", "Firefox", "Safari"],
    "Memory": ["4GB", "8GB", "16GB"]
}

constraints = [
    'IF [OS] = "MacOS" THEN [Browser] IN {Safari, Chrome}',
    'IF [Memory] = "4GB" THEN [OS] <> "MacOS"'
]

# Generate model text
model_lines = []
for param_name, values in parameters.items():
    values_str = ", ".join(values)
    model_lines.append(f"{param_name}: {values_str}")

if constraints:
    model_lines.append("")
    for constraint in constraints:
        if not constraint.endswith(';'):
            constraint += ';'
        model_lines.append(constraint)

model_text = "\n".join(model_lines)
print(model_text)

Using the helper script (optional): The scripts/pict_helper.py script provides utilities for model generation and output formatting:

# Generate model from JSON config
python scripts/pict_helper.py generate config.json

# Format PICT tool output as markdown table
python scripts/pict_helper.py format output.txt

# Parse PICT output to JSON
python scripts/pict_helper.py parse output.txt

To generate actual test cases, the user can:

  1. Save the PICT model to a file (e.g., model.txt)
  2. Use online PICT tools like:

- https://pairwise.yuuniworks.com/ - https://pairwise.teremokgames.com/

  1. Or install PICT locally (see docs/pict_syntax.md)

4. Determine Expected Outputs

For each generated test case, determine the expected outcome based on:

  • Business requirements
  • Code logic
  • Valid/invalid combinations

Create a list of expected outputs corresponding to each test case.

5. Format Complete Test Suite

Provide the user with:

  1. PICT Model - The complete model with parameters and constraints
  2. Markdown Table - Test cases in table format with test numbers
  3. Expected Outputs - Expected result for each test case

Output Format

Present results in this structure:

## PICT Model

Parameters

Parameter1: Value1, Value2, Value3 Parameter2: ValueA, ValueB

Constraints

IF [Parameter1] = "Value1" THEN [Parameter2] = "ValueA";


## Generated Test Cases

| Test # | Parameter1 | Parameter2 | Expected Output |
| --- | --- | --- | --- |
| 1 | Value1 | ValueA | Success |
| 2 | Value2 | ValueB | Success |
| 3 | Value1 | ValueB | Error: Invalid combination |
...

## Test Case Summary

- Total test cases: N
- Coverage: Pairwise (all 2-way combinations)
- Constraints applied: N

Best Practices

Parameter Identification

Good:

  • Use descriptive names: AuthMethod, UserRole, PaymentType
  • Apply equivalence partitioning: FileSize: Small, Medium, Large instead of FileSize: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10
  • Include boundary values: Age: 0, 17, 18, 65, 66
  • Add negative values for error testing: Amount: ~-1, 0, 100, ~999999

Avoid:

  • Generic names: Param1, Value1, V1
  • Too many values without partitioning
  • Missing edge cases

Constraint Writing

Good:

  • Document rationale: # Safari only available on MacOS
  • Start simple, add incrementally
  • Test constraints work as expected

Avoid:

  • Over-constraining (eliminates too many valid combinations)
  • Under-constraining (generates invalid test cases)
  • Complex nested logic without clear documentation

Expected Output Definition

Be specific:

  • "Login succeeds, user redirected to dashboard"
  • "HTTP 400: Invalid credentials error"
  • "2FA prompt displayed"

Not vague:

  • "Works"
  • "Error"
  • "Success"

Scalability

For large parameter sets:

  • Use sub-models to group related parameters with different orders
  • Consider separate test suites for unrelated features
  • Start with order 2 (pairwise), increase for critical combinations
  • Typical pairwise testing reduces test cases by 80-90% vs exhaustive

Common Patterns

Web Form Testing

parameters = {
    "Name": ["Valid", "Empty", "TooLong"],
    "Email": ["Valid", "Invalid", "Empty"],
    "Password": ["Strong", "Weak", "Empty"],
    "Terms": ["Accepted", "NotAccepted"]
}

constraints = [
    'IF [Terms] = "NotAccepted" THEN [Name] = "Valid"',  # Test validation even if terms not accepted
]

API Endpoint Testing

parameters = {
    "HTTPMethod": ["GET", "POST", "PUT", "DELETE"],
    "Authentication": ["Valid", "Invalid", "Missing"],
    "ContentType": ["JSON", "XML", "FormData"],
    "PayloadSize": ["Empty", "Small", "Large"]
}

constraints = [
    'IF [HTTPMethod] = "GET" THEN [PayloadSize] = "Empty"',
    'IF [Authentication] = "Missing" THEN [HTTPMethod] IN {GET, POST}'
]

Configuration Testing

parameters = {
    "Environment": ["Dev", "Staging", "Production"],
    "CacheEnabled": ["True", "False"],
    "LogLevel": ["Debug", "Info", "Error"],
    "Database": ["SQLite", "PostgreSQL", "MySQL"]
}

constraints = [
    'IF [Environment] = "Production" THEN [LogLevel] <> "Debug"',
    'IF [Database] = "SQLite" THEN [Environment] = "Dev"'
]

Troubleshooting

No Test Cases Generated

  • Check constraints aren't over-restrictive
  • Verify constraint syntax (must end with ;)
  • Ensure parameter names in constraints match definitions (use [ParameterName])

Too Many Test Cases

  • Verify using order 2 (pairwise) not higher order
  • Consider breaking into sub-models
  • Check if parameters can be separated into independent test suites

Invalid Combinations in Output

  • Add missing constraints
  • Verify constraint logic is correct
  • Check if you need to use NOT or <> operators

Script Errors

  • Ensure pypict is installed: pip install pypict --break-system-packages
  • Check Python version (3.7+)
  • Verify model syntax is valid

References

  • docs/pict_syntax.md - Complete PICT syntax reference with grammar and operators
  • docs/examples.md - Comprehensive real-world examples across different domains
  • scripts/pict_helper.py - Python utilities for model generation and output formatting
  • PICT GitHub Repository - Official PICT documentation
  • pypict Documentation - Python binding documentation
  • Online PICT Tools - Web-based PICT generator

Examples

Example 1: Simple Function Testing

User Request: "Design tests for a divide function that takes two numbers and returns the result."

Analysis:

  • Parameters: dividend (number), divisor (number)
  • Values: Using equivalence partitioning and boundaries

- Numbers: negative, zero, positive, large values

  • Constraints: Division by zero is invalid
  • Expected outputs: Result or error

PICT Model:

Dividend: -10, 0, 10, 1000
Divisor: ~0, -5, 1, 5, 100

IF [Divisor] = "0" THEN [Dividend] = "10";

Test Cases:

Test #DividendDivisorExpected Output
1100Error: Division by zero
2-101-10.0
30-50.0
410005200.0
5101000.1

Example 2: E-commerce Checkout

User Request: "Design tests for checkout flow with payment methods, shipping options, and user types."

Analysis:

  • Payment: Credit Card, PayPal, Bank Transfer (limited by user type)
  • Shipping: Standard, Express, Overnight
  • User: Guest, Registered, Premium
  • Constraints: Guests can't use Bank Transfer, Premium users get free Express

PICT Model:

PaymentMethod: CreditCard, PayPal, BankTransfer
ShippingMethod: Standard, Express, Overnight
UserType: Guest, Registered, Premium

IF [UserType] = "Guest" THEN [PaymentMethod] <> "BankTransfer";
IF [UserType] = "Premium" AND [ShippingMethod] = "Express" THEN [PaymentMethod] IN {CreditCard, PayPal};

Output: 12-15 test cases covering all valid payment/shipping/user combinations with expected costs and outcomes.

平台分布

Claude Code

27.6%
按下载量换算41

OpenCode

24.93%
按下载量换算37

Antigravity

18.45%
按下载量换算28

Codex

13.35%
按下载量换算20

windsurf

7.84%
按下载量换算12

Gemini CLI

3.52%
按下载量换算5

安全审计

Gen Agent Trust Hub

可疑

Socket

通过

Snyk

通过

权限和风险

操作浏览器

该 Skill 可能涉及浏览器控制能力,使用时可能读取或操作网页内容,需要在受控环境中确认权限边界。

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。

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