Does this require external tools?

The skill generates the logical PICT model and test cases. While it can use Python-based helpers internally, the generated model can also be used with Microsoft's PICT tool or various online generators for further refinement.

Can I use this for API testing?

Yes, the PICT Test Designer is ideal for API testing where you need to verify different combinations of headers, query parameters, and body payloads effectively.

Is this suitable for UI testing?

Absolutely. It is highly effective for generating test scenarios for complex UI forms and user workflows to ensure all input combinations and edge cases are thoroughly considered.

How does it handle invalid combinations?

The skill allows you to define constraints using PICT syntax to ensure that impossible or invalid combinations are either excluded or specifically targeted for negative testing scenarios.

What is PICT testing?

PICT (Pairwise Independent Combinatorial Testing) is a testing technique that tests all possible pairs of parameter values, providing high coverage with a fraction of the test cases required for exhaustive testing.

PICT Test Designer

Name: PICT Test Designer
Author: prof-ramos

byprof-ramos

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Seguridad y Pruebas

Generates comprehensive, pairwise test cases from requirements or code using the PICT methodology to ensure maximum coverage with minimum effort.

Acerca de

The PICT Test Designer is a specialized skill for Claude Code that automates the creation of systematic test suites using Pairwise Independent Combinatorial Testing (PICT). By analyzing input parameters, boundary values, and business constraints from your source code or documentation, it generates optimized test matrices that provide high coverage while significantly reducing the number of manual tests required. This skill is particularly effective for complex system configurations, API endpoints, and web forms where manual test case design is prone to missing edge cases or over-testing redundant combinations.

Características Principales

1 GitHub stars
Intelligent constraint handling for business rules and technical limitations
Pairwise (2-way) combinatorial test case generation for optimized coverage
Automatic equivalence partitioning and boundary value analysis
Standardized Markdown output including PICT models and test case tables
Automated parameter and value identification from code or requirements

Casos de Uso

Generating test matrices for complex API endpoints with multiple optional parameters
Testing cross-platform software configurations across various OS, hardware, and browser versions
Designing comprehensive validation tests for multi-field web forms and user registration flows

Acerca de

Características Principales

1 GitHub stars
Intelligent constraint handling for business rules and technical limitations
Pairwise (2-way) combinatorial test case generation for optimized coverage
Automatic equivalence partitioning and boundary value analysis
Standardized Markdown output including PICT models and test case tables
Automated parameter and value identification from code or requirements

Casos de Uso

Generating test matrices for complex API endpoints with multiple optional parameters
Testing cross-platform software configurations across various OS, hardware, and browser versions
Designing comprehensive validation tests for multi-field web forms and user registration flows