dirty-equals

name

dirty-equals

description

Use this skill when you need to write declarative, readable, and maintainable assertions in Python tests. It is particularly effective for matching complex data structures, validating merged TOML configurations, and performing fuzzy matching on URLs, file paths, or object types. Use this when the user asks to "assert," "check," "verify," or "match" data in a test, even if they don't explicitly mention "dirty-equals."

dirty-equals Skill

This skill provides patterns and best practices for writing declarative assertions in the ruff-sync project using the dirty-equals library.

Overview

Instead of asserting on every field manually, compare against a "dirty" object that matches the expected structure and types.

from dirty_equals import IsInt, IsPartialDict, IsStr

def test_config_logic():
    result = {"status": "active", "version": 1, "extra": "data"}
    # Declarative assertion
    assert result == IsPartialDict({
        "status": IsStr(regex="act.*"),
        "version": IsInt(gt=0),
    })

Detailed reference

Check these references for project-specific usage and common matchers:

• Common Matchers: Standard dirty-equals matchers like IsPartialDict, IsInstance, and more.
• Specialized Matching: Handling tomlkit.unwrap() and Arguments._asdict().

Best Practices

• Import Style: Always use the from dirty_equals import ... style at the module level of your test files.
• Semantic Matching: Use dirty-equals for the semantic part of your test assertions, while using string comparisons or respx for structural/whitespace checks where appropriate.
• Type Safety: Prefer IsInstance(httpx.URL) or IsInstance(pathlib.Path) over custom regex for well-known types in the project.