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testing-strategy

Standardize Python test authoring with dependency isolation and coverage enforcement.

Updated Jul 18, 2026
Search Tags:#testing#tdd#debugging#pytest#coverage#mocking
One-click install
npx skills add https://github.com/arthrod/conejo-skills --skill testing-strategy-arthrod
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: testing-strategy
Source: https://github.com/arthrod/conejo-skills/tree/main/skills/testing-strategy
Command: npx skills add https://github.com/arthrod/conejo-skills --skill testing-strategy-arthrod

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This skill addresses the common pitfalls of brittle, non-deterministic, and poorly covered test suites in Python projects by enforcing a strict hierarchy of dependency isolation and diagnostic procedures.

Core Features & Use Cases

  • Dependency Isolation: Provides a clear priority list for mocking, favoring pure functions and in-memory fakes over generic mocks to ensure test reliability.
  • Diagnostic Workflow: Offers a systematic approach to debugging failing tests, prioritizing the identification of test-side bugs before production code issues.
  • Coverage Enforcement: Mandates line and branch coverage reporting to ensure that test quality remains high and monotonically increasing.

Quick Start

Apply the testing-strategy to analyze the current test suite and identify areas where mocks can be replaced with deterministic fakes.

Frequently Asked Questions about testing-strategy

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I enforce deterministic pytest execution and eliminate flaky tests?▼

pytest branch coverage is enforced by mandating strict line and branch coverage reporting. This ensures test quality remains high and monotonically increasing across the entire codebase.

What is the best way to debug failing tests in a pytest project?▼

TDD red-green-refactor cycles fit into this strategy by providing a strict sequence: write a failing test, write minimal passing code, then refactor. This cycle is required for standardizing test authoring and maintaining high reliability.

Can I use this testing strategy for Python projects without pytest?▼

No, this testing strategy specifically standardizes test authoring for pytest-based Python projects. It requires adherence to red-green-refactor cycles and pytest-compatible coverage enforcement to function correctly.

Why does my test suite remain brittle when using standard mocking?▼

Your test suite remains brittle because standard mocking lacks strict dependency isolation. This strategy enforces a clear priority list favoring pure functions and deterministic fakes over generic mocks to ensure reliability.

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