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testing-standards

Enforce deterministic, high-coverage Python tests for GUTTERS modules and integrations.

Updated Aug 27, 2026
Search Tags:#testing#integration#pytest#coverage#fixtures#deterministic#ci
One-click install
npx skills add https://github.com/drhayf/GUTTERS --skill testing-standards-drhayf
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: testing-standards
Source: https://github.com/drhayf/GUTTERS/tree/main/.agent/skills/testing-standards
Command: npx skills add https://github.com/drhayf/GUTTERS --skill testing-standards-drhayf

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

GUTTERS testing standards ensure consistent, high-quality tests to validate modules, calculations, and integrations, preventing regressions and ensuring reliable behavior across the platform.

Core Features & Use Cases

  • Establish deterministic fixtures and seeds for reproducible results.
  • Enforce minimum coverage (80%+) and validation data for all tests.
  • Provide a structured guideline for test organization, integration checks, and CI readiness.

Quick Start

Run the deterministic testing workflow to set up fixtures and execute the test suite.

Frequently Asked Questions about testing-standards

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

FAQPage Schema
How do I enforce deterministic testing with pytest and seed-driven fixtures?▼

Deterministic testing with pytest uses seed-driven fixtures to establish reproducible data patterns, ensuring consistent validation results and reliable behavior across Python modules without relying on random data generation.

What is the minimum coverage requirement for Python integration tests?▼

The minimum coverage requirement for integration and unit tests is 80% or higher, enforced through strict validation rules to prevent regressions and ensure reliable behavior across the platform.

Can I use mocks in core logic when testing PostgreSQL and Redis integrations?▼

No, mocks are not allowed in core logic. Testing standards require explicit Redis and PostgreSQL usage with real data patterns to validate actual module integrations and end-to-end behavior accurately.

How do I set up CI-ready test suites for Python modules?▼

Setting up CI-ready test suites involves organizing tests into structured guidelines, applying deterministic seeding for fixtures, and enforcing strict validation rules to satisfy integration checks and coverage thresholds before execution.

What is the best way to validate end-to-end tests without mocks in Python?▼

The best way to validate end-to-end tests without mocks is to use real data patterns, explicit database and cache integrations, and seed-driven fixtures to enforce strict validation rules and deterministic results.

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