reviewing-python-tests

Review Python test suites for evidentiary support and ADR compliance.

1|Updated Jan 6, 2026
One-click install
npx skills add https://github.com/simonheimlicher/spx-claude --skill reviewing-python-tests
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: reviewing-python-tests
Source: https://github.com/simonheimlicher/spx-claude/tree/main/plugins/python/skills/reviewing-python-tests
Command: npx skills add https://github.com/simonheimlicher/spx-claude --skill reviewing-python-tests

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps auditors determine whether Python tests provide genuine evidence that outcomes are fulfilled, preventing silent passes that misrepresent capabilities.

Core Features & Use Cases

  • Validate evidentiary integrity across unit and integration tests.
  • Detect adversarial or non-representative tests and enforce protocol compliance.
  • Use case: when reviewing a capabilities/story, verify that tests demonstrate actual fulfillment rather than surface-level checks.

Quick Start

  • Run the review protocol against a Python test suite located in tests/ to identify gaps between tests and outcomes.
  • Cite any deviations from the six-phase process and reference /testing and /standardizing-python-testing for remediation.

Frequently Asked Questions about reviewing-python-tests

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

FAQPage Schema
How do I verify my Python tests provide genuine evidentiary support for specified outcomes?

Verifying Python tests for genuine evidentiary support requires an adversarial review protocol that inspects spec structure, evidentiary integrity, and ADR compliance to prevent silent passes that misrepresent capabilities.

What is an adversarial review protocol for pytest test suites?

An adversarial review protocol for pytest test suites is a six-phase process that validates evidentiary integrity across unit and integration tests, detecting non-representative tests that fail to demonstrate actual outcome fulfillment.

How to detect silent passes in Python unit and integration tests?

Detecting silent passes in Python tests involves running an adversarial review protocol against the suite to identify gaps between surface-level checks and actual capabilities, ensuring tests prove real evidence.

Does reviewing Python tests work with property-based testing and ADR compliance checks?

Reviewing Python tests enforces ADR compliance and evaluates property-based testing by validating spec structure and evidentiary integrity, ensuring the test suite demonstrates actual fulfillment rather than superficial checks.

What is the best way to audit a Python test suite for outcome fulfillment?

The best way to audit a Python test suite for outcome fulfillment is executing a six-phase review protocol that cites deviations from evidentiary standards and references /testing and /standardizing-python-testing for remediation.

What are the limitations of surface-level Python test checks during code review?

Surface-level Python test checks often cause silent passes that misrepresent capabilities, failing to provide genuine evidentiary support for specified outcomes and masking gaps between tests and actual story fulfillment.