---
name: AI-Generated Test Review
slug: ai-generated-test-review
category: Quality
description: AI-Generated Test Review checks AI-written unit, functional, API, or end-to-end tests for false confidence, weak assertions, missing risks, and unsafe behavior. Use it when reviewing tests that pass but may not prove real behavior.
github: "https://github.com/naodeng/awesome-qa-skills/tree/main/skills/en/testing-types/ai-generated-test-review"
language: Python
stars: 193
forks: 27
install: "npx degit https://github.com/naodeng/awesome-qa-skills/tree/main/skills/en/testing-types/ai-generated-test-review ~/.claude/skills/ai-generated-test-review"
installs_to: ~/.claude/skills/ai-generated-test-review
source_path: skills/en/testing-types/ai-generated-test-review/SKILL.md
collection_size: 25
category_size: 1662
collection_url: "https://dirskills.com/collections/naodeng/awesome-qa-skills"
added: 2026-09-05T05:31:35.750Z
last_synced: 2026-09-05T05:31:35.750Z
canonical_url: "https://dirskills.com/skills/ai-generated-test-review"
---

# AI-Generated Test Review

AI-Generated Test Review checks AI-written unit, functional, API, or end-to-end tests for false confidence, weak assertions, missing risks, and unsafe behavior. Use it when reviewing tests that pass but may not prove real behavior.

**Install:**

```bash
npx degit https://github.com/naodeng/awesome-qa-skills/tree/main/skills/en/testing-types/ai-generated-test-review ~/.claude/skills/ai-generated-test-review
```

## README

# AI-Generated Test Review

Determine whether AI-generated tests prove real behavior instead of merely raising coverage or producing green builds.

## When to Use

- Reviewing, changing, or accepting AI-generated unit, functional, API, or E2E tests.
- A test passes but its assertions, isolation, data, or coverage value is doubtful.

## Workflow

1. Inventory test types and files actually present in scope: unit, functional test, API, E2E, or a combination. Review only discovered or user-specified types.
2. Establish the behavior, risk, and available requirement or implementation evidence. Mark missing evidence; do not invent findings.
3. Read `prompts/review-test.md` and report actionable findings by severity, tied to a test, risk, and repair direction.
4. Load rules only for discovered layers: unit, functional test, API, or E2E. Load more than one only for cross-layer concerns; do not perform a global review just because multiple rule files exist.
5. Separate merge-blocking faults from improvements; do not call an effective test defective because of style preference.

## Core Constraints

- Judge observable behavior, failure signals, and risk coverage—not line coverage, test names, or mock-call counts alone.
- When a broken implementation could still pass, state the smallest break and the assertion needed to catch it.
- Never recommend deleting assertions, swallowing errors, loosening timeouts, or changing production behavior merely to obtain green tests.
- Do not expose credentials, personal data, or production write operations in tests, logs, or examples.

## Progressive Disclosure

- Always read `prompts/review-test.md` before reviewing; its test-type routing precedes per-test review.
- For fake tests, excessive mocks, or ineffective assertions, read `references/fake-test-patterns.md`.
- Read `references/unit-test-rules.md`, `references/functional-test-rules.md`, `references/api-test-rules.md`, or `references/e2e-test-rules.md` for the applicable layer.
- Read related material under `examples/good/` or `examples/bad/` when an example is useful.

## Pre-delivery Checklist

- [ ] States scope, evidence, and open questions
- [ ] Ranks findings and includes location, impact, and repair direction
- [ ] Reviews real assertions, negative paths, boundaries, isolation, and repeatability
- [ ] Does not present speculation as fact