openharmonyinsight/openharmony-skills

ai-generated-ut-code-review

Use when reviewing or scoring AI-generated unit tests/UT code, especially when coverage, assertion effectiveness, or test quality is in question and a numeric score, risk level, or must-fix checklist is needed

First seen Feb 7, 2026

Installation

$ npx skills add openharmonyinsight/openharmony-skills --skill ai-generated-ut-code-review

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More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

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Repository health

Stars 33
Default branch main
Open issues 2
Status Active

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 3,464 B
  • docs SUMMARY.md 244 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 77 installs

SKILL.md

AI UT Code Review

Overview

Review AI-generated unit tests for effectiveness, coverage, assertions, negative cases, determinism, and maintainability. Output a 0-10 score, a risk level, and a must-fix checklist. Overall line coverage must be >= 80%; otherwise risk is at least High.

When to Use

  • AI-generated UT/test code review or quality evaluation
  • Need scoring, risk level, or must-fix checklist
  • Questions about coverage or assertion validity

Workflow

  1. Confirm tests target the intended business code and key paths.
  2. Check overall line coverage (>= 80% required).
  3. Inspect assertions for behavioral validity; flag missing/ineffective assertions.
  4. Verify negative/edge cases and determinism (no env/time dependency).
  5. Score by rubric, assign risk, list must-fix items with evidence.

Scoring (0-10)

Each dimension 0-2 points. Sum = total score.

Dimension 0 1 2
Coverage < 80% 80%+ but shallow 80%+ and meaningful
Assertion Quality No/invalid assertions Some weak assertions Behavior-anchored assertions
Negative & Edge Missing Partial Comprehensive
Data & Isolation Flaky/env-dependent Mixed Deterministic, isolated
Maintainability Hard to read/modify Mixed quality Clear structure & naming

Risk Levels

  • Blocker: Coverage < 80% AND key paths untested, or tests have no meaningful assertions
  • High: Coverage < 80% OR assertions largely ineffective
  • Medium: Coverage OK but weak edge cases or fragile design
  • Low: Minor improvements

Must-Fix Checklist

  • Overall line coverage >= 80%
  • Each test has at least one behavior-relevant assertion
  • Negative/exception cases exist for core logic
  • Tests are deterministic and repeatable

AI-Generated Test Pitfalls (Check Explicitly)

  • No assertions or assertions unrelated to behavior (e.g., only not-null)
  • Over-mocking hides real behavior
  • Only happy-path coverage
  • Tests depend on time/network/env
  • Missing verification of side effects

Output Format (Required, Semi-fixed)

  • Score: x/10 — Coverage x, Assertion Quality x, Negative & Edge x, Data & Isolation x, Maintainability x
  • Risk: Low/Medium/High/Blocker — 简述原因(1 行)
  • Must-fix:

- [动作 + 证据] - [动作 + 证据]

  • Key Evidence:

- 引用具体测试用例名或覆盖率报告摘要(1-2 条)

  • Notes:

- 最小修复建议或替代方案(1-2 行)

Rules:

  • 覆盖率 < 80% 风险至少 High,并必须列入 Must-fix
  • 无断言/无效断言直接提升风险级别,必须列入 Must-fix
  • 至少 2 条证据;证据不足需说明并降分

Common Mistakes

  • 仅报告覆盖率,不评价断言有效性
  • 把日志输出当成断言
  • 忽略失败路径/异常路径

Example (Concise)

Score: 5/10 (Coverage 1, Assertion 0, Negative 1, Data 2, Maintainability 1) Risk: High Must-fix:

  • Tests for parseConfig() contain no behavior assertions (only logs)
  • No negative cases for malformed input

Key Evidence:

  • parseConfig() tests only assert no crash
  • Coverage report shows 62% lines

Notes:

  • Add assertions on outputs and side effects; add invalid input tests.