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Agent Skill · 研究检索

test-flakiness(测试片状)

用于辅助测试设计、自动化测试、用例整理和回归验证。它适合让 Agent 编写单元测试、端到端测试、测试计划或根据失败日志定位问题。使用时需要确认项目测试框架、运行命令和夹具数据,避免为了通过测试而改坏真实逻辑;涉及浏览器或外部服务时,应区分本地模拟、测试环境和生产环境。

test-flakiness 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为权限未标注;许可证信息还不明确,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 56,981。

总安装 442周安装 19GitHub Stars 16,636下载 155核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:test-flakiness(测试片状)
来源仓库:https://github.com/donchitos/claude-code-game-studios
仓库路径:skills/test-flakiness
安装命令:
npx skills add https://github.com/donchitos/claude-code-game-studios --skill test-flakiness
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/donchitos/claude-code-game-studios --skill test-flakiness

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

用于辅助测试设计、自动化测试、用例整理和回归验证。适合让 Agent 编写单元测试、端到端测试、测试计划或根据失败日志定位问题。使用时需要确认项目测试框架、运行命令和夹具数据,避免为了通过测试而改坏真实逻辑。涉及浏览器或外部服务时,应区分本地模拟、测试环境和生产环境。

安装方式:github,需通过 npx skills add 命令从指定仓库添加。

来源仓库内路径:skills/test-flakiness

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/donchitos/claude-code-game-studios --skill tes…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    权限没有明确标注,第一次使用时只给一个无关紧要的任务,观察它到底读了哪些文件和网络。

常见问题

6 条

test-flakiness 怎么安装?

在终端执行 npx skills add https://github.com/donchitos/claude-code-game-studios --skill test-flakines…。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

test-flakiness 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

test-flakiness 需要什么权限?

权限级别没有明确标注。第一次使用时建议只给无关紧要的任务,观察它实际读写了哪些内容。

test-flakiness 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

test-flakiness 用的人多吗?

公开数据是累计安装 442 次,近一周安装 19 次,下载 155 次,GitHub 16,636 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

test-flakiness 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 第三方扫描已通过,常见的高风险行为没有命中。
  • 来源仓库有 16,636 星,维护活跃度相对可查。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
test-flakiness

测试片状 · 本页

44216,636权限未标注未明确通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

Test Flakiness Detection

A flaky test is one that sometimes passes and sometimes fails without any code change. Flaky tests are worse than no tests in some ways — they train the team to ignore red CI runs, masking genuine failures. This skill identifies them, explains likely causes, and recommends whether to quarantine or fix each one.

Output: Updated tests/regression-suite.md quarantine section + optional production/qa/flakiness-report-[date].md

When to run:

  • Polish phase (tests have had many runs; statistical signal is reliable)
  • When developers start dismissing CI failures as "probably flaky"
  • After /regression-suite identifies quarantined tests that need diagnosis

1. Parse Arguments

Modes:

  • /test-flakiness [ci-log-path] — analyse a specific CI run log file
  • /test-flakiness scan — scan all available CI logs in .github/ or standard log output directories
  • /test-flakiness registry — read existing regression-suite.md quarantine section and provide remediation guidance for already-known flaky tests
  • No argument — auto-detect: run scan if CI logs are accessible, else registry

2. Locate CI Log Data

Option A — GitHub Actions (preferred)

Check for test result artifacts:

ls -t .github/ 2>/dev/null
ls -t test-results/ 2>/dev/null

For Godot projects: GdUnit4 outputs XML results compatible with JUnit format. Check test-results/ for .xml files.

For Unity projects: game-ci test runner outputs NUnit XML to test-results/ by default.

For Unreal projects: automation logs go to Saved/Logs/. Grep for Result: Success and Result: Fail patterns.

Option B — Local log files

If a path argument is provided, read that file directly.

Option C — No log data available

If no logs found:

"No CI log data found. To detect flaky tests, this skill needs test result history from multiple runs. Options: 1. Run the test suite at least 3 times and collect the output logs 2. Check CI pipeline output and save a log to test-results/ 3. Run /test-flakiness registry to review tests already flagged as flaky in tests/regression-suite.md"

Stop and ask the user which option to pursue.


3. Parse Test Results

For each CI log or result file found, parse:

JUnit XML format (GdUnit4 / Unity):

  • Grep for <testcase name= to get test names
  • Grep for <failure or <error to identify failures
  • Parse classname and name attributes for full test identifiers

Plain text logs:

  • Grep for pass/fail patterns:

- Godot: PASSED / FAILED adjacent to test names - Unreal: Result: Success / Result: Fail - Unity: Test passed / Test failed

Build a table: test_id → [run1_result, run2_result, run3_result,...]


4. Identify Flaky Tests

A test is flaky if it appears in the result history with both PASS and FAIL outcomes across runs with no code changes between them.

Flakiness thresholds:

  • High flakiness: Fails in >25% of runs — quarantine immediately
  • Moderate flakiness: Fails in 5–25% of runs — investigate and fix soon
  • Low/suspected flakiness: Fails in 1–5% of runs — monitor; may be genuinely rare failure

For each flaky test, classify the likely cause:

Cause classification

CauseSymptomsFix direction
Timing / asyncFails after awaiting signals or timers; pass rate correlates with system loadAdd explicit await/synchronisation; avoid time-based delays
Order dependencyFails when run after specific other tests; passes in isolationAdd proper setup/teardown; ensure test isolation
Random seedFails intermittently with no pattern; involves RNGPass explicit seed; don't use randf() in tests
Resource leakFails more often later in a test runFix cleanup in teardown; check orphan nodes (Godot) or object disposal (Unity)
External stateFails when a file, scene, or global exists from a prior testIsolate test from file system; use in-memory mocks
Floating pointFails on comparisons like == 0.5Use epsilon comparison (is_equal_approx, Assert.AreApproximately)
Scene/prefab load raceFails when scenes are not yet readyAwait one frame after instantiation; use await get_tree().process_frame

Use Grep to check the test file for timing calls, randf, global state access, or equality comparisons on floats to narrow down the cause.


5. Recommend Action

For each flaky test:

Quarantine (High flakiness):

"Quarantine this test immediately. Disable it in CI by adding @pytest.mark.skip / [Ignore] / GdUnitSkip annotation. Log it in tests/regression-suite.md quarantine section. The test is now opt-in only. Fix the root cause before removing quarantine."

Investigate and fix soon (Moderate):

"This test is intermittently unreliable. Root cause appears to be [cause]. Suggested fix: [specific fix based on cause classification]. Do not quarantine yet — fix the test directly."

Monitor (Low/suspected):

"This test shows suspected flakiness. Collect more run data before quarantining. Note it as 'suspected' in the regression suite."

6. Generate Reports

In-conversation summary

## Flakiness Detection Results

**Runs analysed**: [N]
**Tests tracked**: [N]

### Flaky Tests Found

| Test | System | Fail Rate | Likely Cause | Recommendation |
|------|--------|-----------|--------------|----------------|
| [test_name] | [system] | [N]% | Timing | Quarantine + fix async |
| [test_name] | [system] | [N]% | Float comparison | Fix: use epsilon compare |
| [test_name] | [system] | [N]% | Order dependency | Investigate teardown |

### Clean Tests (no flakiness detected)

[N] tests ran across [N] runs with consistent results — no flakiness detected.

### Data Limitations

[Note if fewer than 5 runs were available — fewer runs = less statistical confidence]

7. Update Regression Suite + Optional Report File

Ask: "May I update the quarantine section of tests/regression-suite.md with the flaky tests found?"

If yes: use Edit to append entries to the Quarantined Tests table. Never remove existing quarantine entries — only add new ones.

Ask (separately): "May I write a full flakiness report to production/qa/flakiness-report-[date].md?"

The full report includes per-test analysis with cause details and engine-specific fix snippets.

After writing:

  • For each quarantined test: "Add the engine-specific skip annotation to disable this test in CI. Re-enable after the root cause is fixed."
  • For fix-eligible tests: "The fix for [test] is straightforward — change the equality comparison on line [N] to use is_equal_approx."
  • Summary: "Once all quarantine annotations are applied, CI should run green. Schedule fix work for the [N] quarantined tests before the release gate."

Collaborative Protocol

  • Never delete test files — quarantine means annotate + list, not remove
  • Statistical confidence matters — with < 3 runs, flag findings as "suspected" not "confirmed"; ask if more run data is available
  • Fix is always the goal — quarantine is temporary; surface the fix direction even when recommending quarantine
  • Ask before writing — both the regression-suite update and the report file require explicit approval. On write: Verdict: COMPLETE — flakiness report written. On decline: Verdict: BLOCKED — user declined write.
  • Flakiness in CI is a team problem — surface the list and recommended actions clearly; do not just silently quarantine without the team knowing

平台分布

Codex

32.89%
按下载量换算51

Claude

32.25%
按下载量换算50

Cursor

19.22%
按下载量换算30

Gemini CLI

9.59%
按下载量换算15

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

权限未标注

当前来源未能明确判断权限范围,默认进入异常复核队列。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

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