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Agent Skill · 研究检索

evanflow-tdd(埃文流 TDD)

evanflow-tdd 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

evanflow-tdd 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,权限为只读;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 72,323。

总安装 188周安装 8GitHub Stars 337下载 66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:evanflow-tdd(埃文流 TDD)
来源仓库:https://github.com/evanklem/evanflow
仓库路径:skills/evanflow-tdd
安装命令:
npx skills add https://github.com/evanklem/evanflow --skill evanflow-tdd
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/evanklem/evanflow --skill evanflow-tdd

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。

evanflow-tdd 基于测试驱动开发原则,强调通过公共接口验证行为而非实现细节。适用于编写可维护测试、避免水平切片和过度依赖内部结构的情况,提升测试稳定性。核心是写“垂直切片”测试,描述系统能力而非机制,例如“用户可在周限额内完成操作 X”。

安装命令为 npx skills add https://github.com/evanklem/evanflow --skill evanflow-tdd。建议确认项目是否支持 TDD 流程及测试框架兼容性。

来源仓库内路径:skills/evanflow-tdd

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/evanklem/evanflow --skill evanflow-tdd;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

    它标注为只读,先用只读任务试一次,确认行为符合预期再扩大使用范围。

常见问题

6 条

evanflow-tdd 怎么安装?

在终端执行 npx skills add https://github.com/evanklem/evanflow --skill evanflow-tdd。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

evanflow-tdd 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。

evanflow-tdd 需要什么权限?

权限级别是只读。该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

evanflow-tdd 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。

evanflow-tdd 用的人多吗?

公开数据是累计安装 188 次,近一周安装 8 次,下载 66 次,GitHub 337 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。

evanflow-tdd 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。
  • 标注为只读权限,试用阶段不容易误改本地文件。

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。
  • 只想先试试效果、不希望它改动本地文件的人。

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
evanflow-tdd

埃文流 TDD · 本页

188337只读未明确通过

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

EvanFlow: TDD

Vocabulary

See evanflow meta-skill. Key terms: vertical slice, behavior through public interface, deep module.

Core Principle

Tests verify behavior through public interfaces, not implementation details. Code can change entirely; tests shouldn't break unless behavior changes.

Good test: "user can perform action X within their weekly rate limit" — describes capability.

Bad test: "calls createX() with status 'QUEUED' then queues a job" — describes mechanics. Renames break it.

Anti-Pattern: Horizontal Slices

DO NOT write all tests first then all implementation. That produces tests of *imagined* behavior, not *actual* behavior. They become insensitive to real changes.

DO vertical slices: one test → one implementation → repeat. Each test responds to what you learned from the previous cycle.

When to Use

  • Any production code change (new feature, bug fix, behavior change, refactor with behavior implications)
  • All new code in your backend's routers and services
  • All new code in your frontend that has testable logic (not pure-presentation components)

When to Skip (with explicit user approval)

  • Throwaway prototypes
  • Generated code (e.g., database.types.ts)
  • Configuration files
  • Pure-presentation React components with no logic

The Flow

1. Embedded Grill — "What to Test"

Before writing any test, confirm with the user:

  • "Which behaviors matter most? We can't test everything."
  • "What's the public interface — what will callers actually use?"
  • "Are there opportunities to make this a deep module (small interface, complex internals)?"
  • "Where do tests need to integrate with real services (DB, payment provider, email provider) vs. where can we test in isolation?"

Default to integration-style tests against real services (real DB, real queue, real cache) where feasible. Mocked dependencies frequently mask divergence between test and production behavior. Document any project-specific exception in your CLAUDE.md.

2. Tracer Bullet

Write ONE test for ONE behavior end-to-end. Prove the path works.

RED:   Write test → run → confirm it fails for the RIGHT reason
GREEN: Write minimal code → run → confirm it passes

3. Incremental Loop

For each remaining behavior:

RED:   Write next test → fails
GREEN: Minimal code to pass → passes

Rules:

  • One test at a time
  • Only enough code to pass the current test
  • Don't anticipate future tests
  • Tests focus on observable behavior, not internals

4. Refactor

After all tests pass:

  • Look for duplication
  • Look for deepening opportunities: small interface hiding complex implementation (deletion test applies)
  • Run tests after each refactor step
  • Never refactor while RED. Get to GREEN first.

Per-Cycle Checklist

[ ] Test describes behavior, not implementation
[ ] Test uses public interface only
[ ] Test would survive an internal refactor (rename, restructure)
[ ] Code is minimal for this test
[ ] No speculative features added
[ ] Test fails for the right reason before code is written
[ ] ASSERTION IS CORRECT — see warning below

⚠️ Assertion-Correctness Warning

Industry research (HumanEval evaluation across four LLMs) found that over 62% of LLM-generated test assertions were incorrect. This is the single most likely failure mode in LLM-driven TDD: the test passes, but it's testing the wrong thing.

Before writing any test assertion, verify:

  • Does this assertion match what the user actually wants? Don't assert on behavior you imagined — assert on behavior the spec/contract names.
  • Is this the assertion's most-precise form? "result is truthy" is weaker than "result equals 42". Loose assertions catch wrong things and miss right things.
  • Would this assertion still pass if the code was subtly wrong? Mentally introduce a one-character bug — does the assertion catch it? If not, the assertion is too weak.
  • Are you asserting on the right field? A common failure: asserting response.status when the meaningful field is response.body.error.
  • For computed values: did you compute the expected value correctly? Don't trust your own arithmetic — verify by hand or another path.

When in doubt about what to assert, STOP and ask the user rather than guess. An asserted-on-the-wrong-thing test is worse than no test — it provides false confidence.

Hard Rules

  • Vertical slices only. Never write all tests first.
  • Test behavior, not internals. If a rename breaks a test but behavior didn't change, the test was wrong.
  • Watch the test fail. If you didn't see RED, you don't know it tests the right thing.
  • Never auto-commit. TDD cycle is RED-GREEN-REFACTOR, not RED-GREEN-REFACTOR-COMMIT.
  • Default to real services for integration tests. Mocked databases routinely diverge from production behavior — prefer a test DB unless your project documents a specific exception.

Hand-offs

  • Tests + impl complete for the task → return to evanflow-executing-plans to mark task done
  • Discovered the interface is wrong → evanflow-design-interface to redesign
  • Discovered deeper architectural issue → evanflow-improve-architecture

平台分布

Codex

35.62%
按下载量换算24

Claude

28.79%
按下载量换算19

Cursor

18.92%
按下载量换算12

Gemini CLI

8.58%
按下载量换算6

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

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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