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Agent Skill · 研究检索

scenario-testing(场景测试)

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。

scenario-testing 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,权限为接触敏感数据;许可证为 MIT,暂无第三方扫描结果,按安装量在研究检索类的 73,675 个 Skill 里排第 53,815。

总安装 523周安装 22GitHub Stars 公开资料未说明下载 183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
MIT

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:scenario-testing(场景测试)
来源仓库:https://github.com/outfitter-dev/agents
仓库路径:skills/scenario-testing
安装命令:
npx skills add outfitter-dev/agents --skill "scenario-testing"
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。

AgentSkills.tonpx skills
npx skills add outfitter-dev/agents --skill "scenario-testing"

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。

scenario-testing 用于辅助测试设计、用例整理与回归验证。适合让 Agent 编写单元测试、端到端测试或定位失败日志问题。通过 npx skills add 命令从指定仓库安装使用。需确认项目测试框架与运行命令,避免为通过测试而破坏真实逻辑。

适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

来源仓库内路径:skills/scenario-testing

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 outfitter-dev/agents --skill "scenario-testing";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

    它需要接触敏感数据,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

scenario-testing 怎么安装?

在终端执行 npx skills add outfitter-dev/agents --skill "scenario-testing"。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

scenario-testing 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。

scenario-testing 需要什么权限?

权限级别是接触敏感数据。该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

scenario-testing 是免费的吗?

来源标注的许可证是 MIT。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

scenario-testing 用的人多吗?

公开数据是累计安装 523 次,近一周安装 22 次,下载 183 次。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。

scenario-testing 安全吗?

没有查到第三方扫描结果,安全性无法从公开资料判断,安装前请自行审查源码、依赖和命令行为。

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。
  • 许可证明确为 MIT,商用和分发边界好判断。

先想清楚的地方

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 没有第三方扫描结果,安全性只能靠自己读源码判断。
  • 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

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 要求有安全扫描结论才肯安装的团队。
  • 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
scenario-testing

场景测试 · 本页

523—接触敏感数据MIT未收录

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

Scenario Testing

End-to-end validation using real dependencies, no mocks ever.

<when_to_use>

  • End-to-end feature validation
  • Integration testing across services
  • Proof programs demonstrating behavior
  • Real-world workflow testing
  • API contract verification
  • Authentication flow validation

NOT for: unit testing, mock testing, performance benchmarking, load testing

</when_to_use>

<iron_law>

NO MOCKS EVER.

Truth hierarchy:

  1. Scenarios — real dependencies, actual behavior
  2. Unit tests — isolated logic, synthetic inputs
  3. Mocks — assumptions about how things work

Mocks test your assumptions, not reality. When mocks pass but production fails, the mock lied. When scenarios fail, reality spoke.

Test against real databases, real APIs, real services. Use test credentials, staging environments, local instances — but always real implementations.

</iron_law>

<directory_structure>

.scratch/ (gitignored)

Throwaway test scripts for quick validation. Self-contained, runnable, disposable.

CRITICAL: Verify.scratch/ in.gitignore before first use.

scenarios.jsonl (committed)

Successful scenario patterns documented as JSONL. One scenario per line, each a complete JSON object.

Purpose: capture proven patterns, regression indicators, reusable test cases.

Structure:

{"name":"auth-login-success","description":"User logs in with valid credentials","setup":"Create test user with known password","steps":["POST /auth/login with credentials","Receive JWT token","GET /auth/me with token"],"expected":"User profile returned with correct data","tags":["auth","jwt","happy-path"]}
{"name":"auth-login-invalid","description":"Login fails with wrong password","setup":"Test user exists","steps":["POST /auth/login with wrong password"],"expected":"401 Unauthorized, no token issued","tags":["auth","error-handling"]}

</directory_structure>

<scratch_directory>

Purpose

Quick validation without ceremony. Write script, run against real deps, verify behavior, delete or document.

Characteristics

  • Gitignored — never committed, purely local
  • Disposable — delete after validation or promote to permanent tests
  • Self-contained — runnable with single command
  • Real dependencies — actual DB, real APIs, live services

Naming Conventions

  • test-{feature}.ts — feature validation (test-auth-flow.ts)
  • debug-{issue}.ts — investigate specific bug (debug-token-expiry.ts)
  • prove-{behavior}.ts — demonstrate expected behavior (prove-rate-limiting.ts)
  • explore-{api}.ts — learn external API behavior (explore-stripe-webhooks.ts)

Example Structure

// .scratch/test-auth-flow.ts
import { db } from '../src/db'
import { api } from '../src/api'

async function testAuthFlow() {
  // Setup: real test user in real database
  const user = await db.users.create({
    email: 'test@example.com',
    password: 'hashed-test-password'
  })

  // Execute: real HTTP requests
  const loginRes = await api.post('/auth/login', {
    email: user.email,
    password: 'test-password'
  })

  // Verify: actual response
  console.assert(loginRes.status === 200, 'Login should succeed')
  console.assert(loginRes.body.token, 'Should receive JWT token')

  const meRes = await api.get('/auth/me', {
    headers: { Authorization: `Bearer ${loginRes.body.token}` }
  })

  console.assert(meRes.status === 200, 'Auth should work')
  console.assert(meRes.body.email === user.email, 'Should return correct user')

  // Cleanup
  await db.users.delete({ id: user.id })

  console.log('✓ Auth flow validated')
}

testAuthFlow().catch(console.error)

</scratch_directory>

<scenarios_jsonl>

Format

Each line is complete JSON object with fields:

{
  name: string        // unique identifier (kebab-case)
  description: string // human-readable summary
  setup: string       // prerequisites and state preparation
  steps: string[]     // ordered actions to execute
  expected: string    // success criteria
  tags: string[]      // categorization (auth, api, error, etc)
  env?: string        // required environment (staging, local, prod-readonly)
  duration_ms?: number // typical execution time
}

Purpose

  • Pattern library — proven scenarios for regression testing
  • Documentation — executable specification of system behavior
  • Regression detection — compare new behavior against known-good patterns
  • Test generation — source material for permanent test suites

When to Document

Document in scenarios.jsonl when:

  • Scenario validates critical user path
  • Bug was caught by this scenario (regression prevention)
  • Behavior is non-obvious or frequently questioned
  • Integration pattern is reusable across features

Delete from.scratch/ when:

  • One-time debugging script
  • Exploratory testing that didn't find issues
  • Temporary verification during development

</scenarios_jsonl>

Loop: Write → Execute → Document → Cleanup

  1. Write proof program — self-contained script in.scratch/
  2. Run against real dependencies — actual DB, live APIs, real services
  3. Verify behavior — assertions on actual responses
  4. Document if successful — add pattern to scenarios.jsonl
  5. Cleanup — delete script or promote to permanent tests

Each iteration:

  • Script is throwaway (lives in.scratch/)
  • Dependencies are real (no mocks, no stubs)
  • Validation is concrete (actual behavior observed)
  • Pattern captured if valuable (scenarios.jsonl)

<gitignore_check>

MANDATORY before first.scratch/ use:

grep -q '.scratch/' .gitignore || echo '.scratch/' >> .gitignore

Verify.scratch/ directory will not be committed. All test scripts are local-only.

If.gitignore doesn't exist, create it:

[ -f .gitignore ] || touch .gitignore
grep -q '.scratch/' .gitignore || echo '.scratch/' >> .gitignore

</gitignore_check>

1. Setup → Setting up scenario environment

Prepare real dependencies:

  • Spin up local database (Docker, embedded)
  • Configure test API keys (staging credentials)
  • Initialize test data (real records, not fixtures)
  • Verify service connectivity

2. Script → Writing proof program

Create.scratch/ test script:

  • Import real dependencies (no mocks)
  • Setup phase: prepare state
  • Execute phase: perform actions
  • Verify phase: assert on results
  • Cleanup phase: restore state

3. Execute → Running against real dependencies

Run proof program:

  • Execute with real database connection
  • Call actual API endpoints
  • Use live service instances
  • Observe actual behavior (no simulation)

4. Document → Capturing successful patterns

If scenario validates behavior:

  • Extract pattern to scenarios.jsonl
  • Document setup requirements
  • Record expected outcomes
  • Tag for categorization

Delete.scratch/ script or promote to permanent test suite.

ALWAYS:

  • Verify.scratch/ in.gitignore before first use
  • Test against real dependencies (actual DB, live APIs)
  • Use self-contained scripts (runnable with single command)
  • Document successful scenarios in scenarios.jsonl
  • Cleanup test data after execution
  • Tag scenarios for easy filtering
  • Include cleanup phase in all scripts
  • Use test credentials (never production)

NEVER:

  • Use mocks, stubs, or test doubles
  • Commit.scratch/ directory contents
  • Test against production data
  • Skip cleanup phase
  • Assume behavior without verification
  • Promote assumptions to truth
  • Test mocked behavior instead of reality
  • Leave test data in shared environments

ESCALATE when:

  • No staging environment available
  • Real dependencies too expensive to test
  • Test requires destructive production operations
  • Cannot obtain test credentials

Patterns and examples:

Related skills:

  • debugging-and-diagnosis — investigation methodology (scenarios help reproduce bugs)
  • test-driven-development — TDD workflow (scenarios validate features)
  • codebase-analysis — evidence gathering (scenarios provide empirical data)

External resources:

平台分布

Claude Code

28.91%
按下载量换算53

windsurf

21.89%
按下载量换算40

trae

20.35%
按下载量换算37

OpenCode

11.57%
按下载量换算21

Cursor

7.74%
按下载量换算14

Codex

3.66%
按下载量换算7

安全审计

暂无安全审计结果可展示。

权限和风险

接触敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

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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