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scenario-testing场景测试

Agent Skill

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。

总安装

523

周安装

22

GitHub Stars

公开资料未说明

下载量

183
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT

最后核验

2026-05-01

来源状态

来源可访问

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"

简介

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。

SKILL.md

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:

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

04

需要参考平台分布和安装热度时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

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

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。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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