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qa-game问答游戏

Agent Skill

qa-game 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。

总安装

7,295

周安装

298

GitHub Stars

109

下载量

2,336
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

unknown

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:qa-game(问答游戏)
来源仓库:https://github.com/opusgamelabs/game-creator
仓库路径:skills/qa-game
安装命令:
npx skills add https://github.com/opusgamelabs/game-creator --skill qa-game
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/opusgamelabs/game-creator --skill qa-game

简介

qa-game 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。

  • 它支持基于关键词或上下文进行信息匹配与排序,适用于研究、问答或内容筛选类任务。
  • 通过安装命令 npx skills add https://github.com/opusgamelabs/game-creator --skill qa-game 添加技能,具体用法可参考仓库中的 SKILL.md。
  • 安装前请确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写操作。
  • qa-game 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

QA Game

Add automated QA testing with Playwright to an existing game project. Tests verify your game boots, scenes work, scoring functions, and visuals haven't broken — like a safety net for your game.

Instructions

Analyze the game at $ARGUMENTS (or the current directory if no path given).

First, load the game-qa skill to get the full testing patterns and fixtures.

Step 1: Audit testability

  • Read package.json to identify the engine and dev server port
  • Read vite.config.js for the server port
  • Read src/main.js to check if window.__GAME__, window.__GAME_STATE__, window.__EVENT_BUS__ are exposed
  • Read src/core/GameState.js to understand what state is available
  • Read src/core/EventBus.js to understand what events exist
  • Read src/core/Constants.js to understand game parameters (rates, speeds, durations, max values)
  • Read all scene files to understand the game flow
  • Read design-brief.md if it exists — it documents expected mechanics, magnitudes, and win/lose reachability

Step 2: Setup Playwright

  1. Install dependencies: npm install -D @playwright/test @axe-core/playwright && npx playwright install chromium
  2. Create playwright.config.js with the correct dev server port and webServer config
  3. Expose window.__GAME__, window.__GAME_STATE__, window.__EVENT_BUS__, window.__EVENTS__ in src/main.js if not already present
  4. Create the test directory structure: tests/ ├── e2e/ │ ├── game.spec.js │ ├── visual.spec.js │ └── perf.spec.js ├── fixtures/ │ └── game-test.js └── helpers/ └── seed-random.js
  5. Add npm scripts: test, test:ui, test:headed, test:update-snapshots

Step 3: Generate tests

Write tests based on what the game actually does:

  • game.spec.js: Boot test, scene transitions, input handling, scoring, game over, restart
  • visual.spec.js: Screenshot regression for stable scenes (gameplay initial state, game over). Skip active gameplay screenshots — moving objects make them unstable.
  • perf.spec.js: Load time budget, FPS during gameplay, canvas dimensions

Follow the game-qa skill patterns. Use gamePage fixture. Use page.evaluate() to read game state. Use page.keyboard.press() for input.

Step 4: Design-intent tests

Add a test.describe('Design Intent') block to game.spec.js. These tests catch mechanics that technically exist but are too weak to matter.

  1. Lose condition: Detect deterministically whether the game has a lose state. Read GameState.js — if it has a won, result, or similar boolean/enum field, the game distinguishes win from loss. Also check render_game_to_text() in main.js — if it returns distinct outcome modes (e.g., 'win' vs 'game_over'), the game has a lose state. If a lose state exists: start the game, provide NO input, let it run to completion (use page.waitForFunction with the round duration from Constants.js). Assert the outcome is the losing one (e.g., won === false, mode === 'game_over'). This assertion is non-negotiable. Do NOT write a test that passes when the player wins by doing nothing. If the current game behavior is "player wins with no input," that is a bug — write the test to catch it.
  2. Opponent/AI pressure: If an AI-driven mechanic exists (auto-climbing bar, enemy spawning, difficulty ramp), test that it produces substantial state changes. Run the game for half its duration without player input. Assert the opponent's state reaches at least 25% of its maximum. If design-brief.md exists, use its expected magnitudes for thresholds. Otherwise, derive from Constants.js: calculate rate * duration and assert it reaches meaningful levels.
  3. Win condition: Test that active player input leads to a win. Provide rapid input throughout the round and assert the outcome is a win.

Step 5: Entity interaction audit

Audit collision and interaction logic for asymmetries that would confuse a first-time player.

If design-brief.md has an "Entity Interactions" section, use it as the checklist. Otherwise, audit GameScene.js directly:

  1. Find all collision handlers, overlap checks, or distance-based interactions
  2. Map which entities interact with which others
  3. Flag any visible moving entity that interacts with one side (player OR opponent) but not the other — add a // QA FLAG: asymmetric interaction comment in the test file noting the entity name and the asymmetry

This is a flag, not a hard fail. Some asymmetries are intentional (e.g., hazards that only affect the player). The flag ensures the asymmetry is a conscious design choice, not an oversight.

Step 6: Run and verify

  1. Run npx playwright test to execute all tests
  2. If visual tests fail on first run, that's expected — generate baselines with npx playwright test --update-snapshots
  3. Run again to verify all tests pass
  4. Summarize results

Step 7: Report

Tell the user in plain English:

  • How many tests were created and what they check
  • How to run them: npm test (headless), npm run test:headed (see the browser), npm run test:ui (interactive dashboard)
  • "These tests are your safety net. Run them after making changes to make sure nothing broke."

Example Usage

/qa-game examples/flappy-bird

Result: Installs Playwright → creates 15 tests (boot, scene transitions, input, scoring, restart, game-over, visual regression, FPS, load time) → generates tests/ directory with fixtures and helpers → all tests pass. Run npm test anytime after changes.

Next Step

Tell the user:

Your game now has automated tests! Finally, run /game-creator:review-game for a full architecture review — it checks your code structure, performance patterns, and gives you a score with specific improvement suggestions. Pipeline progress: /make-game → /design-game → /add-audio → /qa-game → /review-game

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

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

展示第三方安全扫描或审计结果

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

Codex

34.79%
按下载量换算813

Claude

28.19%
按下载量换算659

Cursor

18.79%
按下载量换算439

Gemini CLI

10.1%
按下载量换算236

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

操作浏览器

该 Skill 可能涉及浏览器控制能力,使用时可能读取或操作网页内容,需要在受控环境中确认权限边界。

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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