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Verification Before Completion

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jnMetaCode
verification-before-completion

在宣称工作完成、已修复或测试通过之前使用,在提交或创建 PR 之前——必须运行验证命令并确认输出后才能声称成功;始终用证据支撑断言

Overview

PublisherjnMetaCode
Repositorysuperpowers-zh
Skill nameverification-before-completion
Stars
8.1K
Forks
758
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by jnMetaCode on GitHub. Read the source before you install it.

Installation

Install the Verification Before Completion AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/jnMetaCode/superpowers-zh.git /tmp/superpowers-zh
mkdir -p .claude/skills
cp -r /tmp/superpowers-zh/skills/verification-before-completion .claude/skills/verification-before-completion
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Verification Before Completion in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Verification Before Completion on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Verification Before Completion is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

完成前验证

概述

核心原则: 始终用证据支撑结论。

对这条规则敷衍了事,就等于违背了它的精神。

铁律

没有新鲜的验证证据,不许宣称完成

如果你在这条消息中没有运行验证命令,就不能声称测试通过。

门控函数

在宣称任何状态或表达满意之前:

1. 确定:什么命令能证明这个结论?
2. 运行:执行完整命令(重新运行,完整执行)
3. 阅读:完整输出,检查退出码,统计失败数
4. 验证:输出是否支持这个结论?
   - 如果否:用证据说明实际状态
   - 如果是:带证据陈述结论
5. 只有这时:才能做出结论

跳过任何一步 = 说谎,不是验证

常见失败模式

结论需要不够格
测试通过测试命令输出:0 failures之前的运行结果、"应该会通过"
Linter 无报错Linter 输出:0 errors部分检查、推断
构建成功构建命令:exit 0linter 通过、日志看起来没问题
Bug 已修复测试原始症状:通过代码改了,假设已修复
回归测试有效红-绿循环已验证测试只通过了一次
代理已完成VCS diff 显示变更代理报告"成功"
需求已满足逐项核对清单测试通过

红线——停下来

  • 使用"应该"、"大概"、"似乎"
  • 验证前就表达满意("太好了!"、"完美!"、"搞定!"等)
  • 即将提交/推送/创建 PR 却没有验证
  • 信任代理的成功报告
  • 依赖部分验证
  • 想着"就这一次"
  • 累了想赶紧收工
  • 任何暗示成功但实际未运行验证的措辞

防止合理化

借口现实
"应该能行了"运行验证命令
"我有信心"信心 ≠ 证据
"就这一次"没有例外
"Linter 通过了"Linter ≠ 编译器
"代理说成功了"独立验证
"我累了"疲劳 ≠ 借口
"部分检查就够了"部分检查什么也证明不了
"换个说法这条规则就不适用了"精神大于字面

关键模式

测试:

✅ [运行测试命令] [看到:34/34 pass] "全部测试通过"
❌ "应该能通过了" / "看起来对了"

回归测试(TDD 红-绿):

✅ 编写 → 运行(通过)→ 回退修复 → 运行(必须失败)→ 恢复 → 运行(通过)
❌ "我写了回归测试"(没有经过红-绿验证)

构建:

✅ [运行构建] [看到:exit 0] "构建通过"
❌ "Linter 通过了"(linter 不检查编译)

需求:

✅ 重读计划 → 创建核对清单 → 逐项验证 → 报告缺口或完成
❌ "测试通过了,阶段完成"

代理委派:

✅ 代理报告成功 → 检查 VCS diff → 验证变更 → 报告实际状态
❌ 信任代理报告

何时使用

以下情况之前必须使用:

  • 任何形式的成功/完成声明
  • 任何满意的表达
  • 任何关于工作状态的正面陈述
  • 提交、创建 PR、标记任务完成
  • 进入下一个任务
  • 委派给代理

本规则适用于:

  • 准确措辞
  • 同义词和换一种说法
  • 暗示成功
  • 任何传达完成/正确性的沟通

Frequently asked questions

What does the Verification Before Completion AI skill do?

在宣称工作完成、已修复或测试通过之前使用,在提交或创建 PR 之前——必须运行验证命令并确认输出后才能声称成功;始终用证据支撑断言

Why use Verification Before Completion on TypingMind?

Because you install it once and use it with any model. Verification Before Completion is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Verification Before Completion in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/jnMetaCode/superpowers-zh/tree/main/skills/verification-before-completion. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Verification Before Completion?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Verification Before Completion?

As many as you like. As long as a model supports skills, you can use Verification Before Completion with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Verification Before Completion AI skill free?

Yes. It is published on GitHub by jnMetaCode under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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