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Multica Verification

Community
it235
multica-verification

判门功能:客观检查产物是否满足验收标准。由 Leader 在门禁点(G1/G2/G3)触发复跑;成员也可用它自证。用于设计检查 / 实现验收 / 测试报告复核。

Overview

Publisherit235
Repositorymultica-best-practices
Skill namemultica-verification
Stars
199
Forks
29
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 it235 on GitHub. Read the source before you install it.

Installation

Install the Multica Verification 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.
    https://github.com/it235/multica-best-practices/tree/main/templates/zh_CN/skills/leader/multica-verification
  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/it235/multica-best-practices.git /tmp/multica-best-practices
mkdir -p .claude/skills
cp -r /tmp/multica-best-practices/templates/zh_CN/skills/leader/multica-verification .claude/skills/multica-verification
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Multica Verification 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 Multica Verification 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 Multica Verification 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.

Verification(判门)

这是什么

验证是一个功能,不是一个角色。它回答一个问题:

每条验收标准,有没有客观证据证明满足?

关键:验证必须是客观可判定的动作(跑命令、看输出、逐条对照),不是「我感觉没问题」。 它不靠某个人的人品,靠证据本身——谁跑结果都一样。

谁来执行

  • 判门:由 Leader 在门禁点(G1 / G2 / G3)触发本 Skill,独立复跑。Leader 不产出任何产物,天然是第三方。
  • 自证:产出者完成后可先跑一遍本 Skill 自证(贴出命令输出),但自证不等于判门——判门必须由非产出者复跑。

门禁出具方必须和被门禁方不同源。作者无法自己盖章「通过」。

Process

  1. 读 Issue 的验收标准。
  2. 把每条标准映射到证据(哪个测试 / 哪条命令 / 哪段输出)。
  3. 复跑验证命令,不引用他人描述的输出。
  4. 检查改动范围:diff 是否只涉及本次需求。
  5. 逐条给出 PASS / FAIL。

Result

PASS —— 所有标准都有充分证据。

FAIL —— 有标准未满足。必须给出:问题、为什么重要、位置、修复方向。

BLOCKED —— 缺信息 / 缺环境,无法验证。如实报告,绝不转成 PASS。

与产品仓 CI 的关系

本 Skill 是 Leader 判门时用的软门禁(无 CI 或 CI 未覆盖的检查)。 产品仓已有 自建 CI 且 G2 相关 check 已绿时,Leader 引用 check 结论,不必重复跑同一命令。

第二层(reviewed 版):各角色 multica-review-* 专属 Reviewer,与判门独立。

为什么有效

「验证」最容易被做成「走个形式」。把验证写成可复跑的动作清单,并规定由非产出者执行,才能同时防住两类作弊:产出者假装验证过(自证环节),以及产出者替自己盖章(判门环节)。

Frequently asked questions

What does the Multica Verification AI skill do?

判门功能:客观检查产物是否满足验收标准。由 Leader 在门禁点(G1/G2/G3)触发复跑;成员也可用它自证。用于设计检查 / 实现验收 / 测试报告复核。

Why use Multica Verification on TypingMind?

Because you install it once and use it with any model. Multica Verification 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 Multica Verification in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/it235/multica-best-practices/tree/main/templates/zh_CN/skills/leader/multica-verification. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Multica Verification?

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 Multica Verification?

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

Is the Multica Verification AI skill free?

Yes. It is published on GitHub by it235 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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