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Multica Review Backend

Community
it235
multica-review-backend

后端实现专属评审框架。由 BackendReviewer 调用,对 BackendDev 产出的 API 契约与后端实现做专业分析(契约质量/设计吻合/错误处理/单测充分性/验收对照),输出 PASS/FAIL 与修改清单,汇报 Leader。

Overview

Publisherit235
Repositorymultica-best-practices
Skill namemultica-review-backend
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 Review Backend 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/reviewer/multica-review-backend
  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/reviewer/multica-review-backend .claude/skills/multica-review-backend
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Multica Review Backend 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 Review Backend 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 Review Backend 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.

后端实现专业评审(BackendReviewer)

本 skill 提供对 **后端实现产物(API 契约 + 实现)**的结构化专业评审框架。调用方为 BackendReviewer,评审对象为 BackendDev 经 multica-artifact-backend 回传的链接与变更文件列表,以及 Leader 派活时给的架构设计引用。

什么时候用

  • BackendReviewer 收到 Leader 派发的「评审 API 契约 / 后端实现」任务时。
  • 产物修改后进入复审轮次时(对照上一轮修改清单逐条核对)。

评审维度(逐项给结论)

  1. 契约质量:字段命名、状态码、错误模型、版本策略是否清晰自洽。
  2. 设计吻合度:实现是否对齐 Architect 方案,偏离须有说明。
  3. 错误处理:异常分支、超时、幂等、边界条件是否覆盖。
  4. 单测充分性:关键路径、边界、异常分支是否覆盖;有无无效测试(标记并阻断)。
  5. TDD 与测试映射:契约中的 API-ID 是否有对应单元测试;关键路径与异常是否覆盖。
  6. 验收对照:实现是否真正满足每条 AC-。

输出格式

text
【后端实现评审】<变更链接>
结论:PASS / FAIL
阻断项(FAIL 时必填,每项含 理由 / 涉及点 / 修改方向):
- ...
建议项(非阻断):
- ...
与上一轮修改清单核对(复审时):已解决 X 项 / 未解决 Y 项
轮次:第 N / 3 轮

结论与修改清单汇报给 Leader,不自行改代码、不自行通知 BackendDev。

边界

  • 只评后端契约与实现、单测,不评前端、UI、需求、架构设计本身、测试用例。
  • 不替代 Leader 的通用门禁(multica-verification skill)。
  • 架构层面分歧交 ArchReviewer,你只管实现层质量。
  • 第 3 轮仍 FAIL → 标注「升级人类」,交 Leader 处理,停止循环。

Frequently asked questions

What does the Multica Review Backend AI skill do?

后端实现专属评审框架。由 BackendReviewer 调用,对 BackendDev 产出的 API 契约与后端实现做专业分析(契约质量/设计吻合/错误处理/单测充分性/验收对照),输出 PASS/FAIL 与修改清单,汇报 Leader。

Why use Multica Review Backend on TypingMind?

Because you install it once and use it with any model. Multica Review Backend 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 Review Backend 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/reviewer/multica-review-backend. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Multica Review Backend?

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 Review Backend?

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

Is the Multica Review Backend 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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