Multica Backend Impl

Community

it235

multica-backend-impl

后端实现：读 JIRA/Confluence/架构设计，TDD 驱动 API 与服务端编码。用于 @BackendDev 契约先行、单元测试、集成验证。

Overview

Publisher	it235
Repository	`multica-best-practices`
Skill name	`multica-backend-impl`
Stars	199
Forks	29
Bundled files	Instructions only
License	MIT
Links	Skill source Repository

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 Backend Impl AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/it235/multica-best-practices/tree/main/templates/zh_CN/skills/backend/multica-backend-impl
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/it235/multica-best-practices/tree/main/templates/zh_CN/skills/backend/multica-backend-impl

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

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/backend/multica-backend-impl .claude/skills/multica-backend-impl

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Multica Backend Impl 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 Backend Impl 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 Backend Impl 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.

Backend Implementation

Purpose

在已批准的技术设计与 Issue 范围内，契约先行 + TDD 完成后端实现。

与 multica-artifact-backend 分工：backend-impl 管怎么读上游、怎么写代码与测试；artifact-backend 管 API 契约文档规范与 Confluence 落地。

Platform 协作

Platform skill	本 skill 用途
`multica-platform-jira`	读 Issue / AC（经 Issue Hub 或 JIRA URL）
`multica-platform-confluence`	读 PRD / 架构设计（`fetch_page.py`）

发布契约走 multica-artifact-backend → platform skill；本 skill 不直接写 REST。

开工前：读上游（必做）

顺序	来源	方式
1	Issue / 验收标准	`multica-platform-jira` → `get-issue`；含 `linked_issues`
2	关联 JIRA（Relates/依赖等）	`get_issue.py --with-linked` → 必读关联的 Confluence/Figma（见 `upstream-read.md`）
3	PRD / 需求正文	JIRA 解析 Confluence → `fetch_page.py`
4	架构设计	Leader 派活链接或 JIRA 描述中的设计链接 → `fetch_page.py`
5	现有代码	codegraph / 局部阅读，遵循项目约定

信息不足 → BLOCKED，写入待确认项，不猜接口形状。

G2 前：单元测试（硬门禁）

merge 到 deploy branch 之前，必须在本地（或项目标准 dev 容器）跑通全部单元测试：

text
1. 跑项目约定的 unit test 命令（如 mvn test / npm test / pytest tests/unit）
2. 必须 0 failure；既有测试不得因本次改动失败（无退步）
3. 新增/变更 API 须有对应单元测试（Red-Green-Refactor）
4. 失败 → 自行修复后再跑，直至全绿
5. 仅当本地无法跑（缺依赖/缺硬件）→ BLOCKED 列环境缺口，不得 push 后「等 CI 再看」

禁止：把单元测试失败留给 Jenkins/CICD 才发现；禁止为通过而删除或 skip 既有测试（除非 Architect/Leader 书面砍 scope）。

CI 是 二次确认，不是第一次跑单测的地方。

TDD 流程（核心）

text
1. 读 AC- / 架构「实现步骤（后端）」→ 列出 API-ID 与行为
2. 写/更新 api-contract.md（交 multica-artifact-backend 发布，供前端并行）
3. Red   — 写失败单元测试（业务逻辑、边界、异常）
4. Green — 最小实现使测试通过
5. Refactor — 保持测试绿，不扩 scope
6. 集成验证 — 跑项目约定命令，贴证据

规则：

业务逻辑须有单元测试；纯 CRUD 也需覆盖异常分支与边界。
先改契约文档再改实现；Breaking change 须升版本并知会 Leader / @FrontendDev。
不静默改字段名/语义；与 Architect 设计偏离须说明。

编码规则

先读再改；遵循现有分层与命名。
范围聚焦；不无关重构。
错误处理：超时、幂等、并发按设计实现。
报告可复跑的命令与结果。

完成证据

API 契约 Confluence 链接（multica-artifact-backend）
变更文件列表
单元测试：命令 + 全绿结果（merge deploy branch 前已跑通；0 failure、无测试退步）
集成测试（若项目要求）命令 + 结果
与 API-ID / AC- 的追溯说明
已知限制

挂载顺序（@BackendDev）

text
multica-backend-impl → multica-artifact-backend

为什么有效

契约 + Confluence 文档让前端/T1 并行；TDD 把验收标准提前锁死在测试里，减少 G2 汇合返工。

Frequently asked questions

What does the Multica Backend Impl AI skill do?

后端实现：读 JIRA/Confluence/架构设计，TDD 驱动 API 与服务端编码。用于 @BackendDev 契约先行、单元测试、集成验证。

Why use Multica Backend Impl on TypingMind?

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

Which AI models can use Multica Backend Impl?

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 Backend Impl?

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

Is the Multica Backend Impl 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Backend Implementation

Purpose

Platform 协作

开工前：读上游（必做）

G2 前：单元测试（硬门禁）

TDD 流程（核心）

编码规则

完成证据

挂载顺序（@BackendDev）

为什么有效

Frequently asked questions

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