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Mini Program Implementation Skill

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NocodeMrLi
mini-program-implementation-skill

Implement bounded features and fixes in existing or new mini-program codebases while preserving repository rules, user-owned changes, approved behavior, assets, framework conventions, and evidence boundaries. Use when users ask to write or modify mini-program code, implement a confirmed specification or architecture, add a scoped feature, remove behavior semantically, update internal documentation required by a change, or carry out a well-defined small fix that does not require root-cause investigation. Establishes a baseline and change boundary, applies test-driven small steps, distinguishes source from generators and build artifacts, verifies the affected contract, and never reports source completion as device validation, formal acceptance, upload, or release.

Overview

PublisherNocodeMrLi
Repositorymini-program-engineering-skill-suite
Skill namemini-program-implementation-skill
Stars
91
Forks
7
Bundled files
4
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.

  • 4 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

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

Installation

Install the Mini Program Implementation Skill 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/NocodeMrLi/mini-program-engineering-skill-suite/tree/main/skills/mini-program-implementation-skill
  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/NocodeMrLi/mini-program-engineering-skill-suite.git /tmp/mini-program-engineering-skill-suite
mkdir -p .claude/skills
cp -r /tmp/mini-program-engineering-skill-suite/skills/mini-program-implementation-skill .claude/skills/mini-program-implementation-skill
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Mini Program Implementation Skill 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 Mini Program Implementation Skill 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 Mini Program Implementation Skill 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.

/mini-program-implementation-skill — 小程序工程实现

在已确认的改动边界内,把稳定产品语义或工程方案落到代码和必要内部文档。保护现有项目、用户已有改动与已验收行为;实现完成后只报告匹配证据,不等于正式验收。

进入条件与边界

  • 接收已确认规格/架构、明确的小型实现目标,或已有项目的事实图和改动边界。
  • 已有项目先读取规则、相关源码/配置/文档、版本控制状态、测试与构建方式;记录用户已有改动,不覆盖、不回退、不顺手整理无关文件。
  • 产品语义、数据规则、权限或外部服务影响尚不明确时,停在决策点;单纯可观察故障但根因未知时转入调试阶段。
  • 不新增未获来源支持的入口、状态、付费、广告、权限理由或业务逻辑;不把内部实现细节擅自扩写到公开说明。

受控实现流程

  1. 写出目标、允许修改文件/模块、必须保护内容、明确不做项、验证命令与回滚条件,建立修改前基线。
  2. 识别真实事实源,区分手写源码、生成脚本、配置、资源和构建产物;应修改生成脚本时不直接把生成结果当唯一修复点。
  3. 为新增行为或缺陷先建立能失败的测试或最小复现,执行 RED 并确认失败原因正确。
  4. 写最小改动到 GREEN,运行目标测试;再做不改变行为的 REFACTOR,每一步保持范围可审查。
  5. 检查空、错误、边界、重复操作、异步与受影响共享契约;发现相邻问题只记录,不静默扩大范围。
  6. 复核差异、用户已有改动、敏感内容和必要内部文档,运行与风险相称的静态、单元或集成检查。
  7. 使用 工程实现工作流 自检,并按 实现交接模板 交给验证阶段。

涉及资产时建立资产谱系,至少记录原始/衍生关系、处理工具与方式、目标槽位、尺寸、透明通道、SHA-256、批准范围和替换关系。命令中断或超时后把在途写入记为 unknown,先刷新工作区、进程、产物和日志,不盲目重放。

涉及云资产、批量数据或会改变运行行为的派生资产时,同时按 云资产、批量导入与派生资产交付工作流 分离 manifest、执行、完整性复验和报告;上传或导入成功不能自动升级为运行时已接线、真实页面已使用或用户已验收。

最低输出

  • 实现目标、输入事实、假设、改动边界与保护项。
  • 逐文件改动清单,以及为何属于当前目标。
  • RED → GREEN → REFACTOR 或无法采用测试驱动时的明确理由与替代证据。
  • 已执行验证、结果、未验证层级、残余风险和回滚方式。
  • 云资产触发时的 manifest 指纹、远端基线、执行计数、完整性结果、失败清单、激活/回滚记录和不可推出状态。
  • 状态最多报告到证据支持的 implemented、built 或 locally-verified;不得推导真机、云端、验收、上传或发布。

停止条件

遇到无法安全区分的用户改动、互相冲突的事实源、需要扩大产品范围的选择、未批准的高风险数据/权限/外部服务变更、不可接受的迁移或回滚缺口时停止。不得以覆盖文件、重置工作区或跳过失败测试强行推进。

独立与套件协作

独立安装时,本 Skill 可完成边界明确的代码实现。位于套件中时,接收规格、架构或调试阶段的稳定交接,只输出实现差异与验证入口,不直接调用其他组件脚本。

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Mini Program Implementation Skill AI skill do?

Implement bounded features and fixes in existing or new mini-program codebases while preserving repository rules, user-owned changes, approved behavior, assets, framework conventions, and evidence boundaries. Use when users ask to write or modify mini-program code, implement a confirmed specification or architecture, add a scoped feature, remove behavior semantically, update internal documentation required by a change, or carry out a well-defined small fix that does not require root-cause investigation. Establishes a baseline and change boundary, applies test-driven small steps, distinguish...

Why use Mini Program Implementation Skill on TypingMind?

Because you install it once and use it with any model. Mini Program Implementation Skill 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 Mini Program Implementation Skill in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/NocodeMrLi/mini-program-engineering-skill-suite/tree/main/skills/mini-program-implementation-skill. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Mini Program Implementation Skill?

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 Mini Program Implementation Skill?

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

Is the Mini Program Implementation Skill AI skill free?

Yes. It is published on GitHub by NocodeMrLi 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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