最简实现
用满足当前需求的最简单实现。
动手前先做减法:列出本次范围内可删除的现有复杂度,先删再加。把新需求当作一开始就存在,按那时会有的设计改造现有结构,再分片交付。报告已删项;没有可删项时写“无”,并列出检查过的范围。
交付前做消融实验:逐项尝试移除或内联本次改动中的抽象和设计,用同一组验收检查对比前后。需求与约束仍满足就保留简化,否则恢复;无法验证的项保留并标为 Unknown。报告每项的去留和验证依据。
避免过度设计:动手前先做减法,交付前通过消融实验删去不必要的抽象和设计。
| Publisher | Dimon94 |
| Repository | skills |
| Skill name | avoid-overengineering |
| Stars | 107 |
| Forks | 21 |
| Bundled files | Instructions only |
| Links |
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
Everything the model needs lives in the instructions — no extra files to sync.
Published by Dimon94 on GitHub. Read the source before you install it.
Install the Avoid Overengineering AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.
TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.
https://github.com/Dimon94/skills/tree/main/skills/avoid-overengineeringAny agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.
git clone --depth 1 https://github.com/Dimon94/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/avoid-overengineering .claude/skills/avoid-overengineeringEnable Avoid Overengineering 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.
AI skills are plain Markdown instructions rather than provider-specific code, so Avoid Overengineering 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.
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.
This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.
用满足当前需求的最简单实现。
动手前先做减法:列出本次范围内可删除的现有复杂度,先删再加。把新需求当作一开始就存在,按那时会有的设计改造现有结构,再分片交付。报告已删项;没有可删项时写“无”,并列出检查过的范围。
交付前做消融实验:逐项尝试移除或内联本次改动中的抽象和设计,用同一组验收检查对比前后。需求与约束仍满足就保留简化,否则恢复;无法验证的项保留并标为 Unknown。报告每项的去留和验证依据。
避免过度设计:动手前先做减法,交付前通过消融实验删去不必要的抽象和设计。
Because you install it once and use it with any model. Avoid Overengineering 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.
Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Dimon94/skills/tree/main/skills/avoid-overengineering. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.
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.
As many as you like. As long as a model supports skills, you can use Avoid Overengineering with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.
It is published on GitHub by Dimon94. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.
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.
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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