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Full Output Enforcement

Community
Hayatelin
full-output-enforcement

覆写 LLM 缺省的截断行为。强制产出完整代码、禁止 placeholder 模式,并干净地处理 token 上限造成的分段。适用于任何需要完整、不删节输出的任务。

Overview

PublisherHayatelin
Repositorytaste-skill-zh-CN
Skill namefull-output-enforcement
Stars
8
Forks
0
Bundled files
Instructions only
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 Hayatelin on GitHub. Read the source before you install it.

Installation

Install the Full Output Enforcement 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/Hayatelin/taste-skill-zh-CN/tree/main/skills/output-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/Hayatelin/taste-skill-zh-CN.git /tmp/taste-skill-zh-CN
mkdir -p .claude/skills
cp -r /tmp/taste-skill-zh-CN/skills/output-skill .claude/skills/full-output-enforcement
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Full Output Enforcement 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 Full Output Enforcement 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 Full Output Enforcement 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.

Full-Output Enforcement(完整输出强制规范)

基本原则

把每个任务都当作 production 等级来对待。不完整的输出就是坏掉的输出。不要为了简洁而优化——要为了完整性而优化。用户要完整的文件,就交出完整的文件;用户要 5 个组件,就交出 5 个组件。没有例外。

禁止的输出模式

以下模式一律视为严重失误,绝对不能出现:

代码区块内:// ...、// rest of code、// implement here、// TODO、/* ... */、// similar to above、// continue pattern、// add more as needed、用光秃秃的 ... 代替被省略的代码

叙述文本内:“如果你想要我可以继续”、“需要的话我可以提供更多细节”、“为了简洁起见”、“其余部分依相同模式类推”、“剩下的以此类推”、“等等”(用来取代实际内容时)、“这部分留给你当练习”

**结构性偷懒:**用户要求完整实现却只输出骨架。只给头尾两段、跳过中间。把重复逻辑缩成一个范例加一段描述。用文本描述代码“应该”做什么,而不是直接把代码写出来。

运行流程

  1. 界定范围(Scope)——完整读完需求。数清楚预期有几个独立交付物(文件、函数、章节、答案),把这个数字锁定下来。
  2. 完整产出(Build)——每个交付物都完整生成。不给半成品草稿,不说“你之后可以自行扩充”。
  3. 交叉核对(Cross-check)——输出前重读原始需求,把交付物数量与第一步锁定的数字比对。少了任何一项,补完再回复。

处理过长输出

当回复接近 token 上限时:

  • 不要压缩剩余章节硬塞进去。
  • 不要跳过中间直接写结论。
  • 以完整质量写到一个干净的断点(函数结尾、文件结尾、章节结尾)。
  • 结尾加上:
[PAUSED — X of Y complete. Send "continue" to resume from: next section name]

收到“continue”时,从停下的地方精准接续。不重述、不重复。

快速检查

定稿前确认:

  • 输出中没有出现上述任何禁止模式
  • 用户要求的每一项都存在且已完成
  • 代码区块里是真正可运行的代码,不是“代码会做什么”的描述
  • 没有任何内容为了省空间被缩短

Frequently asked questions

What does the Full Output Enforcement AI skill do?

覆写 LLM 缺省的截断行为。强制产出完整代码、禁止 placeholder 模式,并干净地处理 token 上限造成的分段。适用于任何需要完整、不删节输出的任务。

Why use Full Output Enforcement on TypingMind?

Because you install it once and use it with any model. Full Output Enforcement 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 Full Output Enforcement in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Hayatelin/taste-skill-zh-CN/tree/main/skills/output-skill. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Full Output Enforcement?

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 Full Output Enforcement?

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

Is the Full Output Enforcement AI skill free?

It is published on GitHub by Hayatelin. Check the repository for licensing terms. 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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