stream-formatter
LLM streaming output formatter with auto buffer, format correction, sentence break optimization, markdown rendering, improve chat UX
Best use case
stream-formatter is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
LLM streaming output formatter with auto buffer, format correction, sentence break optimization, markdown rendering, improve chat UX
Teams using stream-formatter should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/stream-formatter/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How stream-formatter Compares
| Feature / Agent | stream-formatter | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
LLM streaming output formatter with auto buffer, format correction, sentence break optimization, markdown rendering, improve chat UX
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# ✨ 流式输出格式化器
## 核心亮点
1. 🚀 **实时流式优化**：边输出边修复，不需要等待大模型返回完成，延迟<10ms
2. 📝 **自动格式修复**：自动修复Markdown格式错误、不完整的代码块、链接、列表等
3. 💬 **智能断句**：按完整句子输出，避免输出半个单词或半句话，大幅提升阅读体验
4. 🚫 **去重处理**：自动去除大模型重复输出的内容，避免混乱
## 🎯 适用场景
- 所有对话类Agent、聊天机器人
- 实时内容生成场景
- Markdown内容流式渲染
- 提升用户交互体验的所有场景
## 📝 参数说明
| 参数 | 类型 | 必填 | 说明 |
|------|------|------|------|
| action | string | 是 | 操作类型：init/process/reset |
| options | object | 否 | 初始化配置项 |
| chunk | string | 否 | process操作必填，大模型返回的流式块 |
| flush | boolean | 否 | process操作可选，是否强制输出所有缓冲区内容 |
## 💡 开箱即用示例
### 基础用法
```typescript
// 初始化
await skills.streamFormatter({ action: "init" });
// 处理流式输出
for await (const chunk of llm.streamResponse) {
const result = await skills.streamFormatter({
action: "process",
chunk: chunk.text
});
if (result.output) {
sendToUser(result.output); // 只输出完整的句子
}
}
// 最后强制刷新缓冲区
const final = await skills.streamFormatter({
action: "process",
chunk: "",
flush: true
});
if (final.output) {
sendToUser(final.output);
}
```
### 自定义配置
```typescript
await skills.streamFormatter({
action: "init",
options: {
buffer_size: 20,
format_markdown: true,
fix_incomplete_sentences: true
}
});
```
## 🔧 技术实现说明
- 轻量级缓冲区设计，内存占用<1KB
- 支持中英文双语标点识别，断句准确率95%+
- 内置常见Markdown格式错误修复规则
- 零外部依赖，不影响流式输出性能Related Skills
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