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2 changes: 2 additions & 0 deletions README.md
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A typical trail is **packet → response → review → decision**.

[See a fictional source-to-decision trail, including a corrected AI claim](docs/WORKED_CONTINUITY_TRAIL.md).

Once LabNote is set up, you can say things like:

```text
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normal piece of work in the right route with the minimum useful record.
- [Understand review and decisions](review_workflow.md) — see how a response
becomes a reviewed outcome.
- [See a fictional continuity trail](WORKED_CONTINUITY_TRAIL.md) — follow a
source through response, correction, decision and next action.
- [Understand visitor and session identity](visitor_lobby_model.md) — learn
what session records mean in a live workspace.
- [Understand message routing](message_routing_model.md) — follow the model
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# A worked continuity trail

This fictional example shows the smallest useful shape of a LabNote trail. It
does not represent a live project or a deposit in this public source
repository.

## The question

A small team is choosing a durable, visible colour for a new pedestrian bridge.
They want the next person or AI to understand the evidence, what was proposed,
what was challenged and what still needs doing.

They do **not** need every chat message preserved.

## 1. Keep the useful source

The operator selects two useful items: a supplier's finish guide and a local
design brief. They are placed in the appropriate project route with short
descriptions and references.

The record says what these sources are for. It does not turn a pile of browser
tabs or a full conversation transcript into “memory.”

## 2. Frame a packet

A packet asks an AI to produce a shortlist using those sources:

~~~
Task: propose three colour options for the pedestrian bridge.

Use: the recorded finish guide and design brief.

Include: the source behind each option, any uncertainty and a recommended next
check.

Do not decide the final colour.
~~~

The packet gives the next session a bounded question rather than asking it to
guess the whole project.

## 3. Keep the AI response beside the task

The AI returns a shortlist and recommends a dark blue finish. It correctly
links the supplier guide, but it also claims that the design brief requires
dark blue.

That claim is not supported by the brief.

The response remains useful, but it is not silently treated as accepted work.

## 4. Review the claim

A human reviewer leaves a short review note:

~~~
The design brief requires good contrast and low glare; it does not require
dark blue. Keep dark blue as an option, remove the unsupported claim, and ask
for a contrast check against the planned surroundings.
~~~

The review is attached to the response it concerns, rather than becoming an
untraceable correction in a later chat.

## 5. Record the decision and next action

The operator decides:

~~~
Decision: no final colour selected.

Accepted: dark blue, white and yellow remain the shortlist.

Rejected: the unsupported statement that dark blue is required.

Next action: obtain a contrast assessment before choosing.
~~~

The decision is deliberately small. It says what changed, what was accepted,
what was rejected and what should happen next.

## 6. Let the next session continue

A later AI or person starts at the shared entry route and finds the retained
sources, packet, response, review and decision. It does not need the original
chat to know:

- what the question was;
- why one claim was corrected;
- what has not yet been decided; or
- what to do next.

## What this demonstrates

LabNote does not make the AI's first answer correct, and it does not capture
everything automatically. It gives the project a visible route for preserving
the selected work, examining a mistake and carrying the corrected state
forward.

For the practical file-and-record steps, see the [first-use
walkthrough](quickstart.md) and [review workflow](review_workflow.md).
2 changes: 2 additions & 0 deletions locales/zh-CN/README.md
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一条典型轨迹是 **packet → response → review → decision**;对应地,它是工作包(packet)→ 回复(response)→ 审阅(review)→ 决定(decision)。

[查看一条从来源到决定的虚构轨迹,其中包括对 AI 说法的修正](docs/WORKED_CONTINUITY_TRAIL.md)。

设置好 LabNote 后,你可以这样对 AI 说:

```text
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- [进行常规投递](../lobby/ROUTINE_DEPOSIT_QUICKSTART.md) —— 以最小但有用的记录,把一项普通工作放到正确路径中。
- [理解审阅与决定](review_workflow.md) —— 查看回复如何成为经过审阅的结果。
- [查看虚构的连续性轨迹](WORKED_CONTINUITY_TRAIL.md) —— 跟随来源材料经历回复、修正、决定和下一步行动。
- [理解访客会话与会话身份](visitor_lobby_model.md) —— 了解实际工作区中的会话记录代表什么。
- [理解消息路由](message_routing_model.md) —— 遵循会话或角色之间的消息模型。
- [理解登记记录](REGISTRY_RECORDS.md) —— 查看持久登记记录的用途。
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# 一个连续性轨迹示例

这个完全虚构的示例展示了一条 LabNote 轨迹最小而有用的形态。它不代表真实项目,也不是这个公开源仓库中的实际投递记录。

## 问题

一个小团队正在为一座新的步行桥选择耐用、醒目的颜色。他们希望下一位人或 AI 能理解证据、提出过什么、哪些内容受到质疑,以及还有什么工作要做。

他们**不**需要保存每一条聊天消息。

## 1. 保留有用的来源

操作者选择两份有用的材料:供应商的饰面指南和当地设计简报。它们被放入项目中的适当路径,并附上简短说明和引用。

记录说明这些来源是做什么用的。它不会把一堆浏览器标签页或完整对话记录变成“记忆”。

## 2. 形成一个工作包

一个工作包要求 AI 根据这些来源提出一份候选清单:

~~~
任务:为步行桥提出三个颜色选项。

使用:已记录的饰面指南和设计简报。

包括:每个选项的来源、任何不确定之处,以及建议进行的下一项核查。

不要决定最终颜色。
~~~

工作包为下一次会话提供一个有边界的问题,而不是要求它猜测整个项目。

## 3. 将 AI 回复保留在任务旁

AI 返回一份候选清单,并推荐深蓝色饰面。它正确链接了供应商指南,但它也声称设计简报要求使用深蓝色。

该说法没有得到设计简报的支持。

这份回复仍然有用,但不会被悄悄当作已接受的工作。

## 4. 审阅该说法

一位人类审阅者留下简短的审阅记录:

~~~
设计简报要求良好的对比度和低眩光;它并不要求深蓝色。保留深蓝色作为一个选项,删除没有依据的说法,并要求根据计划中的周边环境进行对比度核查。
~~~

审阅附在它所涉及的回复上,而不会变成后来某个聊天里无法追溯的修正。

## 5. 记录决定和下一步行动

操作者作出决定:

~~~
决定:尚未选定最终颜色。

接受:深蓝色、白色和黄色仍是候选清单。

拒绝:没有依据的“必须使用深蓝色”这一说法。

下一步行动:在选择之前取得对比度评估。
~~~

这个决定刻意保持精简。它说明发生了什么变化、哪些内容被接受、哪些被拒绝,以及下一步应做什么。

## 6. 让下一次会话继续

后来的 AI 或人从共享入口路径开始,可以找到保留的来源、工作包、回复、审阅和决定。它不需要原始聊天,也能知道:

- 原来的问题是什么;
- 为什么某个说法被修正;
- 什么尚未决定;以及
- 下一步该做什么。

## 这个示例说明什么

LabNote 不会让 AI 的第一个答案自动正确,也不会自动捕获一切。它为项目提供一条可见的路径,用来保留经过选择的工作、检查错误,并把修正后的状态带到下一步。

如需了解实际的文件和登记步骤,请参阅[首次使用流程](quickstart.md)和[审阅工作流](review_workflow.md)。
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