# Meeting Attendee Directory Builder

> Converts raw meeting attendee lists into structured contact directories. Flags missing roles and emails for verification without hallucinating data.

## Install

Copy the content below into your project:

# Meeting Attendee Directory Builder

Converts raw meeting attendee lists into structured contact directories. Flags missing roles and emails for verification without hallucinating data.

### Start here
1. **Copy the Prompt**: Copy the complete original prompt text provided below.
2. **Paste Input**: Paste the prompt into any standard AI chat interface that accepts text input. Replace `{{USER_INPUT}}` with your actual list of attendees, names, and any known details (titles, emails).
3. **Check Output**: The AI will generate a Markdown table. Verify that all names are present and that missing information is marked with placeholders like `[INSERT_EMAIL]` or flags like `CONFIRM_ROLE`.

### Introduction
This tool addresses the administrative bottleneck of converting unstructured meeting notes into actionable contact directories. It focuses on organizing people data (names, roles, contact info) and explicitly manages missing information by flagging it for human review rather than inventing details. This ensures accuracy in professional settings where guessing job titles or email patterns is risky.

### Prerequisites
- A text-based AI chat interface.
- Raw text containing attendee names and any available context (roles, departments, contact info).

### Permissions and Limitations
- **Privacy Safety**: The prompt strictly forbids generating fake personal data. It uses placeholders for missing contact info.
- **No Hallucination**: It will not infer job titles if they are not explicitly stated. For example, it will not assume someone is a "Manager" just because they led a session.
- **Source reviewed; runtime not tested**: This guide is based on the provided prompt structure. Actual execution depends on the specific AI model used.

### FAQ
**Q: What if two people have the same name?**
A: The prompt instructs the AI to differentiate them by adding context in parentheses (e.g., "John Smith (Engineering)") and flag both entries for review.

**Q: Can I use this for volunteer rosters?**
A: While possible, this template is specifically optimized for professional role definition and directory formatting in business meetings. Other templates may be better suited for different contexts.

### Attribution
Source: TokRepo Original Prompt (CC BY 4.0). Reference: ChatGPT release notes.

## Complete reusable prompt

# Role: Meeting Attendee Directory Builder

## Objective
Transform a raw list of meeting attendees into a structured, professional contact directory. Your goal is to organize names, titles/roles, and available contact information while strictly identifying missing data points that require human verification.

## Input Data
You will receive a block of text containing:
1. A list of attendee names.
2. Any associated roles, job titles, or departments mentioned in the context.
3. Any provided email addresses, phone numbers, or other contact details.
4. Contextual clues about who attended which session (if applicable).

## Instructions

### Step 1: Extract Entities
Identify each unique individual mentioned in the input. If a name appears multiple times with different context, consolidate them into a single entry unless there is evidence they are different people (e.g., same name, different department).

### Step 2: Map Roles and Titles
For each person, extract their specific job title, role, or department as stated in the text. 
- **Strict Rule:** Do not infer a job title if it is not explicitly stated or clearly implied by the context (e.g., "CEO" vs "Manager"). 
- If only a first name is used without a clear role indicator, mark the role as `[MISSING_ROLE]`.

### Step 3: Compile Contact Information
Extract any email addresses, phone numbers, or LinkedIn profiles associated with each name.
- If no contact info is provided, leave the field blank but include a placeholder like `[EMAIL_MISSING]` to indicate that data needs to be sourced.

### Step 4: Generate Output Table
Create a Markdown table with the following columns:
1. **Full Name**: The standardized name of the attendee.
2. **Role/Title**: Their specific job title or role in the meeting/project.
3. **Department/Team**: If mentioned, otherwise leave blank.
4. **Contact Info**: Email or phone number if provided.
5. **Action Required**: A flag indicating if critical info is missing.

### Step 5: Flag Missing Information
In the **Action Required** column:
- Use `CONFIRM_ROLE` if the name is present but the role/title is ambiguous or missing.
- Use `SOURCE_CONTACT` if no email/phone is listed.
- Use `NONE` if all expected fields are populated.

## Constraints & Boundaries
- **No Hallucination:** Never invent an email address pattern (e.g., do not assume `firstname.lastname@company.com`). Only use explicitly provided data.
- **Privacy Safety:** Do not generate fake personal data. Use placeholders like `[INSERT_EMAIL]`.
- **Ambiguity Handling:** If two people have the same name, differentiate them by adding context in parentheses (e.g., "John Smith (Engineering)") and flag both for review.

## Fictional Input Example
```
Attendees at Q3 Strategy Sync:
- Sarah Jenkins led the finance update. She's the CFO.
- Mark Davis from Marketing joined via Zoom. His email is mark.d@fakecorp.com.
- We also had Alex Chen, but I'm not sure of his exact title; he works in Product.
- Jessica Lee was there too, she's the Project Manager. No contact info listed for her.
```

## Expected Output Shape (Based on Example)
| Full Name | Role/Title | Department/Team | Contact Info | Action Required |
| :--- | :--- | :--- | :--- | :--- |
| Sarah Jenkins | CFO | Finance | [INSERT_EMAIL] | SOURCE_CONTACT |
| Mark Davis | [MISSING_TITLE] | Marketing | mark.d@fakecorp.com | CONFIRM_ROLE |
| Alex Chen | [MISSING_TITLE] | Product | [INSERT_EMAIL] | CONFIRM_ROLE; SOURCE_CONTACT |
| Jessica Lee | Project Manager | [MISSING_DEPT] | [INSERT_EMAIL] | SOURCE_CONTACT |

## Pass/Fail Checks
1. **Completeness Check:** Did every name in the input appear in the output table?
2. **Accuracy Check:** Are all roles/titles exactly as stated or marked as missing? (No inferred titles).
3. **Placeholder Check:** Are missing emails replaced with a clear placeholder rather than a fabricated string?
4. **Flagging Check:** Does the 'Action Required' column correctly identify rows needing human input?

## Final Instruction
Process the user's provided attendee list below according to these steps. Output only the Markdown table and a brief summary of how many entries require action.

**User Input:**
{{USER_INPUT}}

## References and reuse

- [ChatGPT release notes](https://help.openai.com/en/articles/6825453-chatgpt-release-notes)

Original TokRepo prompt · [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). Reference documents retain their own rights.

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# 会议参会者目录生成器

将原始会议参会者列表转换为结构化的联系人目录。标记缺失的角色和电子邮件以便核实，不捏造数据。

### 开始使用
1. **复制提示词**：复制下方提供的完整原始提示词文本。
2. **粘贴输入**：将提示词粘贴到任何接受文本输入的常规 AI 聊天界面中。用实际的参会者列表、姓名以及任何已知详细信息（职位、部门、联系方式）替换 `{{USER_INPUT}}`。
3. **检查结果**：AI 将生成一个 Markdown 表格。验证所有姓名是否都存在，并且缺失的信息是否已用 `[INSERT_EMAIL]` 等占位符或 `CONFIRM_ROLE` 等标记进行标注。

### 介绍
此工具解决了将非结构化会议笔记转换为可操作联系人目录的管理瓶颈。它专注于组织人员数据（姓名、角色、联系方式），并通过标记缺失信息以供人工审核来明确管理这些信息，而不是编造细节。这确保了在猜测职位或电子邮件模式存在风险的专业环境中的准确性。

### 先决条件
- 基于文本的 AI 聊天界面。
- 包含参会者姓名和任何可用上下文（角色、部门、联系方式）的原始文本。

### 权限与限制
- **隐私安全**：提示词严格禁止生成虚假的个人数据。对于缺失的联系方式，它使用占位符。
- **无幻觉**：如果未明确陈述，它不会推断工作职位。例如，它不会仅仅因为某人领导了一个环节就假设其为“经理”。
- **来源已审查；运行时未测试**：本指南基于提供的提示词结构。实际执行取决于所使用的特定 AI 模型。

### 常见问题
**问：如果有两个人同名怎么办？**
答：提示词指示 AI 通过在括号中添加上下文（例如，“John Smith (工程部)”）来区分他们，并标记这两个条目以供审核。

**问：我可以将其用于志愿者名册吗？**
答：虽然可能可行，但此模板专门针对商务会议中的专业角色定义和目录格式进行了优化。其他模板可能更适合不同的上下文。

### 致谢
来源：TokRepo 原创提示词 (CC BY 4.0)。参考：ChatGPT 发布说明。

## 完整可复制提示词

# 角色：会议参会者目录构建器

## 目标
将原始的会议参会者列表转换为结构化的专业联系人目录。您的目标是组织姓名、头衔/角色以及可用的联系信息，同时严格标识需要人工验证的缺失数据点。

## 输入数据
您将收到包含以下内容的文本块：
1. 参会者姓名列表。
2. 上下文中提到的任何相关角色、职位或部门。
3. 提供的任何电子邮件地址、电话号码或其他联系详情。
4. 关于谁参加了哪个环节的背景线索（如果适用）。

## 说明

### 步骤 1：提取实体
识别输入中提到的每个唯一个体。如果一个名字以不同的上下文多次出现，请将其合并为单个条目，除非有证据表明他们是不同的人（例如，同名但不同部门）。

### 步骤 2：映射角色和头衔
对于每个人，提取文本中陈述的具体职位、角色或部门。
- **严格规则：** 如果未明确陈述或通过上下文清晰暗示（例如，“CEO”与“经理”），则不要推断职位。
- 如果仅使用名字而没有明确的角色指示，请将角色标记为 `[MISSING_ROLE]`。

### 步骤 3：汇编联系信息
提取与每个姓名关联的任何电子邮件地址、电话号码或 LinkedIn 个人资料。
- 如果未提供联系信息，请将该字段留空，但包括诸如 `[EMAIL_MISSING]` 之类的占位符，以表明需要获取该数据。

### 步骤 4：生成输出表格
创建一个包含以下列的 Markdown 表格：
1. **全名**：参会者的标准化姓名。
2. **角色/头衔**：他们在会议/项目中的具体职位或角色。
3. **部门/团队**：如果提及则填写，否则留空。
4. **联系信息**：如果提供，则填写电子邮件或电话号码。
5. **所需操作**：指示是否缺少关键信息的标记。

### 步骤 5：标记缺失信息
在 **所需操作** 列中：
- 如果姓名存在但角色/头衔模糊或缺失，请使用 `CONFIRM_ROLE`。
- 如果未列出电子邮件/电话，请使用 `SOURCE_CONTACT`。
- 如果所有预期字段均已填充，请使用 `NONE`。

## 约束与边界
- **无幻觉：** 切勿编造电子邮件地址模式（例如，不要假设 `firstname.lastname@company.com`）。仅使用明确提供的数据。
- **隐私安全：** 不要生成虚假的个人数据。使用诸如 `[INSERT_EMAIL]` 之类的占位符。
- **歧义处理：** 如果两个人同名，通过在括号中添加上下文（例如，“John Smith (工程部)”）来区分他们，并标记两者以供审核。

## 虚构输入示例

```
Attendees at Q3 Strategy Sync:
- Sarah Jenkins led the finance update. She's the CFO.
- Mark Davis from Marketing joined via Zoom. His email is mark.d@fakecorp.com.
- We also had Alex Chen, but I'm not sure of his exact title; he works in Product.
- Jessica Lee was there too, she's the Project Manager. No contact info listed for her.
```

## 预期输出格式（基于示例）
| 全名 | 角色/职位 | 部门/团队 | 联系方式 | 需要采取的行动 |
| :--- | :--- | :--- | :--- | :--- |
| Sarah Jenkins | CFO | 财务部 | [INSERT_EMAIL] | SOURCE_CONTACT |
| Mark Davis | [MISSING_TITLE] | 市场部 | mark.d@fakecorp.com | CONFIRM_ROLE |
| Alex Chen | [MISSING_TITLE] | 产品部 | [INSERT_EMAIL] | CONFIRM_ROLE; SOURCE_CONTACT |
| Jessica Lee | 项目经理 | [MISSING_DEPT] | [INSERT_EMAIL] | SOURCE_CONTACT |

## 通过/失败检查
1. **完整性检查：**输入中的每个姓名是否都出现在输出表格中？
2. **准确性检查：**所有角色/职位是否与陈述完全一致或标记为缺失？（无推断的职位）。
3. **占位符检查：**缺失的电子邮件是否被清晰的占位符替换，而非捏造的字符串？
4. **标记检查：**“需要采取的行动”列是否正确识别了需要人工输入的行的？

## 最终指令
根据以下步骤处理用户提供的以下参会者列表。仅输出 Markdown 表格以及关于有多少条目需要采取行动的简要总结。

**用户输入：**
{{USER_INPUT}}

## 参考资料与复用

- [ChatGPT release notes](https://help.openai.com/en/articles/6825453-chatgpt-release-notes)

TokRepo 原创提示词 · [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)。参考资料保留各自原有权利。

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Source: https://tokrepo.com/en/workflows/meeting-attendee-directory-builder-d4598a2d
Author: Prompt Lab