An automated multi-agent simulation tool that generates realistic WeChat group conversation histories in Excel format for model training and platform testing.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install chat-record-gene
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install chat-record-gene using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Chat Record Generator?
The Chat Record Generator is an orchestration engine designed to synthesize realistic WeChat group conversations from scratch. By rendering structured Excel (.xlsx) workbooks featuring dedicated metadata and dialog streams, this skill provides mock datasets that align with the training specifications of conversational AI systems. Deploying this tool within the Openclaw Skills suite empowers developers to skip tedious manual dataset writing.
By driving multi-agent simulation protocols, this tool dynamically manages isolated agent actors to engage in context-aware conversations on designated themes. Developers can programmatically dictate the style, behavior, and frequency of simulated human speakers to output rich conversational interactions tailored to custom domain scenarios.
Chat Record Generator Use Cases
- AI Assistant Training: Rapidly assemble realistic group chats matching specialized model tuning formats for conversational assistants.
- Knowledge Base Evaluation: Stress-test conversational retrieval pipelines, automatic routing, and FAQ mapping systems with simulated group discussions.
- Multi-Agent Flow Demonstrations: Model, test, and present collaborative multi-agent routines working on shared conversational tasks.
- Complex Mock Datasets: Generate fully structured chat history logs populated with accurate timestamps, customized user styles, and file attachments.
How Chat Record Generator Works
- Define Group Characteristics: Establish core configurations (such as group name, active participants, targeted themes, and total dialog quota) manually or via automated setup scripts.
- Initialize Agent Personas: Spin up isolated subagents (up to 5 concurrently) mimicking diverse personas with distinct ages, professions, styles, and reply ratios.
- Coordinate Dialog Sequence: Gather prompt-generated sub-dialogues, chronological order arrays, and simulated assets into consolidated timelines.
- Write Structured Workbook: Output three distinct Excel sheets containing formatting overrides to resolve cell type display issues, matching platform standards.
Chat Record Generator Setup
Prerequisites
Ensure Node.js is installed. The script depends on the xlsx parsing library to compile output datasets.
1. Install Dependencies
Setup your working folder and install the required utility packages:
mkdir -p /tmp/xlsxparse && cd /tmp/xlsxparse
npm install xlsx
2. Generate Group Configuration
Run the configuration generator to establish your target group profile and chat parameters:
node scripts/generate_group_def.js
(Alternatively, structure your setup definitions based on the schema guides in references/group-schema.md.)
3. Build the Excel Sheet
Execute the final compiler script to synthesize the generated multi-agent conversation logs into your Excel spreadsheet:
node scripts/write_xlsx.js
Chat Record Generator Data Schema & Taxonomy
The generator generates structured workbooks designed to fit standard schema patterns within the Openclaw Skills ecosystem. The exported file structures contain three key sheets:
| Sheet Name | Description | Key Attributes |
|---|---|---|
group_info |
Metadata specifying the chat group boundaries | group_id, group_name, bot_config (null), task_list (null) |
active_members |
Profile records for simulated participants | member_id, nickname, avatar, join_time |
message_stream |
The master log of all serialized dialogue | msg_id, sender_id, msg_type, content, time |
Format Guidelines
- Timeline Calculations: Time strings are derived using standard Excel datetime systems starting with
new Date(Date.UTC(1899, 11, 30)). - Format Customizer: Direct assignments to target cells (e.g.,
cell.z = 'yyyy/m/d h:mm;@') bypass potential formatting bugs during export. - Attachment Mocking: Non-text events (images, files) are represented with sequential pointers (e.g.,
image_1,file_2) alongside empty textual message fields.
Chat Record Generator Advanced Features
- Parallel Personas Generation: Multi-agent sessions are spawned concurrently up to a safe threshold of 5 agents via
sessions_spawnfor efficient output. - Fine-Grained Context Constraints: Customize individual agent attributes including targeted professions, age ranges, and conversational tones to produce authentic human-like behavior.
- Natural Timing Spacing: Implements dynamic gaps of approximately 3 minutes between generic chats and 1-2 hours between major topic transitions to mimic logical conversational patterns.
- Automated Agent Responses: Supports contextual @-mentions of simulated assistant bots, driving targeted multi-turn response generation in a single stream.
SKILL.md
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