An automation skill for generating structured WeChat group chat logs in Excel format using parallel multi-agent simulations.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install chat-record-generator
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-generator 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 WeChat Chat Record Generator?
The WeChat Chat Record Generator is a specialized automation pipeline designed to programmatically construct realistic group dialogue datasets from scratch. Developed to integrate smoothly with Openclaw Skills ecosystems, this tool generates high-fidelity Excel (.xlsx) files containing three distinct sheets: group_info, active_members, and message_stream. The outputs are perfectly compatible with the training specifications of mainstream AI agent and chat model fine-tuning environments like Platform Pai.
By leveraging this framework, developers can bypass the tedious manual compilation of conversational data. The skill uses advanced multi-agent orchestrations to spin up highly customized virtual personas who carry out natural multi-turn conversations across designated discussion topics. This solves the data-scarcity bottleneck for training enterprise-grade group assistants and testing multi-agent systems.
WeChat Chat Record Generator Use Cases
- Training Chatbot Assistants: Generate vast corpora of simulated multi-turn conversations on specific topics to fine-tune group-chat-capable LLM agents.
- FAQ System Validation: Populate test environments with complex, simulated chat histories to run diagnostic tests on vector databases, search retrievers, and FAQ engines.
- Multi-Agent Flow Demonstrations: Run mock scenarios to analyze how multiple agents collaborate, debate, and respond under specific cognitive constraints.
- Custom Dataset Synthesis: Generate pristine Excel chat templates containing precise user metadata and message flows for any corporate or domain-specific use case.
How WeChat Chat Record Generator Works
- Define Group Dynamics: Run the configuration script to establish group parameters including custom group names, ID configurations, participant quotas, target topics, and the presence of dedicated AI assistant personas.
- Spawn Parallel Persona Subagents: Spawn isolated subagent sessions concurrently (up to 5 parallel instances) to model distinct user personalities, professions, ages, and communication styles using custom system prompts.
- Simulate Structured Dialogues: Each persona agent generates its designated volume of structured JSON messages mapped to assigned chat themes.
- Compile and Format Excel: A compilation runner merges the JSON payloads, generates chronologically correct timestamps, applies cell formats (mitigating standard sheet cell formatting errors), and writes the fully validated multi-tab Excel file.
WeChat Chat Record Generator Setup
To deploy and start running this tool within the Openclaw Skills environment, ensure you have Node.js installed on your machine. Follow these configuration steps:
- Prepare the Workspace Directory:
mkdir -p /tmp/xlsxparse && cd /tmp/xlsxparse
- Install Required Excel Generation Dependencies:
npm install xlsx
- Generate Group Definition File: Run the metadata builder script to configure your group parameters:
node scripts/generate_group_def.js
- Run the Chat Generation Routine: Once configuration files and subagents complete their conversation tasks, parse the resulting JSON outputs and execute the compilation script to output your ready-to-use spreadsheet:
node scripts/write_xlsx.js
WeChat Chat Record Generator Data Schema & Taxonomy
The tool structures its generation workflow into three normalized sheets within the final Excel workbooks, following a strict layout compatible with Platform Pai data inputs:
| Sheet Name | Description | Key Fields / Schema Rules |
|---|---|---|
group_info |
Metadata about the virtual chat group itself | Contains fields like group_name, group_id, member_count, and bot_config (defaults to null). |
active_members |
Complete directory of simulated group participants | Includes user attributes like nickname, role_type (active, standard, trigger-happy), and join_time with granular seconds-level format. |
message_stream |
The sequential log of simulated chat events | Chronologically arranged events featuring message types (text, image/file), sender nicknames, content payloads, and empty attachments relative to message type. |
Crucial Formatting Fixes
- Excel Time Epoch: Employs the Excel-standard epoch offset starting from
new Date(Date.UTC(1899, 11, 30))for date computations. - Format Preservation (numFmt Bypass): Cell format parameters must manually bind the cell property
.zto retain formatting structures during parsing (e.g.,'yyyy/m/d h:mm;@'or'yyyy/m/d h:mm:ss;@').
WeChat Chat Record Generator Advanced Features
- Multi-Agent Simulation Concurrency: Dynamically balances load by running up to 5 concurrent subagents at a time via sessions_spawn runtime, queueing additional instances sequentially to prevent rate limiting.
- Dynamic Bot Interaction Hooks: Orchestrates simulated active AI bots inside the group chat, dynamically injecting @ replies to mimic automated group agent workflows.
- Granular Timestamp Generation Rules: Generates realistic conversational pauses, distributing message sequences with realistic ~3-minute windows (
0.002Excel day fraction) and long gaps between distinct group chat topics. - Flexible Media Emulation: Supports mixed-media simulation, outputting dedicated file pointer templates (
file_Norimage_N) alongside standard text strings inside Openclaw Skills data channels.
SKILL.md
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