A specialized AI skill for engineering bilingual email translation workflows that handle subject normalization, body formatting, and history summarization.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install bilingual-mail-translator
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 bilingual-mail-translator 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 Bilingual Mail Translator?
The Bilingual Mail Translator is a sophisticated prompt-layer contract designed for developers building or refining AI-driven email assistants. It focuses on converting raw email data into user-friendly, Chinese-English bilingual outputs. By leveraging this Openclaw Skills asset, developers can ensure consistency across subject translation, date normalization, and body formatting without cluttering the program-side logic with complex heuristics.
This skill is particularly valuable for applications requiring language-learning support or side-by-side review capabilities. It enforces a strict separation of concerns, keeping formatting and linguistic decisions within the prompt layer while the program remains focused on data extraction and LLM runtime execution.
Bilingual Mail Translator Use Cases
- Creating automated inbox notification systems that provide side-by-side bilingual translations for international teams.
- Building language-learning tools that help users understand English emails by providing context-aware Chinese translations.
- Developing email summarization agents that condense long reply chains and quoted histories into readable snippets.
- Implementing clean notification previews by truncating large recipient lists and suppressing duplicate lines in translated output.
How Bilingual Mail Translator Works
- An inbox poller or watcher identifies a new raw email and prepares the initial header data.
- The translation script assembles a specific prompt based on the Openclaw Skills contract defined in this repository.
- A lightweight LLM runtime processes the prompt to generate bilingual text, including subject, date, and body.
- The system applies specific logic to truncate recipients if they exceed three and summarizes any quoted history.
- Duplicate suppression is performed to ensure that if a translation matches the original text, only one version is displayed.
- The final formatted text is delivered to the user interface or notification channel.
Bilingual Mail Translator Setup
To integrate this skill into your environment, ensure your workspace follows the standard Openclaw Skills structure. Follow these steps:
# Navigate to your project root
cd your-mail-agent-project
# Create the required directory structure if it doesn't exist
mkdir -p references scripts
# Reference the contract docs before modification
cat references/contracts.md
# Run a sample translation test
python3 scripts/mail_translate.py --sample raw_email.eml
Bilingual Mail Translator Data Schema & Taxonomy
The skill manages data through a combination of prompt contracts and reference documents. The following structure is utilized:
| File/Component | Purpose |
|---|---|
scripts/mail_translate.py |
Primary execution script for prompt assembly and LLM calling. |
references/contracts.md |
The master document defining the prompt-layer rules and expectations. |
references/commands.md |
Contains generic command checklists for skill maintenance. |
| Prompt Metadata | Includes variables for subject, date, recipient list, and body segments. |
Bilingual Mail Translator Advanced Features
- Smart history summarization using specific section labels like 历史邮件摘要： to maintain context without bloat.
- Automated recipient truncation logic (1, 2, 3...) to keep notification headers concise.
- Intelligent duplicate-line suppression to prevent redundant content when source and target languages align.
- Decoupled post-processing that relies on prompt-level formatting instructions rather than hard-coded heuristics.
- Built-in regression testing workflow for prompt contract assertions to ensure stability during updates.
SKILL.md
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