OpenCC

Chinese character conversion between Simplified/Traditional and regional variants. Use when Pi needs to convert Chinese text with precision and awareness of...

Security Scan

The skill's files, instructions, and declared dependency (opencc) match its stated purpose of Chinese Simplified/Traditional and regional conversion and do not request credentials or perform suspicious operations.

Purpose & Capability

Name/description, README, SKILL.md, pyproject.toml, scripts/convert.py, and tests all align: the skill wraps the OpenCC conversion library and exposes CLI and Python usage. There are no unrelated environment variables, binaries, or credentials requested.

Instruction Scope

Runtime instructions and the CLI script only read input text (positional argument, stdin, or an input file), perform conversion via opencc.OpenCC(...).convert(...), and write output to stdout or a file. They do not access external endpoints, secrets, or unrelated system files. Note: the skill expects OpenCC JSON mode files (e.g., s2t.json) to be available via the opencc package at runtime — the repo does not include those JSON files itself, so correct behavior depends on the installed opencc package providing them.

Install Mechanism

No registry install spec is provided in the skill metadata, but SKILL.md/README suggest installing via 'uv add opencc' or 'pip install opencc' and pyproject.toml declares dependency 'opencc>=1.2.0'. Installing a PyPI package is expected for this functionality. This is a normal install pattern, but the runtime depends on the authenticity and contents of the opencc package (which may include native components).

Credentials

The skill requests no environment variables, no credentials, and no config paths. All code operates on supplied text/files and uses the opencc library only — there is no disproportionate secret access or credential request.

Persistence & Privilege

The skill does not request always:true, does not modify other skills or global agent settings, and is user-invocable. It does not persist credentials or enable itself automatically in a privileged way.

Assessment

This skill appears coherent and purely focused on Chinese character conversion, but before installing you should: 1) Verify which opencc package you'll install (pip package name and maintainer) and pin a known-good version — malicious or typo-squatted PyPI packages are possible. 2) Confirm the opencc package you install provides the expected JSON mode files (s2t.json, s2tw.json, etc.) or supply them from a trusted OpenCC release. 3) If you require production or high-assurance use, audit the installed opencc package (check for native code, post-install hooks, and network behavior) and run the included tests (pytest test_convert.py). 4) No secrets or network endpoints are present in the skill itself, but keep general supply-chain caution for third-party dependencies.

OpenCC Skill for OpenClaw

Convert Chinese text with precision using OpenCC (Open Chinese Convert).

Quick Start

Basic Conversions

import opencc

# Simplified → Traditional
converter = opencc.OpenCC('s2t.json')
result = converter.convert('汉字')  # → 漢字

# Traditional → Simplified  
converter = opencc.OpenCC('t2s.json')
result = converter.convert('漢字')  # → 汉字

# Simplified → Taiwan Standard (正體)
converter = opencc.OpenCC('s2tw.json')
result = converter.convert('软件')  # → 軟體

# Simplified → Hong Kong variant (繁體)
converter = opencc.OpenCC('s2hk.json')
result = converter.convert('软件')  # → 軟件

Conversion Modes

Config	Source	Target	Use Case
s2t.json	Simplified	Traditional (OpenCC)	Generic traditional conversion
t2s.json	Traditional	Simplified	Generic simplified conversion
s2tw.json	Simplified	Taiwan Standard (正體)	Taiwan-specific terminology
tw2s.json	Taiwan Standard	Simplified	Taiwan to Mainland
s2hk.json	Simplified	Hong Kong (繁體)	Hong Kong variant
hk2s.json	Hong Kong	Simplified	Hong Kong to Mainland
s2twp.json	Simplified	Taiwan + idioms	Taiwan terminology + common phrases
tw2sp.json	Taiwan	Simplified + idioms	Taiwan to Mainland with idioms
t2tw.json	Traditional	Taiwan Standard	Standardize to Taiwan
t2hk.json	Traditional	Hong Kong	Standardize to Hong Kong
hk2t.json	Hong Kong	Traditional	Hong Kong to OpenCC standard
t2jp.json	Traditional	Japanese Kanji (Shinjitai)	Convert to modern Japanese
jp2t.json	Japanese	Traditional Chinese	Japanese to traditional Chinese
tw2t.json	Taiwan Standard	Traditional	Taiwan to OpenCC standard

Use Cases

When to Use OpenCC

1. Blog writing with regional awareness — Ensure blog uses consistent Taiwan/Mainland terminology
2. Content localization — Convert content for different Chinese-speaking regions
3. Character normalization — Standardize variant characters (e.g., 裏 vs 裡)
4. Multi-script content — Convert between Chinese and Japanese Kanji as needed

Region-Specific Guidance

• Mainland China: Use s2t for traditional, t2s for simplified
• Taiwan (正體): Use s2tw for precise Taiwan terminology, s2twp for common phrases
• Hong Kong: Use s2hk for Hong Kong variant
• Japan: Use t2jp for modern Japanese Kanji

CLI Usage

Use the included convert.py script:

# Command line conversion
python scripts/convert.py --source s2t "汉字转换"

# With stdin
echo "汉字转换" | python scripts/convert.py --source s2t

# Save to file
python scripts/convert.py --source s2t --input input.txt --output output.txt

Installation

# Using uv (recommended)
uv add opencc

# Or with pip
pip install opencc

API Reference

See references/opencc_guide.md for:

• Detailed conversion mode descriptions
• Common conversion patterns
• Performance considerations
• Troubleshooting

Characteristics

• Character-level conversion: Precise character-by-character transformation
• Phrase-level conversion: Smart phrase detection for accurate multi-character terms
• Variant handling: Distinguishes between character variants (e.g., 裏/裡)
• Regional awareness: Customized rules for Mainland, Taiwan, Hong Kong, Japan
• No linguistic translation: Converts writing system only, not meaning

Limitations

• Not a translator: Converts writing systems, not languages. Cannot convert between Cantonese/Mandarin or other dialects.
• Phrase-based: Some contexts may require manual review (e.g., proper nouns, technical terms)
• Regional boundaries: Different regions have different standards; always verify output matches your target

Files

• scripts/convert.py — Python CLI wrapper
• references/opencc_guide.md — Detailed mode reference
• pyproject.toml — Python project configuration with uv