encoding-guard

Name: encoding-guard
Availability: InStock
Author: muddyrain

Detect mojibake and suspicious text-loss regressions in UTF-8 source files.

1|Updated Feb 28, 2026

Search Tags:#code-quality #encoding #multilingual #mojibake #unicode #text-editing #text-loss

Ready to run

npx skills add https://github.com/muddyrain/valley-mas --skill encoding-guard

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: encoding-guard
Source: https://github.com/muddyrain/valley-mas/tree/main/.codex/skills/encoding-guard
Command: npx skills add https://github.com/muddyrain/valley-mas --skill encoding-guard

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This skill helps prevent encoding corruption and silent text loss when editing source files containing non-ASCII text (e.g., Chinese, Japanese, Korean). It guards against Mojibake and unexpected replacement of user-visible text during edits.

Core Features & Use Cases

Seamless detection of mojibake with actionable recovery guidance.
Detection of suspicious text-loss such as repeated '?' in user-visible strings or JSX-like content.
Safe editing workflow guidance, including pre- and post-edit checks and corrective recommendations.

Quick Start

Run this tool before and after edits to detect mojibake and suspicious text-loss with python .codex/skills/encoding-guard/scripts/check_mojibake.py.

Frequently Asked Questions about encoding-guard

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I detect mojibake and text loss in UTF-8 source files?▼

To detect mojibake and text loss in UTF-8 source files, run a Python-based script that parses current changes against HEAD and reports actionable findings regarding encoding corruption and suspicious text replacement.

What causes mojibake in multilingual codebases?▼

Mojibake in multilingual codebases occurs when UTF-8 source files containing non-ASCII text like Chinese, Japanese, or Korean are edited with incorrect encoding settings, leading to silent corruption and unexpected replacement of user-visible strings.

How do I prevent silent text loss when editing non-ASCII source code?▼

Prevent silent text loss by running encoding checks before and after edits to detect suspicious regressions like repeated question marks in user-visible strings or JSX-like content, ensuring safe editing workflow guidance.

Can I check specific Git commits for suspicious text-loss regressions?▼

Yes, you can check specific commits for text-loss regressions by parsing and comparing current changes against the HEAD revision to identify suspicious replacements of user-visible text in multilingual codebases.

Does encoding detection work without installing external dependencies?▼

Yes, encoding detection works without external dependencies because the Skill relies on built-in Python-based scripts to parse files and identify suspicious text-loss regressions and mojibake within your repository.

Why does my source file show repeated question marks after editing?▼

Repeated question marks in source files indicate suspicious text-loss regressions where non-ASCII characters were unexpectedly replaced, which this tool detects by comparing current edits against HEAD to provide recovery guidance.