Write Clear Japanese Output with ja-text-communication

Where this fits

Your agent has just finished analyzing a stack trace and starts replying in mixed Japanese-English: retrieve した data を filter して…, while also assuming you remember the temp file name it invented ten minutes ago. The user is left to untangle abbreviations and missing context. ja-text-communication is a ruleset the agent loads before any writing task, so every chat reply, report, or doc is introduced, defined, and explained as if the reader is seeing it for the first time.

Why agents benefit

• Reintroduces internal filenames, variables, and log labels at first mention, treating user knowledge as separate from agent memory.
• Chooses standard Japanese technical terms instead of inventing translations or attaching Japanese particles to English words.
• Keeps sentences one idea at a time; no compressed arrow chains or unexplained table cells.
• Prefers specific names over ambiguous pronouns and relative phrases like the earlier step.
• Maintains polite 敬体 consistently, avoiding accidental rough memo tone in final output.

Practical scenarios

Writing a bug report in Japanese

The agent investigated a memory leak. Instead of writing ヒープ使用量が増加、GC に圧力, it defines GC, states the observation, then the causal chain step by step, and mentions the exact log marker it used.

Explaining a design decision

When asked why a document indexing pipeline uses a particular library, the agent introduces the library by role, explains the trade-off in plain Japanese, and avoids semantic search 的な approach.

Producing a weekly progress report

The agent lists completed tasks with specific identifiers and defines any internal experiment labels. It writes in full sentences and avoids noun-stopping, so the report can be forwarded to a non-technical manager without rewriting.

Add it to your agent workflow

Install the skill with:

npx skills add mathbullet/skills --skill ja-text-communication

Then add a rule to your agent’s system prompt: Before writing any Japanese text, consult the ja-text-communication skill. For example, when a user asks for a summary, the agent first loads the skill, then produces the output. You can also wire it into post-processing: check the draft against the skill rules before sending. For more context on this specific skill, see the OpenAgentSkill listing at https://www.openagentskill.com/skills/mathbullet-ja-text-communication.

Compare before adopting

Compare how well the rules match your actual output language and audience. Check the repository’s maintenance freshness: last commit, open issues, and whether the author updates examples. Consider alternatives such as a custom prompt block or a different Japanese style guide. Also test workflow fit: does your agent already have a competing always-be-concise instruction that conflicts with this skill’s explain-before-compressing rule?

Why it is worth tracking

This skill is small, focused, and has 119 stars, which is a decent signal that other developers feel the same pain. Because Japanese output quality is easy to get wrong and hard to review, a skill like this deserves evaluation whenever your agent generates user-facing Japanese text, especially if you are moving from English-only prompts to multilingual output. Track its commit cadence and star growth; if it stays maintained, it is a stronger candidate than a one-off prompt.