A Mechanical Nose for 'AI Smell' in Japanese Business Writing

The Smell Test

There is a particular odor to AI-generated Japanese business prose. It is not grammatical error; the particles are correct, the keigo is passable, and the orthography is clean. The problem is what the project calls AI臭さ — the unmistakable whiff of machine translation and LLM boilerplate. Sentences open with hollow signposts like 重要なのは. They close with timid hedges like と言えるだろう. English syntax wears a Japanese mask: inanimate subjects force transitive verbs, nested modifiers pile up like calques from a foreign grammar, and paragraphs march in lockstep rhythm until the reader’s eyes glaze over.

The insight that animates this repository is simple and borrowed from an influential Japanese blog post on de-AI-ing writing agents: large language models are anosmic. They cannot smell their own machine breath. Ask a model to “write naturally” and it will confidently serve up the same tics, because its training loss never penalized the flatness of a three-paragraph argument or the ritual emptiness of このように. The fix, then, is not more prompting but a split custody arrangement. Detection becomes the job of a deterministic machine. Judgment stays with the agent — or the human behind it.

Detection Is Machine, Judgment Is Human

At the center of the skill sits a linter driven by sudachipy-style morphological analysis. Because Japanese does not delimit words with spaces, tokenization is the prerequisite for any pattern matching. The linter uses it to hunt forbidden phrases, monotonous sentence rhythms, paragraph-level homogeneity, and the specific sin of English-to-Japanese syntactic calque: inanimate subjects married to transitive verbs, or modifiers nested so deeply that the main clause suffocates. These are not spelling mistakes. They are stylistic tells that betray a non-native cognitive architecture — and in this case, the non-native mind is the model itself.

The tool is deliberately not a grammar checker in the mold of Bunpo Check or GoJapanese, which target human learners struggling with particles and conjugation. Nor is it a politeness coach like the keigo guides found in business Japanese primers. It is a forensic layer aimed at the AI. Notably, the linter always exits cleanly; it is not a CI gate that blocks a build. It is a mirror held up to the draft, reflecting a list of suspicions. What to rewrite, what to keep with reasoned justification, and what to ignore — that remains the agent’s call. This separation of concerns avoids the trap of automated “correction” mangling context-specific nuance in, say, a merger memo or a research report.

Prevention Over Correction

The most architecturally interesting choice is front-loaded discipline. The maintainers argue that AI smell seeps into structure — paragraph uniformity, argument pacing, the mechanical repetition of the same rhetorical mold — and that scrubbing it post-hoc usually requires a near-total rewrite. Better to prevent the odor than to perfume it away.

To that end, the skill enforces a twelve-article “writing constitution” as a generation constraint. Lead with conclusions. Headings must carry a message, not merely label a topic. The same cast may not be repeated three times. Before generation begins, the agent locks in the reader, the main message, and a heading skeleton. Document-type templates for minutes, research reports, internal guides, slide decks, essays, and blog posts bake genre expectations into the prompt. The result is a three-phase pipeline — design, constrain, inspect — that treats the agent as a managing editor rather than a stenographer. This displaces the standard “generate then edit” workflow, which tends to preserve the structural DNA of the first draft no matter how heavily it is line-edited.

The Convergence Loop

Post-generation, the skill enters a loop: lint the draft, judge the findings, rewrite, and lint again. A baseline-diff mechanism classifies each finding as resolved, newly introduced, or persistent across iterations. Intermediate files self-destruct; only the converged document remains. The loop acknowledges a hard truth about Japanese prose: some readability sins resist mechanical thresholding. Corpus analysis in the repository confirmed that word-order metrics, comma placement, and subject-verb distance cannot be reliably gated by simple heuristics. Those remain the agent’s visual homework, guided by antipattern catalogs and genre-specific weighting notes.

A recent addition, the reading-load lane, flags five structural burdens — excessive sentence length, buried lists, double negatives, kanji chains, and nested nominalizers — as informational hints rather than errors. For the experimentally inclined, an opt-in semantic script uses sentence embeddings to detect topic flatness, though it is isolated from the main linter because it drags in a gigabyte of transformer weight. The maintainers are frank about where the machine stops and the eyeball begins.

Position in the Field

The repository sits in an awkward, fertile gap. Existing language tools assume a human is holding the pen and needs tutoring. Speak Japanese Naturally and the Comprehensible Japanese channel focus on phonetic and pitch-accent fluency for speakers. Shinka Management’s guides and Real Estate Tokyo’s phrasebooks explain the ritualized choreography of office interaction — the お疲れ様です and お世話になっております that lubricate corporate life. None of them address the problem of a model that has already written the sentence and needs to be told it sounds like a press release translated from a language with no articles.

natural-japanese is part of a broader shift toward Agent Skills, distributed via package managers and plugin marketplaces, that turn generic assistants into culturally specific specialists. It is less a product than a protocol: a declaration that Japanese business rhetoric, with its indirectness, density constraints, and unspoken rules of hierarchy, is too encoded to be left to base-model statistical inference alone.

Limits and Honest Edges

The maintainers do not pretend this is alchemy. The stack is essentially sophisticated glue — inline Python metadata, a morphological analyzer, a bundle of Markdown reference files, and a loop. The semantic module is explicitly experimental. Readability metrics that work for English agglutination fantasies fail for Japanese topic-drop syntax, so the repo simply refuses to automate them. What makes the project notable is not the depth of any single component but the precision of the joins: knowing exactly what to automate, what to constrain, and what to leave for human judgment.

Outlook

As agent frameworks proliferate, this repository offers a template for localized rhetorical engineering. It suggests that the next frontier in AI text generation is not necessarily a larger context window, but finer-grained cultural guardrails — constitutions and lint rules tuned to a specific language’s professional register. The unresolved tension is whether these patterns will generalize beyond the salaryman’s document stack into creative fiction, casual blogging, or spoken-dialogue transcripts. For now, it stands as one of the few open-source projects willing to admit that AI-generated Japanese is often ugly, and that the fix requires not more parameters, but more discipline.