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humanizer-lint

Edit stiff AI-shaped prose with a deterministic local prose and rhythm linter

A local, zero-dependency prose linter that flags stiff, AI-sounding writing (stock phrases, flat sentence rhythm, repeated wording) and tells you what to revise. A style checker, not an AI detector.

What it does | Try in your browser | Install | Quick start | CLI reference | Detectors | Agent skill | FAQ

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Summary

• Flags repetitive AI stock phrases, flat sentence rhythm, and rigid translation patterns in blog posts, SEO articles, copy, and technical documentation.
• Reports diagnostic CV, paragraph CV, adjacent rhythm delta, MATTR, n-gram repetition, and one-pass fix hints; these values are indicators, not mechanical style targets.
• Ships 8 language-specific lexical packs (en, id, es, ms, fr, de, pt, zh) and Indonesian morphology checks. Experimental rhythm diagnostics (uncalibrated thresholds) use Intl.Segmenter for other languages.
• Supports local author voice profiling via node <skill_dir>/scripts/fingerprint.mjs and passes the listed skills.sh security audits with zero network text uploads.

npx skills add yanstd888/humanizer-lint

Bahasa Indonesia: Linter prosa lokal tanpa dependency yang menandai tulisan kaku ala AI (frasa klise, ritme kalimat datar, kata berulang) dan menunjukkan bagian yang perlu direvisi. Pemeriksa gaya, bukan detektor AI.

8-language summary: EN — AI humanizer and prose linter for stiff writing and sentence rhythm. ID — humanizer bahasa Indonesia: perbaiki tulisan AI yang kaku dan audit ritme kalimat. ES — humanizador de texto IA y corrector de estilo y ritmo. MS — humanizer teks bahasa Melayu untuk semak dan baiki gaya ayat AI. FR — humaniseur de texte IA et analyseur stylométrique de prose. DE — KI-Schreibstil verbessern und KI-Floskeln entfernen. PT — humanizador de texto IA e corretor de ritmo e estilo. ZH — AI文章润色与人性化，消除AI写作腔并提升句式节奏。

Verified benchmark

Run npm run benchmark to reproduce the 20 synthetic fixtures (16 multilingual and 4 domain checks) and exploratory source corpus on the local machine.

Known limitations

Findings are editorial heuristics, not evidence of authorship. Some valid formal or technical prose may be flagged; review each finding in context. Statistical rhythm checks require eight sentences, and rhythm diagnostics outside the eight lexical packs are experimental. Compression uses a fixed deterministic JavaScript DEFLATE implementation; its ratios can differ substantially from earlier native-compression releases. The 0.64 repetition threshold is exploratory and sample-derived, not independently calibrated: the reference repetition case is 0.5605, and the two human demo samples meeting the 150-word gate score 0.7233 and 0.7485. The other six human demo samples are too short to exercise this gate. The midpoint rounded to 0.64 is not an estimate of population accuracy. The exploratory corpus is small, non-random, and not independently adjudicated, so its results do not estimate real-world accuracy. Agent discovery and end-to-end tool execution have not been fully verified: a Codex sandbox blocked file access and a Claude model request returned HTTP 404; see evals/RESULTS.md in the repository.

Try in your browser

Use the official demo or its mirror. Open a deterministic sample view directly:

| Language | AI Sample (FAIL) | Human Sample (PASS) | |---|---|---| | EN | open | open | | ID | open | open | | ES | open | open | | MS | open | open | | FR | open | open | | DE | open | open | | PT | open | open | | ZH | open | open |

Use ?lang=en, ?lang=id, ?lang=es, ?lang=ms, ?lang=fr, ?lang=de, ?lang=pt, or ?lang=zh to set the playground chrome only for that visit; it does not overwrite a saved browser preference.

It works locally with zero runtime dependencies. Instead of rewriting text through another language model, it reports paragraph- and sentence-level violations with correction instructions. Use it to lint LLM-generated documentation, review robotic Markdown, check burstiness and lexical diversity, or add a prose gate to an agent or CI/CD workflow. The bundled rules flag phrases such as delve and tapestry without rewriting the source.

Editorial search intents

Use this skills.sh humanizer as an AI writing style editor and anti-slop linter to identify passages to revise. It can help you remove LLM clichés and stock phrases or fix robotic AI writing tone. The CLI reports sentence length variation, coefficient of variation (CV), adjacent sentence rhythm delta, and moving-average type-token ratio (MATTR); author voice fingerprinting records local writing samples. Its selected domain checks include a copywriting and CTA linter and an AI code comment linter, not a complete accessibility or code audit.

The CLI can serve as a stylometric analyzer, sentence burstiness checker, and Markdown prose linter; its diagnostics still require editorial judgment.

The repository provides a Claude Code writing skill; users may also use it as a Cursor prose skill, Codex writing skill, Windsurf skill, or Gemini CLI skill, subject to their agent's local execution capabilities.

• ID: Untuk ubah tulisan AI jadi natural dan hilangkan kata klise AI, gunakan editor gaya bahasa dan ritme kalimat untuk audit artikel SEO dan dokumentasi teknis tanpa mengubah fakta.
• MS: Semak ritma ayat dan frasa klise untuk perbaiki gaya penulisan AI yang kaku.
• ES: Un editor de estilo y corrector de ritmo puede ayudar a quitar clichés de escritura IA.
• FR: Cet éditeur de prose et analyseur stylométrique aide à corriger le style robotique.
• DE: KI-Text humanisieren und dabei Satzrhythmus und Stil prüfen.
• PT: Um editor de estilo e corretor de ritmo textual ajuda a remover clichês de IA.
• ZH: 使用句式节奏与词汇多样性分析，检查文本风格，提升文章可读性。

Language support

| Tier | Languages | Checks | |---|---|---| | Tier 1 — rule packs | en, id, es, ms, fr, de, pt, zh | Universal stylometric metrics plus lexical, definitional-opening, and structural-cliché packs; Indonesian keeps its morphology rules. | | Tier 2 — universal | universal or another ISO 639-1 code such as tr, ja, or ar | Experimental rhythm diagnostics through native Intl.Segmenter; lexical and MATTR thresholds are uncalibrated. |

Use cases & agent integration

Use this local AI text humanizer and prose linter for copywriting, blog posts, SEO articles, technical documentation, Markdown proofreading, academic prose, and natural translation polishing. Tier 1 packs flag configured anti-slop phrases in English, Indonesian, Spanish, Malay, French, German, Portuguese, and Mandarin; Tier 2 offers experimental, uncalibrated rhythm diagnostics for other languages.

For a closed-loop agent workflow, install the skill first, draft without changing facts, and run the local node <skill_dir>/scripts/analyze.mjs --stdin --json command before revising flagged prose. The installed analyzer needs no runtime dependency or network text upload. Editor-specific discovery and plugin behavior should be verified in the target agent; they are not guaranteed by the CLI.

What it does

humanizer-lint analyzes prose after removing or neutralizing common non-prose Markdown structures. It then combines lexical rules, sentence-length statistics, structural checks, Indonesian morphology checks, and an optional voice profile into a score from 0 to 100.

The project includes:

• Eight reviewable lexical and structural rule packs for EN, ID, ES, MS, FR, DE, PT, and ZH.
• Sentence-length burstiness, adjacent rhythm delta, short/long sentence ratios, and adaptive MATTR.
• Repetition checks based on paragraph symmetry, repeated sentence starters, and compression ratio.
• Indonesian nominalization, passive-voice, loanword, and translation-style opening checks.
• Optional voice profiles generated from one to five writing samples.
• Human-readable terminal output and pure JSON output.
• Standard exit codes for agents, scripts, and CI jobs.

It does not estimate perplexity, classify who wrote the text, rewrite prose automatically, scan directories, or provide SARIF output.

Terminal demo

Given this input in draft.md:

In today's fast-paced world, teams delve into a robust landscape. Moreover, this pivotal approach fosters seamless collaboration across every project. In conclusion, the solution unlocks valuable insights for everyone.

humanizer-lint --file draft.md --lang en reports FAIL, Score: 70, and exits 1. Its three sentences skip the statistical rhythm gate; lexical checks still run. A passing analysis exits 0. Results vary by rules and profile.

Static SKILL.md Humanizers vs. humanizer-lint (Closed-Loop CLI)

A static writing prompt can ask a model to vary prose, but it cannot verify the finished draft. humanizer-lint takes the smaller, deterministic approach: inspect the exact text