name: humanizer-lint description: Deterministic local prose, copywriting, UI/accessibility markup and code-comment linter. Includes eight language-specific lexical packs and experimental universal rhythm diagnostics via Intl.Segmenter. summary: Local deterministic prose, copywriting, and code-comment linter with 8 lexical anti-cliché packs and universal stylometric rhythm analysis across 100+ languages. topic: agent-workflows allowed-tools: Bash(node:*) version: 1.5.0

Humanizer Lint

Audit prose locally, revise only measured problems, and verify the final text before delivery.

• Run node <skill_dir>/scripts/analyze.mjs --stdin --json for deterministic local analysis.
• Tier 1 covers eight language-specific lexical packs (en, id, es, ms, fr, de, pt, zh), with Indonesian morphology diagnostics. Tier 2 is experimental universal sentence-rhythm segmentation across 100+ languages via Intl.Segmenter; lexical and MATTR thresholds are uncalibrated.
• Follow each finding's surgical suggestedReplacement, splitSuggestion, and fixInstruction without rewriting clean prose.
• Build a local author voice profile with node <skill_dir>/scripts/fingerprint.mjs; samples never leave the machine.

Agent Tool Boundary

Inside an agent workflow, execute only:

node <skill_dir>/scripts/analyze.mjs --stdin --json
node <skill_dir>/scripts/fingerprint.mjs --dir <samples_dir> --out <skill_dir>/profiles/custom-voice.json

Do not call network tools or upload the user's text. npx humanizer-lint is for manual CLI or CI use outside this agent workflow.

For explicit routing, add --domain auto|prose|copywriting|code|ui; use --no-domain-lint to disable domain checks. Without a filename, auto is prose-first; choose a domain explicitly for stdin. Ignore directives in JavaScript/TypeScript with backticks are disabled conservatively and reported in ignoreWarning.

Preserve executable logic, identifiers, commands, structured payloads, legal clauses, quotations and factual numbers. Revise flagged prose, copy, comments, or UI/accessibility markup only when the change is appropriate and does not alter functionality or meaning.

Workflow A — Drafting New Prose

1. Write with concrete nouns, active verbs, specific claims, and transitions that reflect the actual relationship between ideas.
2. Keep facts, quotations, names, numbers, and required terminology unchanged.
3. Avoid stock openings, staged conversational hooks, generic claims, and repeated sentence templates.
4. Before returning the draft, pipe it to node <skill_dir>/scripts/analyze.mjs --stdin --json.
5. Revise only actionable findings, then verify again. Stop after at most three analysis/revision passes.

Workflow B — Auditing and Revising Existing Text

1. Run the analyzer before changing a single word.
2. Inspect findings even if the rhythm gate is SKIP; short inputs can still fail lexical or domain checks.
3. Edit only actionable flagged text. Check sourceScope: match is an exact match; sentence-range and document-excerpt are context, not text to replace mechanically. Preserve unflagged content.
4. Preserve all unflagged prose and factual meaning.
5. Re-run the analyzer. Stop after at most three iterations and report any unresolved diagnostic instead of forcing a rewrite.

Metrics Are Diagnostics, Not Rigid Targets

CV, paragraph CV, adjacent sentence delta ($\Delta_{adj}$), sentence-length ranges, MATTR, and compression ratio identify flat or repetitive patterns. They are not mechanical writing targets. Never manufacture a 3-word sentence followed by a 35-word sentence merely to raise a number; the result must still sound coherent.

Formal, legal, academic, and regulated documents often use lower sentence variance, nominalized terms, or prescribed wording. If further changes would distort meaning or required register, stop editing, return the faithful text, and briefly explain why the statistical diagnostic was overridden. Never invent facts or examples to change a score. When a consistent author voice matters, use a custom --profile rather than forcing casual cadence.

Local Voice Profile

Place at least three human writing samples in one local folder. A combined total of 500 words or more is recommended. Files must be .txt or .md.

node <skill_dir>/scripts/fingerprint.mjs \
  --dir <samples_dir> \
  --out <skill_dir>/profiles/custom-voice.json

Then analyze with:

node <skill_dir>/scripts/analyze.mjs \
  --stdin \
  --profile <skill_dir>/profiles/custom-voice.json \
  --json

Fingerprinting and analysis run locally with zero network uploads.

Indonesian Revision Examples

Before

Tidak dapat dipungkiri bahwa pengimplementasian sistem ini dilakukan untuk meningkatkan efisiensi proses.

After — meaning preserved

Sistem ini diterapkan untuk meningkatkan efisiensi proses.

Before

Dalam rangka memastikan stabilitas, pelaksanaan pemeriksaan dilakukan oleh tim sebelum deployment.

After — meaning preserved

Tim melakukan pemeriksaan sebelum deployment untuk memastikan stabilitas.

Mixed Indonesian–English technical prose is supported. Keep necessary terms such as deployment, latency, cache, middleware, and pull request; Indonesian stopwords and sentence structure determine routing when they dominate the text.

Delivery Gate

Use --delivery-gate for a human-readable four-block report or --json for the same structured data:

1. rhythmAndStylometry — document CV, paragraph CV, adjacent delta, range, and clause symmetry.
2. lexicalAndMorphology — active rule pack, repeated n-grams, stock phrases, nominalization, and passive bloat.
3. domainHygiene — copy/CTA, code comments, and filename-aware UI/accessibility/responsive checks.
4. finalVerdict — score, PASS/FAIL/SKIP, and prioritized one-pass fixes.

humanizer-lint

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

Detect repetitive stock phrasing, uniform sentence rhythm, structural clichés, and voice-profile drift with 8 language-specific lexical and morphological packs plus universal Intl.Segmenter rhythm diagnostics for 100+ languages without sending text to an external service.

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

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 and morphological packs (en, id, es, ms, fr, de, pt, zh) plus universal Intl.Segmenter sentence-rhythm analysis for 100+ 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

Use the command above to install the agent skill. For one-off local linting, run the published humanizer-lint CLI below.

8-language summary: EN — local prose linter. ID — perbaiki prosa dan periksa ritme. ES — corrector de ritmo. MS — semak ayat. FR — analyseur stylométrique. DE — KI-Floskeln entfernen. PT — corretor de estilo. ZH — 句式节奏分析.

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.

The benchmark runs synthetic multilingual and domain fixtures. Its fixture pass rate and runtime do not measure real-world accuracy or authorship. The source corpus is small and non-random; assumed-clean labels are not independently adjudicated.

Try in your browser

Use the official demo: humanizer.aiclub.ai.id.

humanizer-lint.pages.dev remains available as a mirror.

Open a deterministic sample view directly:

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 specific correction instructions. Use it to lint LLM-generated documentation, remove robotic AI phrasing from Markdown, check burstiness and lexical diversity, or add a deterministic prose gate to an agent workflow.

It can serve as a CLI text humanizer guardrail, automated writing style linter, or CI/CD documentation linter. The bundled rules flag overused AI clichés such as delve and tapestry without rewriting the source.

Language support

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 native anti-slop phrases in English, Indonesian, Spanish, Malay, French, German, Portuguese, and Mandarin; Tier 2 applies deterministic stylometry to 100+ languages.

For a closed-loop agent workflow, install npx skills add yanstd888/humanizer-lint, draft without changing facts, run humanizer-lint --lang auto, revise only flagged prose, and re-run before returning the text. It works with Claude Code, Cursor, Windsurf, Cline, Codex, and Gemini CLI without a runtime dependency or network text upload.

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 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.

Run the analyzer:

humanizer-lint --file draft.md --lang en

The current CLI reports FAIL with lexical findings. The input has only three sentences, so the statistical rhythm gate is skipped even though rhythm metrics are displayed:

Status: FAIL
Score: 70
Language: en
Rule pack: en

The score and diagnostics depend on the rules and profile in use.

A failed analysis exits with code 1. A passing fixture returns:

Status: PASS
Score: 100
Language: en

and exits with code 0.

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 after drafting, make only flagged revisions, then check again locally.