Lexaudit

lexaudit

Finds odd, wrong or copied-from-another-language words in your texts, and tells you which pages to fix first.

In plain words

You write or translate articles, courses or documentation. A normal spellchecker shouts about every brand name, abbreviation and long German word, and still misses the words that really hurt: a word copied from another language ("calque"), a real word used with the wrong meaning ("false friend"), or a word someone invented.

lexaudit reads your files, ignores the harmless things, and gives you a short ranked list: "look at this page first." It runs on an ordinary laptop in about a second. No graphics card, no internet, no account.

It only reports. It never changes your texts.

Unknown terms? See the glossary. 0.3.0 prep and publish path: docs/p1-INDEX.md.

• Languages: English (en), German (de), Spanish (es), Polish (pl), Russian (ru), Swedish (sv), Ukrainian (uk)
• Hardware: CPU only
• Author: Vadim Nareyko (github.com/nareyko, Founder at Arviser Sp. z o.o.)
• License: dual MIT package and AGPL-3.0-or-later hosted SaaS (details)

---

Try it in 30 seconds

pipx run lexaudit audit --path . --locale en --check

or: uvx lexaudit audit --path . --locale en --check

Missing spelling dictionaries download automatically into your user cache. No GPU. No config file required. Exit code 1 means confirmed calques or invented words remain.

Optional helpers: lexaudit doctor, lexaudit fetch-dicts, lexaudit init.

Check a file or a whole project

# One file or folder, no settings file needed

uv run lexaudit audit --path ./README.md --locale en

Or create starter settings first, then check everything

uv run lexaudit init uv run lexaudit audit --locale en

Open the report

The reports are in the output folder, one subfolder per language. Start with queue.md:

| score | unique_suspects | path | top_words |

|---|---|---|---| | 12.0 | 2 | docs/en/en-04-defect-known-calques.md | orientir surveyness | | 6.0 | 1 | docs/en/en-05-defect-false-friend.md | denseness | | 1.6 | 2 | docs/en/en-03-clean-unseen-jargon.md | vinylester haulout |

A higher score means "look here first". top_words shows the words behind the score.

Ask why a word was flagged

uv run lexaudit explain orientir --locale en

word: orientir (normalized: orientir, locale: en)

status: calque (tier: confirmed) reason: loan-calque-orientir | detectors: calque, hunspell editorial hint: working rule / ballpark / figures - not orientir

Reproduce our test results

uv run lexaudit audit --config examples/synthetic-demo/lexaudit.config.yaml

Then open tmp/synthetic-demo-out/ /queue.md.

---

How good is it? An honest answer

We built a 42-page test set in all 7 languages: 21 clean technical pages and 21 pages with planted problems. Some problems are known to the rules, some are brand new words the rules have never seen. Details: docs/synthetic-benchmark.md.

What this means:

• Pages with known problems come first in every language. The plain spellchecker puts clean pages first.
• Known-rule problems: 100.0% (26/26). Unseen single-occurrence neologisms (Zipf + char n-gram / affix-glue): 100.0% (7/7). Combined planted problems (honest fraction): 100.0% (33/33).
• With exactly 3 defective pages per locale, Recall@2 is 66.7% when the top-2 slots are both bad (Precision@2 = 100.0%). Precision@3 / Recall@3 are 100.0% (singleton inventeds outrank repeated domain jargon).
• Clean-page false positives: 47.1 unique flagged words per 1000 tokens (36 unique FP / 764 tokens).
• Domain terms that recur on a page (or sit in H1/title) are tagged domain-term-candidate, nearly zero-weighted in ranking, and listed under Suggested glossary additions.

---

Documentation

Start with docs/README.md.

---

More commands

# Re-rank pages, filter by source (docs, academy, model_pack)

uv run lexaudit rank --locale ru --mode weighted --sources=docs

Add every page that contains a known bad word to your to-do list

uv run lexaudit expand-needle --needle orientir --locale all \ --write-queue /abs/path/to/rewrite-queue.md

Optional: ask a language model for a second opinion (your own endpoint)

uv run lexaudit label --locale ru --top-pages 20 --top-words 50 --tiers likely,weak --profile cpu

Compare two runs

uv run lexaudit diff --baseline .../report.json --current .../report.json --out /tmp/diff.json

---

For contributors

Setup and checks:

uv sync --extra dev

uv run ruff check . && uv run ruff format --check . uv run mypy --strict src/ uv run pytest uv run python scripts/crap_report.py uv run mutmut run && uv run python scripts/check_mutation_score.py --min-percent 80 uv build

Test layers: unit and integration tests (branch coverage 80% or more), the 7-language benchmark, property-based fuzzing (Hypothesis), golden regression with a read-only check of your files, JSON Schema contract, complexity gate (CRAP), mutation testing (kill rate 80% or more).

Pictures in docs/images are generated from code: run python scripts/render_diagrams.py (needs Inkscape and Pillow). Never edit the JPG files by hand.

License

Full wording: docs/licensing.md. Adding rules: docs/contributing-rules.md.

Origin and author

Created and maintained by Vadim Nareyko (github.com/nareyko). It grew out of auditing multilingual courses and encyclopedias at FairHelm.