A local detector for AI-writing patterns. It scores every prose file your agent saves and asks for the flagged spans to be fixed. Python standard library only, no network, no model. Prose only, never code.
Install · What it does · Measured · Limits · Patterns · Ethics
[!IMPORTANT] This is a command-line tool and an agent plugin. There is no website and no hosted version. Nothing you write is uploaded, there is no account, and no text ever leaves your machine. Any site offering a service under this name is unrelated to this project.
Install
Paste into Claude Code, Codex, Cursor, or any coding agent:
Install the sloptrim plugin from https://github.com/seyedehsanhadi/sloptrim
Restart, then run /sloptrim doctor. It answers with four [OK] lines.
Explicit commands, and installing without the marketplace
/plugin marketplace add seyedehsanhadi/sloptrim
/plugin install sloptrim@sloptrim
git clone https://github.com/seyedehsanhadi/sloptrim.git ~/.claude/skills/sloptrim
mkdir -p ~/.claude/commands
cp ~/.claude/skills/sloptrim/install/sloptrim-command.md ~/.claude/commands/sloptrim.md
Do not skip the mkdir. On a fresh machine ~/.claude/commands does not exist yet
and the copy fails with "No such file or directory". In PowerShell the last two lines
are New-Item -ItemType Directory -Force $HOME/.claude/commands and Copy-Item.
The copy puts /sloptrim in the / menu, because Claude Code does not scan a skill
folder's own commands/. A marketplace install needs no such step. Either way the
router also answers to /sloptrim:sloptrim.
What it does
The score is 0-100 against 71 documented patterns. 62 of them have a detector; the other 9 need a reading and are worked during the rewrite. Of the 62, 50 can move the score and 12 are reported as writing advice and count for nothing: most of them because measurement showed they mark formal register rather than machine authorship, the rest because they are typographic habits.
| Formats | 20, including .docx, .pptx, .xlsx, OpenDocument, .epub, .ipynb, LaTeX |
| Runs in | Claude Code, on save. Other agents via /sloptrim init, which writes the contract to AGENTS.md, and .cursor/rules/ |
| Needs | Node for the hooks, Python 3.9 or newer for the detector, nothing else |
| Suite | 99 Python tests and 67 hook checks, green in CI on Linux, Windows and macOS, against Python 3.9 and 3.13 (macOS on 3.13) |
| Does not see | A file written by a Bash command, which reaches disk without passing Write or Edit |
| Command | Effect |
|---|---|
/sloptrim full | Contract + guard (default) |
/sloptrim strict | Flags at 20 instead of 40, and asks for a character scrub |
/sloptrim lite / off | Contract only / nothing |
/sloptrim check <file> | Score a file, name the tells, no rewrite |
/sloptrim init | Write the contract to ./AGENTS.md and a Cursor rule to ./.cursor/rules/ |
/sloptrim doctor | Diagnose the install |
python scripts/detect.py draft.docx # JSON: patterns, metrics, 0-100 score
A score lands in one of five bands: clean, light tells, mixed, heavy tells,
pervasive tells. The guard nudges above 40, or above 20 in strict mode.
Measured
The figures below were measured against corpora held privately. Neither those corpora nor the harness that read them is in this repository, and nothing here recomputes any of it. They are cited as results, with the corpus named, and cannot be re-derived from what you have cloned.
False positives on human prose, at the default threshold, worst corpus first:
| corpus | n | rate |
|---|---|---|
| American textbooks, 30 titles | 9,333 | 0.85% |
| MAGE human web text | 504 | 0.40% |
| PubMed abstracts, pre-2020 | 529 | 0.00% |
| arXiv abstracts, pre-2021 | 939 | 0.00% |
Detection, ROC-AUC per corpus. The worst one is the headline:
What it cannot do
It cannot tell you whether a current frontier model wrote something. The machine arms above come from GPT-2, GPT-J, OPT, FLAN-T5, MPT, Mistral, Mixtral, GPT-3.5 and GPT-4, nothing newer. A run against a current frontier model measured ROC-AUC 0.551, close to a coin flip.
The writing contract has no measured effect. Twenty documents drafted twice from one brief with the switch toggled, both arms verified from the transcripts: mean change +2.25, bootstrap 95% CI -5.65 to +9.90, sign test p = 0.27. Its banned-word list works; nothing else in it does. That is 20 pairs against the 40 the protocol asks for, so the question is unresolved rather than settled.
It is not an authorship classifier and must not be used as one. A score says something about writing, never about a person. Read ETHICS.md.
License
Apache-2.0 (full text, NOTICE). Cite with CITATION.cff.