slopscore-lint 0.4.2

Transparent AI-slop writing-pattern analysis for essays, blog posts, Markdown, JSON, and websites.

slopscore

A transparent linter for AI-slop writing patterns in essays, blog posts, Markdown, JSON, and websites.

slopscore reads text and returns a 0 to 100 SlopScore measuring the density of formulaic, generic, low-specificity, over-polished writing patterns associated with low-effort LLM output. It reports per-dimension scores and evidence spans (the exact phrases that triggered each finding), so you can see and fix what it flags.

⚠️ What slopscore is NOT

It does not detect whether text was written by AI, and must never be used to accuse a writer. It flags writing patterns in text (not authorship, not authors): patterns common in low-effort or AI-like prose and in plenty of human writing. Use it as a prose linter to nudge toward clearer, more specific writing, not as an AI detector. Authorship detectors are unreliable and biased; slopscore deliberately is not one.

What it is, and what it is not

slopscore detects writing patterns, not authorship. It does not claim a text was written by AI, and it should never be used to accuse a writer. AI-authorship detectors are unreliable on short, edited, translated, and non-native-English text, so slopscore takes a more honest and more useful position:

"This text has a high concentration of generic, formulaic, low-evidence writing patterns."

not

"This was written by AI."

Think of it as a linter for slop, closer to Vale or ruff than to a black-box AI detector. Every point in the score comes from a visible rule with an evidence span.

Install

pip install slopscore-lint            # lean, rule-based core
pip install "slopscore-lint[web]"     # + website extraction (trafilatura)
pip install "slopscore-lint[nlp]"     # + spaCy NER and sentence-transformer embeddings
pip install "slopscore-lint[lang]"    # + non-English language detection
pip install "slopscore-lint[report]"  # + HTML report rendering (Jinja2)
pip install "slopscore-lint[all]"     # everything

Name note: the PyPI package is slopscore-lint (plain slopscore belongs to a different tool). The import stays import slopscore, and the command is slopscore-lint.

Usage

slopscore-lint scan post.md
slopscore-lint scan essay.txt --format json
slopscore-lint scan content.json --json-path "$.article.body"
slopscore-lint scan https://example.com/post        # requires slopscore-lint[web]

Calibrate against your own writing

Instead of asking "does this look like AI?", ask "does this deviate from my usual style in sloppy ways?". Build a baseline from a folder of your past writing, then compare new drafts to it:

slopscore-lint calibrate ./my-old-posts --name me
slopscore-lint scan new-post.md --baseline me     # reports per-dimension z-score deviations

Higher-precision syntactic detection (optional)

The default install detects syntactic tells (trailing "-ing" analyses, and so on) with regex. Install the [nlp] extra and the spaCy English model for a higher-precision, lower-false-positive path:

pip install "slopscore-lint[nlp]"
python -m spacy download en_core_web_sm

slopscore auto-upgrades to the spaCy path when the model is present; nothing else changes.

Use it as a linter in CI

slopscore-lint scan ./content --recursive --fail-on high          # exit 1 if any high finding
slopscore-lint scan ./content --recursive --format sarif -o out.sarif   # for GitHub code scanning
slopscore-lint scan post.md --format html -o report.html          # highlighted-span HTML (needs [report])
slopscore-lint scan . --diff origin/main --fail-on medium         # only files changed vs a ref

Exit codes: 0 clean (or below --fail-on), 1 findings at or above the threshold, 2 usage error, 3 a needed extra is missing. A composite GitHub Action (action.yml) scans, uploads SARIF to code scanning, and fails by threshold; a pre-commit hook (.pre-commit-hooks.yaml) is published for pre-commit. SARIF and HTML line numbers for Markdown are relative to the extracted prose (raw-source mapping is a later enhancement).

from slopscore import SlopScorer

scorer = SlopScorer(profile="blog", strictness="conservative")
# the argument below is an example of the slop the tool flags:
report = scorer.scan_text("In today's fast-paced digital landscape, it is crucial to leverage synergy.")
print(report.score.slop_score, report.score.label)
print(report.evidence[:3])

Status

v0.4: linter maturity. slopscore.toml / [tool.slopscore] config with per-rule toggles and severity overrides, inline <!-- slopscore-disable … --> suppression, a findings baseline (--fail-on-new), the implemented unsupported_claims dimension, opt-in --suggest rewrite suggestions (with SARIF fixes), an optional separate authorship-adapter interface (no detector bundled), PyPI packaging, and a docs site.

v0.3: an evaluation framework (slopscore-lint eval: TPR@FPR, PR-AUC, calibration, per-subgroup FPR) and a transparent learned scorer, a sign-constrained, calibrated logistic regression over the 13 dimensions, serialized as auditable JSON and run with pure numpy (--scorer ml). The rule scorer stays the default: under a replace-if-wins gate the learned model must beat it on held-out TPR@1%FPR without regressing subgroup false positives, and on the seed set it does not (it over-flags plain English). See MODEL_CARD.md and DATA_SOURCES.md.

v0.2.1: productionization. console/JSON/Markdown/SARIF/HTML reports, recursive and changed-files (--diff) batch scanning with CI exit codes, a GitHub Action, and a pre-commit hook.

v0.2: detection expansion grounded in Wikipedia's "Signs of AI writing" field guide. Dimensions: lexical markers, formulaic structure, significance inflation, superficial "-ing" analyses, vague or over-attribution, negative parallelism and rule-of-three, copula avoidance, genericity, redundancy, cadence, formatting tells, prompt residue, and a negative human-writing signal. Scoring is conservative by default: a corroboration gate damps weak-alone tells, and scores abstain on short or non-English input. See MODEL_CARD.md for citations and limitations.

License

MIT