defluff 0.1.1

Deterministic prose linter for LLM-generated text — flags filler phrases, AI buzzwords, hedges, and clichés. No model required. Python API + CI exit codes + RL reward signal.

defluff

The deterministic slop check for AI-generated prose. Point it at a changelog, a doc, or an agent's own output and get back the filler phrases to cut — plus a CI exit code and a pinnable score, identical on every run. No model, no API key.

Every flagged span carries no information, so cutting them loses nothing. Clean text, same tool, passes straight through:

What makes defluff worth installing over a one-off grep is the engine around the list: bring your own phrases, per-project overlays, and an MCP server your agents pick up with no wiring.

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Install

pip install defluff

That's it. No model download. No API key. Runs anywhere Python does.

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Quick start

# Lint a file — exit 1 on slop, 0 when clean
defluff lint essay.md

# Pipe text
cat draft.md | defluff lint

# Get a bare score for scripts (0.0 – 1.0)
defluff score essay.md

# Machine-readable JSON for downstream tooling
defluff lint essay.md --json

---

MCP server

Exposes three tools so any MCP-aware agent can self-check prose without bespoke wiring — including its own draft, before returning it.

pip install "defluff[mcp]"
defluff-mcp

Register in your MCP config:

{
  "mcpServers": {
    "defluff": { "command": "defluff-mcp" }
  }
}

Tool	Args	Returns
slop_detect	text: str	slop_score, spans (text, category, weight, offsets), categories, lexicon_version
slop_add	pattern: str, category: str, scope: "user"|"project"	adds a phrase to the lexicon overlay
slop_ignore	pattern: str, scope: "user"|"project"	suppresses a phrase (e.g. domain jargon)

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Common use cases

• Agent self-correction — call slop_detect on a draft and revise the flagged phrases before returning it. Zero wiring, one session, no second model in the loop.
• CI gate on generated content — fail the build when an AI-drafted changelog or doc ships full of "furthermore" and "robust." Deterministic + exit codes + a pinnable lexicon is what an LLM-judge gate can't give you.
• Writing assistant feedback — highlight the exact phrases an editor would cut, instead of a vague "this sounds AI."
• A reward component for fine-tuning — see reward loops for caveats; on its own it's gameable.

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Why defluff?

Every "AI detector" tries to classify whether text was AI-generated — a hard, unreliable problem. defluff asks a different question: does this text contain removable filler? That's deterministic, and it's true whether a human or an LLM wrote "at the end of the day."

proselint is the closest prior art — deterministic, no model — but emits yes/no warnings rather than a tunable density score, and isn't built around a list you swap, overlay, or pin.

A grep over a word list gives you raw hits. defluff gives you what you'd otherwise have to build around that list:

• An MCP server — agents self-check with zero wiring.
• Markdown- and code-aware — strips code fences, inline code, and URLs first.
• Whole-word matching — "foster" won't fire inside "fostering".
• A normalized score instead of a hit count — filler density, so one threshold works on a tweet or a 5,000-word doc.
• Overlap handling — "at the end of the day" overlapping "end of the day" counts each word once (longest-match-wins).
• Exit codes, JSON, and char-offset spans — drop into CI, pre-commit, and editor tooling.
• A pinnable lexicon hash — prove the ruler didn't move between runs.

Bring your own slop

defluff lint draft.md --lexicon team-slop.md

# team-slop.md
- circle back
- low-hanging fruit
- boil the ocean
paradigm shift

One phrase per line (# comments and -/* markers ignored). These layer on top of the built-in defaults and report under a neutral custom category. For real categories and per-phrase weights, use a .json list (see Lexicon overlays).

Ready-made domain packs

defluff lint post.md --pack marketing-growth
defluff lint post.md --pack marketing-growth,ai-llm   # stack several

Pack	Catches	Pack	Catches
corporate-linkedin	office jargon	crypto-web3	crypto hype
startup-vc	pitch-deck speak	pr-press-release	press-release boilerplate
marketing-growth	hype copy	academic	research hedging
ai-llm	LLM tells	wellness-selfhelp	influencer-speak
social-media	X/Twitter engagement-bait

List them with defluff packs. High-false-positive terms (e.g. pivot, detox) ship commented-out so they're inert until you opt in. See the packs README.

Batteries-included defaults

~130 curated patterns across five weighted categories, case-insensitive, whole-word matched:

Category	What it catches	Examples
ai-vocab	Words disproportionately overused by LLMs	delve, tapestry, nuanced, pivotal, robust, showcase
cliche	Hollow idioms that add no information	at the end of the day, move the needle, circle back, game changer
hedge	Empty qualifiers	it should be noted that, needless to say, basically, essentially
corporate	Buzzword inflation	leverage, synergy, actionable insights, cutting-edge, scalable
transition	Filler connectives LLMs reach for by default	furthermore, moreover, in conclusion, first and foremost

Rhetorical patterns (beyond the list)

Some AI tells are sentence shapes, not fixed phrases — the antithesis: it's not X, it's Y, not just a list but a runtime. A regex pattern layer catches these under a rhetoric category:

Mode	What it looks for	Default	Why
Compound (confident)	full shape: it's not X, it's Y · not just X but Y	on	the second clause proves the rhetorical move — rarely a false alarm
Fragment (guessing)	bare X, not Y — e.g. a signal, not a verdict	off, --pack rhetoric	also fires on plain corrections (shipped Tuesday, not Wednesday)

Each match counts as one unit toward the score regardless of length. Turning on fragment mode changes the lexicon hash.

defluff lint draft.md                  # compound antithesis caught by default
defluff lint draft.md --pack rhetoric  # also catch the punchy "X, not Y" fragment
defluff lint draft.md --category rhetoric   # gate CI on antithesis alone

---

Python API

import defluff

report = defluff.detect("It is worth noting that we should leverage synergies.")
print(report.slop_score)   # 0.0 – 1.0
print(report.spans)        # flagged phrase locations + categories

score = defluff.score(text)       # bare float
clean = defluff.is_slop(text)     # bool at default threshold

lex = defluff.load_lexicon()                # pin for reproducible runs
score = defluff.score(text, lexicon=lex)

SlopReport fields:

Field	Type	Notes
slop_score	float	Clamped [0, 1] — use for thresholds
slop_density	float	Raw unclamped — better gradient for reward loops
spans	list[Span]	Per hit: text, category, weight, char offsets into the cleaned text (see Limitations)
categories	dict[str, float]	Per-category density
n_words	int	Token count
low_confidence	bool	True when n_words < 20
lexicon_version	str	SHA prefix of resolved entry set

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Lexicon overlays and versioning

The bundled lexicon is the baseline; layer on top without editing the package:

# "synergy" is slop on this machine
defluff lexicon add "synergy" --category corporate --scope user

# "leverage" is fine in this repo (finance context) — commit this with the repo
defluff lexicon rm "leverage" --scope project
git add .defluff/ignore.json && git commit -m "allow 'leverage' in finance context"

• User overlay (~/.config/defluff/) — machine-wide, not committed
• Project overlay (.defluff/ at git root) — per-repo, commit it for your whole team

Writes are atomic and cross-process locked; a corrupt overlay is warned and skipped — detect() never crashes.

Every resolved lexicon (bundled + overlays + packs) carries a short content hash, printed on every run (lexicon: 2cc05ba84457) and exposed as SlopReport.lexicon_version. Pass lexicon=defluff.load_lexicon() once and every call scores against the same ruler — pinnable for CI baselines and RL rewards, and auditable since the hash changes if and only if the resolved entry set changes. Each release ships a dated lexicon with a changelog (CHANGELOG.md); ai-vocab is expected to turn over release to release, cliche/hedge/corporate/transition are near-stable.

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Reading the output and setting the threshold

1. The spans — the exact filler phrases, with category. Deterministic, reliable. Act on these.
2. The score — slop_score = flagged words ÷ total, weighted by category (ai-vocab counts a little more, transition a little less). 0.20 ≈ "a fifth of this text is listed filler." Usually 0.0–1.0; can edge slightly above on text that's almost nothing but slop. Instead of being a quality quantfier, its just a tripwire that drives the CI exit code.

Pick a threshold based on how filler-dense the text is allowed to be:

--threshold	Meaning	Good for
0.05	~5% filler — strict	marketing copy, landing pages, customer-facing text
0.08 (default)	~8% filler — a tripwire for triage	general prose, blog drafts
0.12–0.20	only flag heavy padding	technical docs that legitimately use robust, scalable, in order to

The default 0.08 is provisional — hand-chosen, not yet calibrated on a labeled corpus (hence the [threshold provisional] tag). For a hard CI gate, set your own threshold and suppress your domain's vocabulary first (below).

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Use in CI

Technical writing — API docs, ADRs, RFCs — legitimately uses words like robust, scalable, in order to. Suppress your project's domain vocabulary first:

defluff lexicon rm "scalable" --scope project
defluff lexicon rm "in order to" --scope project
git add .defluff/ignore.json && git commit -m "defluff: allow domain vocabulary"

Then gate only on the categories you trust, not all five:

- name: Check AI-generated content for slop
  # --category ai-vocab,hedge gates only on the highest-precision categories
  run: cat generated_output.md | defluff lint --category ai-vocab,hedge --threshold 0.1

Exit code 1 fails the step, 0 passes.

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Use with pre-commit

# .pre-commit-config.yaml
repos:
  - repo: local
    hooks:
      - id: defluff
        name: defluff slop check
        entry: defluff lint
        language: system
        types: [markdown]

---

Use in reward loops (experimental)

A deterministic, non-differentiable scalar for filler density can be a small component of a reward mix — but it's gameable alone: a model optimized purely against a fixed phrase list learns to paraphrase the filler rather than remove it. Pair it with a real quality signal (human or LLM judge); we don't yet have a published training run showing it helps.

lex = defluff.load_lexicon()  # pin once
reward = lambda text: -defluff.detect(text, lexicon=lex).slop_density  # unclamped, better gradient

delta = defluff.compare(draft_v1, draft_v2, lexicon=lex)
# {"score_a": 0.31, "score_b": 0.18, "delta": -0.13,
#  "improved": [...], "regressed": [...]}  # set diff of flagged phrases, not a semantic diff

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CLI reference

defluff lint [FILE] [--json] [--threshold FLOAT] [--category CATS] [--lexicon PATH] [--pack NAMES] [--no-project-overlay]
defluff score [FILE] [--pack NAMES] [--no-project-overlay]
defluff packs          # list bundled domain packs

defluff lexicon list   [--category CATEGORY] [--scope SCOPE] [--json]
defluff lexicon add    PATTERN --category CATEGORY [--scope SCOPE] [--weight FLOAT]
defluff lexicon rm     PATTERN [--scope SCOPE]

• --category — comma-separated; only spans in those categories count toward the exit-code decision (still reports all hits). Valid: ai-vocab, cliche, hedge, corporate, transition, custom, rhetoric.
• --lexicon PATH — layers your phrases on top of the defaults. .txt/.md is one phrase per line (lands in custom); .json carries explicit categories and weights.
• --pack NAMES — comma-separated domain packs. rhetoric is reserved for the pattern pack, enabling the opt-in X, not Y antithesis fragment.

Exit codes for defluff lint: 0 = clean · 1 = slop · 2 = bad input.

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Accuracy

defluff is a deterministic matcher, not a trained classifier, so the metric that matters is precision — when it flags something, is it actually removable filler? On a 50-example hand-labeled set (eval/validation.jsonl) spanning clear slop, clean prose, and jargon-as-content traps (e.g. "the robust standard errors", "pivotal trials"), at the default threshold:

Metric	Score	Reading
Precision	1.00	0 false positives — clean prose and legitimate jargon were not flagged
Recall	0.65	bounded by lexicon coverage

Reproduce: python eval/score.py eval/validation.jsonl

Misses are novel buzzwords the lexicon hasn't seen yet (e.g. "operationalize the ideation funnel") — the known limit of a list-based matcher, not noise. Recall on listed filler is 1.00 and will rise as the lexicon grows, but won't reach 1.00 against open-ended novel jargon without a semantic layer.

Caveats: the set is small and labeled by the author — a sanity check on precision, not an independently adjudicated benchmark.

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Limitations

• It matches a known list by design — it doesn't understand text. Novel buzzwords are missed; this is the trade for being deterministic, local, and reproducible (no model, no API key, pinnable hash). Pair with an LLM judge if you need semantic detection of novel filler.
• Domain jargon is contextual. "leverage" in a finance document is real content. Read the flagged spans; suppress false positives with defluff lexicon rm (adds to an ignore list, doesn't delete from the bundled lexicon).
• Span offsets are into the cleaned text. defluff strips code fences, inline code, URLs, and markdown markup before matching, so offsets won't line up with your original document — match on span.text, not raw offsets, against marked-up source.
• custom is read-only-via-file. Phrases from a --lexicon file land in custom, but defluff lexicon add --category custom is rejected — add only takes the five curated categories.
• English only in v0.
• Short texts (< 20 words) get low_confidence: true — the denominator is floored at 20 so one phrase can't read as 100% slop on a two-sentence input.

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Contributing

The easiest contribution is adding a missed filler phrase:

1. Add it to src/defluff/data/lexicon-v1.json with the right category
2. pytest — smoke tests catch boundary errors
3. PR with one or two examples of the phrase in the wild

See CONTRIBUTING.md for code setup and guidelines.

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License

MIT