deslop

name

deslop

description

Use this skill before any prose leaves the terminal for a human reader, and whenever text needs AI tells removed. Covers docs, READMEs, release notes, blog posts, Slack messages, PR bodies, commit bodies, and specs. Activates on mentions of deslop, unslop, humanize, remove AI tells, AI slop, sounds like AI, reads like ChatGPT, em dashes, clean up this prose, make this sound human, slop check, or AI writing patterns.

Deslop

AI tells sit at three levels: characters, words, and shape. The first two are quick to fix and carry less signal. In fiction that gap is measured, with narrative-structure features alone separating model from human text at 93.2% macro-F1 across 61,608 stories (StoryScope). Nobody has run that measurement on a README, so structure-first is a well-supported bet rather than a law, and the syntactic features with the largest verified gaps (participial clauses at 5.3 times the human rate, nominalizations at 2.1) appear in every register.

So the passes below open with character checks, because a script does those for free, then work down through document shape, argument shape, and sentence shape, reaching vocabulary last. The order is the contribution: neither source catalog prescribes one, and a flat list invites a reader to fix twenty easy word hits and stop.

The second thing that decides every rule is the surface. A pass tuned for a blog post will strip the semantic emoji a PR body requires, flatten the calibrated uncertainty that makes a postmortem honest, and spend tokens polishing a brief no human will read. Read the surface row before editing anything.

The job is deleting tells, never improving prose. That distinction is measurable: in one study of human-judged edits, changes classified as style were the only category with a negative quality delta, and fluency-driven rewriting over-corrects badly, trading precision for recall and erasing authorship signal. Every edit here needs a named tell behind it. "This reads better" is not one.

This file gets its own rhetorical, sentential, and epistemic passes. Its tables, dense phrasing, and house jargon are correct for an agent reader and would be wrong in a README, which is what the agent-facing row below means.

Surface first

One canonical matrix. When a surface names an authority skill, that skill decides structure and this one supplies prose patterns.

Surface	Register target	Structure authority	What changes here
PR body, PR comment reply	Reviewer-first, receipts	super-good-pr	Semantic emoji headers and bold section leads stay. Prose patterns apply
PR review report	Principal-engineer prose	hyper-pr-review	Severity markers stay. Prose patterns apply
Commit body	Plain text, factual	implement, git	No markdown at all, wrap at 76. Trailers and URLs exempt
Spec, plan, design doc	Precise, decision-carrying	plan	Full pass. Slop here propagates into everything built from it
Research report	Findings with provenance	research	Full pass. Stitched agent summaries arrive pre-slopped
README, docs, guides	Plain, concrete, second person	this skill	Full pass. Code, CLI output, config samples untouched
Changelog, release notes, migration guide	Version-scoped	this skill	Full pass, but narrating the change is correct here rather than a tell
Slack, email	Warm, complete sentences	this skill	Full pass. Required signature footers stay
Interactive chat with the user	The house register	the project contract	Skip. They watched the work happen and can ask a follow-up
Blog, essay, opinion, personal	The author's voice	the author	Full pass, and stance belongs here. Flat neutrality is its own tell
Encyclopedic, reference, neutral docs	Neutral and plain	the publication	Full pass, no injected stance. Neutral is the correct human register
Scientific, legal, medical, forecasting, postmortem	Calibrated uncertainty	the discipline	Hedges are honest. Cut only hedges that qualify nothing
Marketing copy the user asked for	Persuasive	the brief	Puffery is the genre. Name it once, then respect the brief
Fiction, creative

Per-surface detail and worked artifact-level rewrites are in references/surface-profiles.md.

Two roles, and why they are separate

Detection and rewriting are different jobs, and one agent doing both grades its own work. Split them when the stakes justify it: the detector reports findings and may not rewrite, and the rewriter consumes those findings without re-deriving them. On a subagent host, enforce it with tool permissions rather than instructions, denying the detector Write and Edit.

The evidence is transferred from adjacent tasks rather than measured on prose editing (references/evidence.md): ungrounded self-critique loses to doing nothing on the closest analogue, and a rewriter without a discriminative gate changes 92 to 96% of everything handed to it.

So NO_EDITS is a first-class, non-failing result. A pass that finds nothing worth changing succeeded. Detection is the bottleneck rather than rewriting, so a finding ships as {span, tell id, why, proposed replacement} and any span without a concrete replacement gets dropped. Enumerable tells get a span and a reason; a holistic "still reads generated" judgment is recorded as a judgment, with no reason required, because forcing a model to verbalize an unnameable residual degrades it.

For a short artifact one agent can hold both roles, provided the detection report is written down before any edit starts.

Where the folklore is wrong

Several rules that circulate as deslop advice are unsupported, and applying them makes prose worse. Citations and tiers in references/evidence.md. The correction that matters: on several features the direction reverses between model families, so GPT-4o over-uses hedges while every Llama 3 variant avoids them, and a blanket rule about either is wrong for half the models measured.

Common rule	The problem	Do this instead
"Cut passive voice"	Direction varies by model, and one family runs near the human rate	Fix only passive that hides an actor the reader needs
"Strip hedges"	Direction reverses between families	Cut hedges that qualify nothing, keep calibrated ones
"Avoid contractions, first person"	Under-used by one family, over-used by the other	Judge by register, never by rule
"Ban robust, leverage, crucial"	All three skew human in HC3 log-odds	Off the banned list. Judge in context
"Em dashes prove AI"	Published human essays out-dash frontier models	A house rule plus a density check, not a verdict
"Cut every 'in order to'"	Isolated wordy constructions are a human signal in the Wikipedia corpus	Cut clusters, not instances

Where to aim instead, since these are the largest verified gaps against human rates: present participial clauses at 5.3x, "that" clauses as subject at 2.6x, nominalizations at 2.1x, and phrasal coordination at 1.9x. All four are repaired by the same move, which is giving a real subject a real verb. That single edit is worth more than any wordlist pass.

Lexical markers also rot. Words get called out publicly, writers avoid them, and models keep moving. Treat any wordlist here as dated (Aug 2026) and weight structure higher, because structural tells have survived every generation so far.

The five passes

digraph deslop {
  rankdir=LR;
  node [shape=box, style=rounded];
  m [label="1. Mechanical\n(script)"];
  s [label="2. Structural\n(document shape)"];
  r [label="3. Rhetorical\n(argument shape)"];
  n [label="4. Sentential\n(sentence shape)"];
  l [label="5. Lexical\n(words)"];
  g [label="Exit gate\n(rescan + epistemic audit)", shape=diamond];
  m -> s -> r -> n -> l -> g;
  g -> s [label="one revision, then ship", style=dashed];
}

1. Mechanical. Run the scanner. It masks every protected region (frontmatter, fenced code in all forms, indented code, inline code spans, blockquotes, HTML comments, link and autolink targets) before any check, so it does not report a hit inside code or inside a quoted example.

perl skills/deslop/scripts/slopscan.pl --surface house FILE...
# --surface house (default, our repos) | published | agent | sample
# --show-masked   list the hits masking suppressed
# exit 0 clean · 10 candidates only · 20 hard failures · 30 unreadable input

The masker is a heuristic rather than a CommonMark parser, and the dangerous failure would be over-masking, where visible prose gets blanked and a real tell exits 0. So the scanner runs every check twice, once masked and once raw, and reports what masking suppressed. A file with dashes in its code blocks says so and still passes; a masking bug shows up on that line instead of vanishing. One known gap sits behind it: a fence closed deeper than its list indent still over-masks. Rerun scripts/selftest.sh after any change to the masker, since every one of its cases is a failure it actually had.

Hard failures get fixed. Candidates get judged: the heading check fires only on a file that mixes cases and flags the minority style, a proper noun can still put a heading on the wrong side of it, and a bold lead that ends in a period and then adds new information is fine. On the agent surface the scanner skips structural candidates outright, because that surface exempts structure. The rhythm lines are diagnostics and never a gate: short technical prose legitimately scores low, and the thresholds circulating for these metrics are unvalidated. Vocabulary is inventory at this stage, applied in pass five.

2. Structural. Judge the document with the text out of focus, looking only at its shape: header inflation, bullets where the ideas connect, uniform section and paragraph sizes, reasoning packed into table cells, a summary that restates what was just read.

3. Rhetorical. Read for the argument. Inflated significance, puffery, vague attribution, signposting, generic closes, and the reasoning-era tells (premise stacking, announced counts, the tie-back close, fractal summaries, invented concept labels). Run the topic-string test here: underline the first seven or eight words of every sentence up to the verb, and check that they name a small set of related ideas rather than a new abstraction each time.

4. Sentential. Sentence shape: participial tails (the largest measured signature), nominalizations, "that"-clause subjects, actor-hiding passive, false ranges, clause stacks that force a reread, manufactured punchlines, staccato runs, and the phrasal-coordination imbalance above. Run the verb-list test: list every verb in the paragraph and read the list alone, and if it reads is, serves, has, represents, the actions are hidden in nouns. Then the stress-position test: tap hard on the last three or four words of each sentence and ask whether they deserve the emphasis.

Most of these are one defect wearing different clothes, and one move fixes them: give a real subject a real verb. references/craft-moves.md carries the procedure and the guards that keep it from overshooting.

5. Lexical. Plain words, filler clusters, copula avoidance, adverbs propping up weak verbs, abstract metaphor nouns, house jargon leaking outward. Colons introduce lists and explanations; as a dramatic mid-sentence hinge a colon does the same work as the em dash it replaced.

Epistemic axis, checked at the gate. Truth-level tells cut across all five: cutoff disclaimers, speculative gap-filling, sycophancy, chatbot residue, process theatre, unfalsifiable claims, and fabricated specificity.

Every entry, with before-and-after pairs, is in references/pattern-catalog.md. Do not restate the catalog here.

Carry at most three rules per pass. Instruction-following collapses as simultaneous constraints accumulate: measured satisfaction drops from roughly 85% at one constraint to 62% at five in one benchmark, and from 90% to 25% across one to six in another. That decay, not tidiness, is why this is five passes instead of one checklist.

How many signals convict

This governs judgment candidates only. Mechanical hard failures (a dash on a house surface, provider residue, a curly quote) are deterministic and get fixed on sight, with no convergence needed.

For everything requiring judgment, a single marker proves nothing, and acting on one is how a deslop pass starts damaging good writing.

Independent signals	Report
One	Nothing. No finding, no hedged mention
Two	The reader-facing defect, on its own evidence, without citing markers
Three or more, plus a named defect	A finding that states how many signals it rests on

Independence is by signal family rather than by catalog id, because several ids describe one habit. The families are listed at the end of references/pattern-catalog.md. Two hits inside a family count once, and so do multiple markers inside one sentence.

Evidence per finding

Each finding carries an artifact or it does not get reported.

Test	Question	Artifact required
Deletion	Cut the span. What did the reader lose?	The cut span and the named loss. "Nothing" convicts
Stranger	Could someone who never read the source have written this?	The specific fact only a reader would know
Attribution	Does "studies show" resolve to a source supporting this claim?	The resolved citation, or the finding
Portability	Would this sentence transplant unchanged into another project?	The other project it would fit, or the boilerplate exception

Deletion is the primary test and the other three are specializations of it. Note what deletion does not prove: necessary policy and safety text often has a negation nobody would assert and still has to be there, so a statement being obvious is not grounds for cutting it.

Report a "not flagged, and why" note whenever softer markers were present and deliberately passed over. It is the one restraint control that shows its work.

Swap traps

Removing a tell tends to relocate it. Treat this as a light guard rather than a documented mechanism: displacement has not been measured for prompt-level bans, and the one study that looked found no compensation.

The four that fire most often, with the full table in references/pattern-catalog.md:

You removed	The trap	Do this instead
An em dash	Parenthesis spray, colon as connector, -- substitute	End the sentence. A comma for a tight aside
Rule of three	Rule of four, or three items with new rhythm	Count the real items, then rebuild the list as clauses
Uniform rhythm	Mechanical short-long-short alternation	Vary in clusters. Human sentence runs are persistent
Bold-lead bullets	A heading per former bullet	Prose, or a bold lead ending in a period plus new detail

Voice: match, never invent

Voice comes from a source. In priority order: a writing sample the user provides, the author's existing prose in the same repo or thread, the register the project contract specifies, then the surface default.

A user-provided sample outranks every rule here, including the dash ban. If the sample uses em dashes, keep them at the sample's frequency.

Where the surface allows stance, let the writing carry opinions, mixed feelings, uneven rhythm, asides, and first person when it fits. Stance is voice and it is yours to add. Facts are not: no name, number, date, quote, or citation enters the rewrite unless the source or the user supplied it. Trading a vague claim for an invented specific is the failure that matters most here, because the result reads better and is false.

Preservation contract

Never edit: fenced or inline code, CLI output, frontmatter, link targets, quoted material, titles, proper names, required boilerplate, or a watched phrase that is being discussed rather than used. When a human hands you their own text for editing, edit it surgically and keep what they clearly chose.

Some strings carry semantic or legal weight, so a wording change there is not a style change: RFC 2119 normative keywords (MUST, SHOULD, MAY), regulated safety signal words (DANGER and CAUTION are distinct severity tiers), approved consent and safe-harbor language, and quotations, where the legal test is whether the edit produces a material change in meaning (Masson v. New Yorker, 501 U.S. 496). Straightening a dash inside a quote is defensible; changing "%" to "percent" is not, because a reader hunting the original will fail to find it.

Not tells on their own: clean grammar, formal vocabulary, a single transition word, curly quotes from an auto-curling editor, one em dash, one short emphatic sentence, mixed casual and formal register, unsourced claims, and precise technical vocabulary.

Signs of a human author, which mean edit less: hard-to-fabricate specifics, unresolved mixed feelings, era-bound references, real sentence-length variance, self-interrupting asides, first-person editorial choices the writer can defend, plain copulas, honest superlatives, and text predating late 2022.

Two axes that cannot be traded

Judge the pass on both, qualitatively. Neither is a number, because no validated scale exists for either.

Slop is the confirmed tells that survived. Lifelessness is the opposite failure: prose that reads as anti-AI cosplay, with legitimate longer sentences, precise technical vocabulary, and the author's own habits sanded off. A pass that improves one and wrecks the other has not worked.

The scanner reports an over-correction warning when mean sentence length drops below 10 words with a standard deviation under 4, or when more than 60% of sentences fall under 6 words. Those cutoffs are unvalidated and surface-independent, so treat the warning as a prompt to reread rather than a gate.

The better number is the size of your own edit: (original words - revised words) / original words, where a third to a half is normal and past about 70% you have replaced the prose rather than cleaned it. It measures the edit instead of the text, which is why it catches an over-eager pass. Details in references/craft-moves.md.

The stop rule

One rewrite, one audit, one revision. Then ship, and say which checks still fail and why they were left.

Judgment passes get two rounds. A rule the scanner verifies may loop to about five, stopping on no-diff. The numbers behind the cap are in references/evidence.md; the short version is that most corrections land in the first pass and loops without a deterministic checker do not self-terminate.

When text still reads as generated after that, ask which of these it is before rewriting again: the wrong surface profile, source material with no information in it, a rewrite that relocated tells instead of removing them, or missing voice calibration. Each has a different fix, and only the second is unfixable by editing.

Exit gate

1. The scanner reports zero hard failures for this surface, rerun after the rewrite.
2. Every name, number, date, and citation traces to the source.
3. No claim is unfalsifiable and no conversation residue survived.
4. Nothing inside the preservation contract was touched.
5. The opening does not announce and the close does not send off.
6. Any paragraph that would transplant unchanged into an unrelated project's docs is either specific now, or is boilerplate the reader needs.
7. It reads like the author, or like a person.

Invocation modes

Mode	Trigger	Deliver
Embedded	Another skill or task calls this mid-job	The final text only, no ceremony
File	The user points at a file	Rewrite prose in place, then report what changed in a few lines
Pasted	The user pastes text in	The rewrite plus the tells found
Gate	Pre-ship check on an artifact	Pass or fail against the exit gate, with the fixes
Audit	"Does this read as AI?"	Findings with locations and no rewrite

Default to embedded when another skill invoked this one.

Anti-patterns

Anti-pattern	Fix
Running the vocabulary pass and calling it done	Structure and argument first. Words are pass five
One profile for every surface	Read the surface row. A PR body and an abstract share almost no rules
Acting on a single marker	Convict on converging signals, per the table above
Reporting a finding with no artifact	Run one of the evidence tests, or drop it
Deslopping a brief, prompt, or memory's structure	Structure is exempt there. Prose passes still apply
Replacing a vague claim with an invented specific	Ask for the fact, or write the plain version without it
Trusting your own scan for dashes and curly quotes	Run the script. Reading for them misses them
Rewriting until it feels clean	One revision, then diagnose which of the four causes it is
Stripping hedges where uncertainty is honest	Scientific, legal, medical, and forecasting prose keeps its calibration
Injecting typos or filler to sound human	That is detector cosplay. Write better instead
Flattening a human author's voice	Match the sample. It outranks every rule here

What this skill is NOT

• Not a detector-evasion tool. No injected typos, no artificial filler, no errors added to move a score. Audited commercial humanizers lose to doing nothing on every tier. Never emit an authorship verdict, and never repeat a detector score as though it settled anything: detectors measure lexical sophistication and call it authorship, with false-positive rates on second-language writing that collapse when the same essays are given richer vocabulary. references/evidence.md carries the figures and the limits of the neurodivergent-bias claim, which is one small study and often overstated.
• Not a translator into one house voice. It removes tells and matches the author, whose voice outranks its defaults.
• Not a structure authority for artifacts that own their shape. super-good-pr governs PR bodies and hyper-pr-review governs review reports.
• Not a fact-checker. It audits fabrications it introduced, not claims already in the source.
• Not a substitute for having something to say. Editing cannot add information to an empty draft.

References

• references/pattern-catalog.md carries every tell, grouped by pass, with before-and-after pairs.
• references/craft-moves.md carries the diagnostics that repair them, from the editing tradition, plus the guards that stop a craft pass from overshooting.
• references/surface-profiles.md carries per-surface rules with worked artifact-level rewrites.
• references/evidence.md records what the corpus research supports, with citations and verification tiers, plus the false-positive traps.
• scripts/slopscan.pl is the protection-aware mechanical scanner, and scripts/selftest.sh is its regression gate. Run the gate after any edit to the masker.
• references/fixtures/ holds slop, protected, and clean fixtures for checking the scanner after an edit.

The pattern set synthesizes Wikipedia's Signs of AI writing (WikiProject AI Cleanup), the blader/humanizer skill, Cursor's pstack unslop skill, peer-reviewed corpus work cited in references/evidence.md, and house conventions from super-good-pr and hyper-pr-review.