deslop

Deslop: removing the signs of AI writing

LLMs have an identifiable writing style. Left unchecked, AI prose regresses toward the statistical mean: it smooths specific, unusual, verifiable facts into generic, positive, important-sounding filler. The result reads fluent but hollow - "slop." This skill is a field guide to catching and fixing those tells.

How to use this skill

There are two modes:

1. Gate mode (default, silent). When you are writing substantial prose, self-check the draft against the patterns below before presenting it. Don't announce that you're doing this; just produce clean output.
2. Audit mode (explicit). When the user gives you text and asks you to de-slop it, critique it, or check whether it sounds AI-written, scan against every category, then either (a) return an edited version, or (b) return a findings list with specific quoted offenders and fixes - match whatever the user asked for.

For a full audit of an external file, read references/full-checklist.md for the exhaustive pattern list with examples. The summary below covers the high-frequency offenders that catch ~90% of slop.

Critical mindset

• The patterns are signals, not crimes. Humans write some of these too (blogs, editorials, press releases). The presence of one phrase doesn't condemn a text; a cluster of them is the tell. Don't mechanically purge every "however."
• Fixing the surface tic is not the goal - fixing the underlying emptiness is. Deleting the word "underscores" while leaving a sentence that says nothing just makes the slop harder to detect. If a sentence only puffs up significance and carries no fact, cut the whole sentence, don't reword it.
• Specificity is the antidote. The core failure of slop is vagueness masquerading as importance. Replace "a revolutionary titan of industry" with "inventor of the first train-coupling device." When you can't add a real fact, delete the claim.

The high-frequency tells

1. Puffery: undue emphasis on significance and legacy

AI inflates importance by asserting that the subject represents some broader trend or leaves a lasting mark - even for mundane subjects.

Watch words: stands/serves as, is a testament/reminder, plays a vital/significant/crucial/pivotal/key role, underscores/highlights its importance, reflects broader, symbolizing its enduring/lasting, contributing to the, setting the stage for, marking a shift, key turning point, evolving landscape, focal point, indelible mark, deeply rooted, rich cultural heritage.

Fix: Delete the significance claim, or replace it with the specific fact that would justify it. "The 1989 founding marked a pivotal moment in the evolution of regional statistics" → "It was founded in 1989." If there's a real reason it mattered, state that reason concretely.

2. Superficial analysis tacked onto sentence ends

A trailing present-participle ("-ing") phrase that editorializes about significance, impact, or implication - often a synthesis the sources don't support.

Watch words: highlighting/underscoring/emphasizing…, ensuring…, reflecting/symbolizing…, contributing to…, fostering…, cultivating…, encompassing…, valuable insights, aligning/resonating with…

"Douera enjoys close proximity to the capital, further enhancing its significance as a dynamic hub of activity and culture."

Fix: Amputate the trailing clause. The factual half of the sentence usually stands fine alone.

3. The rule of three

Compulsive grouping in threes: tricolon adjectives ("significant, sustained, and verifiable"), three parallel clauses, three examples where two or four would be natural.

Fix: Break the pattern. Use one strong adjective, or a different count. Vary sentence rhythm so the triads don't drumbeat.

4. "Not X, but Y" / "Not only X, but also Y"

A signature rhetorical frame used to manufacture profundity.

"This dispersal is not mere decoration but a deliberate becoming."

Fix: State Y directly. Drop the contrived contrast unless the X is a real misconception worth correcting.

5. Canned emphasis on notability and sourcing

Hammering that a subject is notable by listing what kinds of outlets covered it, echoing sourcing-guideline language ("independent coverage," "national media outlets," "profiled in," "maintains an active social media presence").

Fix: In normal prose, just state the fact and cite it once. Don't narrate the evidence about the evidence.

6. Filler vocabulary (high-density AI diction)

Overused across LLM output: delve, tapestry, testament, realm, navigate (the landscape), boasts, robust, nuanced, multifaceted, intricate, pivotal, crucial, vital, foster, underscore, garner, showcase, leverage, seamless, holistic, comprehensive, rich (history/heritage), align with, resonate, vibrant, stark, meticulous, ever-evolving.

Fix: Swap for plain words or cut. "Delve into" → "look at" / "examine" / cut. "A rich tapestry of" → just name the things. "Robust framework" → say what it actually does.

7. Title Case in headings + overuse of boldface

AI capitalizes Every Main Word in section headings and scatters bold mid-sentence for emphasis.

Fix: Use sentence case for headings unless the house style says otherwise. Reserve bold for genuine UI labels or defined terms, not for emphasis on ordinary phrases.

8. Em-dash overuse and curly quotes

Heavy reliance on em dashes for dramatic asides, and "smart"/directional quotation marks where the surrounding document uses straight ones (a copy-paste tell).

Fix: Vary punctuation - commas, periods, parentheses. Match the document's existing quote style.

9. Outline-like / promotional conclusions

A wrap-up paragraph that restates significance ("In conclusion, X stands as a testament…"), or a "Challenges and Future Directions" section grafted onto something that didn't need one.

Fix: End on the last real fact. Most factual writing needs no peroration.

10. Collaborative-chatbot leakage

Text addressed to a user rather than a reader: "I hope this helps!", "Certainly! Here's…", "Would you like me to…", "As an AI…", "Let me know if you'd like me to expand." Also knowledge-cutoff disclaimers ("As of my last update…") and self-references.

Fix: Strip every trace of the chat frame. The deliverable is the prose, not a message about the prose.

11. Markdown bleeding into the wrong format

**bold**, ## headers, or * bullets appearing in a context that doesn't use Markdown (wikitext, plain email, a CMS field). A dead giveaway of pasted AI output.

Fix: Convert to the target format's actual markup, or remove.

12. Fabricated or broken citations

AI invents plausible-looking sources, dead URLs, fake DOIs, or attributes claims to named people/outlets that never said them ("Roger Ebert highlighted the lasting influence…").

Fix: Verify every citation actually exists and supports the claim. Never let an unverifiable reference through. If you can't confirm a source, remove the claim or flag it explicitly.

13. Gratuitous cross-references

Naming a sibling skill, command, or concept as analogy or aside when the reader doesn't need to understand it to follow the instructions. The reference adds comprehension cost ("what's marathon - do I need to read that first?") with no behavioural payoff; the sentence would instruct identically without it.

"This dispersal works exactly as marathon composes pr-review-merge."

Distinct from a load-bearing composition pointer the reader must actually follow ("composes skill-forge's A/B equivalence capability") - that one is legitimate, don't flag it.

Fix: Cut the analogy. If the reader genuinely needs the referenced skill, make it a declared dependency, not a passing mention. This is prose-level judgment only - it catches decorative name-drops, not whether a document's real composition graph is correct.

Output formats

When editing: Return the cleaned text. If the user wants to see what changed, follow with a short bullet list of the categories you hit and why - quote the worst offenders.

When auditing without editing: Produce a findings list. For each issue: the quoted phrase, the category number above, and a one-line fix. Close with an overall verdict (e.g., "heavy slop — puffery and rule-of-three throughout" vs. "mostly clean, two trailing-participle clauses").

Always: Prioritize the underlying emptiness over surface tics. If removing the slop would gut the text down to nothing, that's the real finding - say so. The fix for a paragraph that only asserts importance is to get a real fact or delete it, not to reword the puffery.

For the complete pattern catalog (including vague attributions, "elegant variation," letter-like talk-page writing, emoji-as-formatting, section-title-in-plaintext, prompt-refusal artifacts, and date-handling tells), see references/full-checklist.md.

Provenance and freshness

Derived from Wikipedia's "Signs of AI writing" (Wikipedia:Signs_of_AI_writing) as captured on 29 May 2026. The tells drift as models change - diction that marked one model generation reads clean in the next, and new tics appear. Treat this snapshot as a point-in-time field guide, not a permanent one. If this skill has not been updated in a while, strongly prefer re-deriving it: pull the live Wikipedia page, diff it against this version, and refresh the patterns before relying on the output. A stale slop-detector is worse than none, because it gives false confidence while missing the current generation's tells.