# Deslop > Detect and remove the telltale signs of AI-generated 'slop' from any written text - articles, reports, emails, essays, bios, marketing copy, documentation, encyclopedia entries, or anything meant to read as if a thoughtful human wrote it. Apply silently as a quality gate before finalizing substantial prose, and explicitly when asked to clean a draft. TRIGGER when the user says 'make this sound le… - **Type:** Skill - **Install:** `agentstack add skill-bjcoombs-ai-native-toolkit-deslop` - **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior - **Seller:** [bjcoombs](https://agentstack.voostack.com/s/bjcoombs) - **Installs:** 0 - **Category:** [Developer Tools](https://agentstack.voostack.com/c/developer-tools) - **Latest version:** 0.1.0 - **License:** Apache-2.0 - **Upstream author:** [bjcoombs](https://github.com/bjcoombs) - **Source:** https://github.com/bjcoombs/ai-native-toolkit/tree/main/skills/deslop ## Install ```sh agentstack add skill-bjcoombs-ai-native-toolkit-deslop ``` Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent. ## About # 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. ## Source & license This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code. - **Author:** [bjcoombs](https://github.com/bjcoombs) - **Source:** [bjcoombs/ai-native-toolkit](https://github.com/bjcoombs/ai-native-toolkit) - **License:** Apache-2.0 Install and usage instructions live in the source repository linked above. ## Pricing - **Free** — Free ## Security capabilities Automated source analysis of v0.1.0 — what this tool can access: - **Network access:** no - **Filesystem access:** no - **Shell / process execution:** no - **Environment & secrets:** no - **Dynamic code execution:** no *"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.* ## Versions - **0.1.0** — security scan: passed — Imported from the upstream source. ## Links - Listing page: https://agentstack.voostack.com/l/skill-bjcoombs-ai-native-toolkit-deslop - Seller: https://agentstack.voostack.com/s/bjcoombs - Browse the marketplace: https://agentstack.voostack.com/browse --- Listed on AgentStack — the marketplace for AI agent skills and MCP servers. 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