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Deslop

Skill dy/skills/skills/deslop

Agent skills: artbureau (Lebedev/Gorbunov design & text review), marketing (verified direct-response canon)

Install
npx -y skills add dy/skills --skill deslop

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

3 things to look at

  • 26 days oldThe repository was created 26 days ago. New is not bad, but a brand new repository carrying a familiar-sounding name is the shape a typosquat arrives in, and there has been no time for anyone else to find a problem with it.
  • no licenseNo license file was found in the repository. Code published without one is not open source by default, so using it at work is a question for whoever answers licensing questions where you are.
  • 0 stars0 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Remove AI slop from prose and code — the merged canon of the field's most effective deslop/humanizer skills (blader/humanizer, stop-slop, fuck-slop, no-ai-slop, humanize-writing, Rossmann's rules, story-deslop, cursor/deslop and others), deduplicated and reworked. Use when the user wants to deslop, humanize, de-AI, or naturalize text; remove AI patterns, tells, or "AI smell" from writing; check whether text reads as AI-generated; or clean AI slop from a code diff. For UI/design slop use the hallmark skill instead.

SKILL.md

8.0 KB, as published. Nobody here has run it

Deslop

Slop is the absence of a decision — the statistically likeliest filler standing where a choice should have been made. That is the test for every finding: was this chosen? The same construction, picked deliberately in the author's voice, is not slop. The goal is never "clean" text; it is text where every word survived a decision.

Two facts shape the method:

  1. You cannot see your own slop. Slop patterns are artifacts of preference tuning, not vocabulary mistakes — the model that produces them rates them as good writing on re-read. Verification is therefore mechanical (re-scan with the checks in references/checks.md), never impressionistic.
  2. Overcorrection is also slop. Scrubbed-flat prose, fake typos, forced slang, and mechanical long/short sentence alternation are their own recognizable genre. Voice is protected before anything is subtracted.

Modes

ModeTriggerWhat happens
Edit (default)text to deslop/humanizeFull process below; rewrite delivered with a short change note
Detect"is this AI?", "find the tells"Name patterns, quote the lines. Never output an "AI likelihood" score — named patterns are evidence, detectors are guesses. Do not edit.
Codea diff or "deslop this code"Code checklist below, diff-scoped
UIpages, components, stylingRoute to the hallmark skill — do not improvise UI rules here

Principles

  1. Protect voice first. Before editing, list 3–5 voice signals to preserve (vocabulary level, cadence, bluntness, humor, uncertainty style, digressions, punctuation habits). A user-supplied writing sample overrides every default in this skill, including the dash budget. See references/voice.md.
  2. Delete first, rewrite second. Most slop guards nothing — cut it. Rewrite only what carries meaning. Cap total deletion at ~25% of the piece; past that you are gutting content, not removing slop.
  3. Rewrite by meaning, never by paraphrasing the pattern. Ask what the sentence is for, then say that plainly. A paraphrase that preserves the move ("the real X is Y" for "not X but Y") is a new finding, not a fix — the banned escape hatches are listed in references/structures.md.
  4. Fake-profound lines are deleted, not re-metaphored. Replacing one purple kicker with a better metaphor reproduces the disease.
  5. Never invent facts. Specificity comes from the source or from the author. Where a concrete detail is missing, leave [ADD: which study? what year?] — never a plausible-sounding fill. Before delivering, answer explicitly: does the rewrite state any fact, name, number, date, or citation not present in the source?
  6. One strong finding beats three weak ones. Bold + scare quotes + a dash aside on the same phrase is one stacked tell, not three. Consolidate before reporting.
  7. Every rule carries its exemption. House-style Title Case, genuine tricolons, term-definition bullets in docs, a single "however" — the exemption columns in the reference files are as binding as the rules.
  8. Match the register. Reddit, academic, docs, and marketing have different fatal tells and different legitimate structures — check the register guide in references/voice.md before flagging.

Process (Edit mode)

  1. Read whole, capture voice. Read the full piece once. Note register + audience. Write down the 3–5 protected voice signals.
  2. Scan. Sweep against references/patterns.md (surface tells), references/structures.md (structural moves), and the mechanical checks in references/checks.md. Collect findings with quotes.
  3. Triage. Apply exemptions; consolidate stacked tells; drop anything that is a defensible authorial choice or a protected voice signal.
  4. Rewrite. Delete-first. Per finding: what is this sentence for? Say that, in the author's voice. No escape-hatch paraphrases.
  5. Verify mechanically. Re-run the scan against your own output. Any hit is a new finding. Loop at most 3 passes; if a passage still fires, rewrite it from the bare claim in plain declarative sentences.
  6. Fact check. Answer the invented-facts question from Principle 5.
  7. Deliver. Final text plus a change note of at most 8 lines, grouped by pattern. If the note needs more than 8 lines, you changed too much — reconsider.

Quick checks (30 seconds, before delivering)

  • Any em/en dash? Budget: 0 in short copy, ≤2 in a long piece — and no swapping them for parentheses; restructure the sentence. (Voice sample overrides.)
  • Any "not X but Y" — or any of its escape-hatch paraphrases?
  • Any inanimate noun performing a human action ("the data tells us")?
  • Three items where two would do? Three sentences of the same length in a row?
  • Any sentence that sounds like a pull-quote? Delete it.
  • Opener that clears its throat ("Here's the thing")? Ending that recaps or reassures?
  • Any bold mid-prose, emoji in headings, **Label:** restatement bullets?
  • Any fact, number, or name the source doesn't contain?
  • Would the author actually say this sentence out loud?

Code mode

Scope: the diff (git diff main or this session's changes), not the whole repo. Keep behavior unchanged; minimal focused edits; match the file's existing style. Remove:

  • Comments a human wouldn't write — narrating the obvious, explaining the change to a reviewer ("// added to handle the new case"), restating the next line. Keep only comments stating what the code cannot: constraints, whys, workarounds.
  • Defensive bloat — try/catch, null checks, and validation abnormal for that area, especially on trusted/already-validated codepaths; generic except Exception / empty catch blocks.
  • Type evasionany casts and assertions used to silence the checker instead of fixing the type.
  • Placeholder namingdata, result, temp, handleData, Manager/Helper suffixes carrying no specificity.
  • Ceremony — zero-information docstrings, single-use wrapper layers, speculative abstraction, dead code, debug leftovers, README boilerplate ("This project aims to..."), emoji in code or docs.
  • Any other style inconsistent with the file.

Run the tests after. Report in 1–3 sentences.

Credits

Merged and reworked from: blader/humanizer (Siqi Chen; the Wikipedia "Signs of AI writing" lineage — also the root of the Zed, Hermes, jpeggdev, softaworks, and humanizer-zh forks), hardikpandya/stop-slop, juliusbrussee fuck-slop, petergyang/no-ai-slop, jpeggdev/humanize-writing, realrossmanngroup/no_ai_slop_writing_rules, nousresearch/hermes-agent humanizer, worldwonderer/oh-story-claudecode story-deslop, cursor/plugins deslop, and brianlovin/agent-config deslop. All doctrine belongs to its authors; this is a transformative merge with attribution.

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.