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de-slop

de-slop

github.com/petekpCreative & media1041 installsUpdated 21w ago
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About de-slop

De-Slop is an editing skill for identifying and revising AI writing patterns, intended for reviewing AI-assisted drafts or text before publication. It diagnoses issues by severity and emphasizes preserving meaning.

Key features

  1. 01Identify three levels of writing patterns
  2. 02Remove stock phrases and grandiose filler
  3. 03Address repeated wording and structural habits
  4. 04Review tone and meaning after editing

Use cases

  1. 01Revise prose with chatbot-like phrasing
  2. 02Review AI-assisted drafts
  3. 03Check writing before publication
  4. 04Rewrite mechanical or overly balanced passages

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/petekp/agent-skills --skill de-slop

After installing, type/de-slopin Claude Code to confirm it loaded.

Bundled filesreferences/structural-patterns.mdreferences/word-list.md

Details

From the project README

De-Slop

Strip AI writing patterns from text to restore natural, human-sounding prose.

Based on Wikipedia: Signs of AI writing and WikiProject AI Cleanup.

When to Use

  • Editing any prose that sounds like chatbot output
  • Reviewing drafts generated with AI assistance
  • Self-check before publishing AI-assisted writing
  • When text feels "off" but the reason is hard to pinpoint

Process

Step 1: Diagnose

Read the full text before changing anything. Load references/word-list.md and references/structural-patterns.md to identify which patterns are present.

Categorize findings into three severity levels:

Red — Immediate tells (fix first)

  • Chatbot leakage ("I hope this helps", "Certainly!", template blanks)
  • Grandiose filler ("stands as a testament", "in today's fast-paced world")
  • Synonym cycling (same entity referred to by 4+ different names)

Yellow — Statistical signals (fix in clusters)

  • 3+ words from the overused word list appearing in close proximity
  • Rule of three used more than twice
  • Tailing participle phrases ("emphasizing the significance of")
  • Em-dash density higher than ~1 per 200 words

Green — Structural patterns (require rewriting, not word swaps)

  • Relentless balance (every section same length)
  • Uniform register (no tonal variation)
  • Generic specificity (hypothetical examples, no real names)
  • Excessive hedging (qualifiers every third sentence)
  • Risk aversion (no specific claims, no edge)

Present the diagnosis as a brief summary before making changes. Example:

Diagnosis: 4 red flags (chatbot leakage, grandiose filler), 7 yellow signals
(word clusters in paragraphs 2, 5, 8), 2 green patterns (relentless balance,
uniform register).
Step 2: Fix Red Flags

Remove or replace all Red items. These are unambiguous AI artifacts.

Chatbot leakage: Delete entirely.

Grandiose filler: Replace with plain statements or delete.

  • "stands as a testament to" -> "shows" or "is"
  • "plays a vital role in shaping" -> "shapes" or "affects"
  • "in today's fast-paced world" -> delete (it never adds meaning)

Synonym cycling: Pick one term and stick with it. Use pronouns for variety.

Step 3: Fix Yellow Signals

Work through clusters. The goal is not to ban specific words but to break up detectable patterns.

Word clusters: Replace overused words with plain alternatives.

  • "delve into" -> "look at" / "examine" / (often just delete)
  • "leverage" -> "use"
  • "robust" -> "strong" / "solid" / (ask: is this adjective needed at all?)
  • "nuanced" -> "detailed" / "complicated" / (often delete)
  • "landscape" -> name the actual domain
  • "multifaceted" -> drop it; describe the actual facets instead
  • "crucial" / "pivotal" / "paramount" -> "important" or delete

Copula avoidance: Restore simple verbs.

  • "serves as" -> "is"
  • "features" / "offers" / "boasts" -> "has"

Transition abuse: Remove mechanical connectives.

  • "Moreover," / "Furthermore," / "In addition," -> start the sentence without them, or use "and" / "also"

Rule of three: Break at least half of them. Use two items, or four, or one.

Tailing participles: Rewrite as separate sentences or delete.

  • "..., emphasizing the importance of X" -> delete, or: "X matters because..."
Step 4: Fix Green Patterns

These require actual rewriting, not substitution.

Relentless balance: Redistribute weight. Expand important sections. Trim or collapse unimportant ones. A 3-sentence paragraph next to a 12-sentence paragraph is fine.

Uniform register: Inject tonal shifts. A blunt short sentence after a complex one. A casual aside in a technical passage. Let the writing breathe.

Generic specificity: Replace hypothetical examples with real ones, or remove examples that add nothing.

Excessive hedging: Remove qualifiers that don't reflect genuine uncertainty. If something is true, state it without "often" / "generally" / "can be."

Risk aversion: Sharpen claims. Add an opinion. Allow an imperfect sentence to stand if it has energy.

Enthusiasm gap: Vary paragraph investment. Spend more words where the writer (or subject) is more interesting.

Step 5: Final Read

Read the entire edited text once more. Check for:

  1. Overcorrection — Did fixes make the text choppy or too informal? Restore where needed.
  2. Meaning preservation — Does every sentence still say what it originally meant?
  3. New patterns — Did edits introduce their own repetitive patterns?
  4. Voice consistency — Does the text sound like one person wrote it?

Principles

  • Prefer plain words. "Use" over "leverage." "Is" over "serves as." "Important" over "crucial."
  • Prefer short sentences. Break long compounds. Not every thought needs a clause.
  • Preserve meaning. Never change what the text says, only how it says it.
  • Don't over-correct. Some em dashes are fine. An occasional "furthermore" is fine. The goal is to break patterns, not ban words.
  • Real > hypothetical. A named example beats "consider a scenario where..."
  • Uneven > balanced. Spend more words on what matters more.
  • Specific > vague. "Response time dropped from 200ms to 50ms" beats "significantly improved performance."

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