de-slop
Remove LLM-isms and AI writing patterns from text. This skill should be used when editing prose to sound less like AI output — removing overused words, fixing structural tells, and restoring natural human voice. Triggers: "de-slop", "remove AI writing", "humanize this", "sounds too AI", "LLM-isms", "AI slop", or when reviewing text that reads like chatbot output.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/petekp/agent-skills --skill de-slop- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads de-slop automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
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:
- Overcorrection — Did fixes make the text choppy or too informal? Restore where needed.
- Meaning preservation — Does every sentence still say what it originally meant?
- New patterns — Did edits introduce their own repetitive patterns?
- 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."
FAQ
What does the de-slop skill do?
Remove LLM-isms and AI writing patterns from text. This skill should be used when editing prose to sound less like AI output — removing overused words, fixing structural tells, and restoring natural human voice. Triggers: "de-slop", "remove AI writing", "humanize this", "sounds too AI", "LLM-isms", "AI slop", or when reviewing text that reads like chatbot output.
How do I install de-slop?
Run npx skills add https://github.com/petekp/agent-skills --skill de-slop in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/de-slop/ (personal) or .claude/skills/de-slop/ (project).
Which AI agents can use de-slop?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is de-slop safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains de-slop?
It is published by petekp in petekp/agent-skills. It has 41 recorded installs.
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