anti-ai-editor

Original：🇺🇸 English
Translated

Review and revise content to remove AI-sounding patterns. Voice-agnostic editor that detects cliches, passive voice, structural monotony, and meta-commentary. Use when content sounds robotic, needs de-AIing, or voice validation flags synthetic patterns. Use for "edit for AI", "remove AI patterns", "make it sound human", or "de-AI this". Do NOT use for grammar checking, factual editing, or full rewrites. Do NOT use for voice generation (use voice skills instead).

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Sourcenotque/claude-code-toolkit
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NPX Install

npx skill4agent add notque/claude-code-toolkit anti-ai-editor

Tags

Translated version includes tags in frontmatter
anti-ai-editorde-ai-processingcontent-refinementtext-naturalizationdocument-editing

SKILL.md Content

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Anti-AI Editor

Operator Context

This skill operates as an operator for content editing, detecting and removing AI-generated writing patterns. It implements the Targeted Revision architectural pattern -- scan for patterns, propose minimal fixes, preserve meaning -- with Wabi-Sabi Authenticity ensuring human imperfections are features, not bugs.

Hardcoded Behaviors (Always Apply)

  • CLAUDE.md Compliance: Read and follow repository CLAUDE.md before editing
  • Over-Engineering Prevention: Make minimal fixes only. No rewrites, no "while I'm here" improvements
  • Preserve Meaning: NEVER change actual meaning or intent while fixing style
  • Show All Changes: Display before/after for every modification with reason
  • Context Awareness: Some flagged words are appropriate in technical contexts
  • Wabi-Sabi Enforcement: Human imperfections (run-ons, fragments, loose punctuation) are features -- do NOT "fix" them

Default Behaviors (ON unless disabled)

  • Full Preview: Show complete edited content before saving
  • Categorized Reporting: Group issues by type (cliches, passive, structural, meta)
  • Actionable Fixes: Every detected issue includes a specific replacement
  • Frontmatter Skip: Skip YAML frontmatter, code blocks, and inline code
  • Voice Integration: If voice specified, check voice-specific anti-patterns

Optional Behaviors (OFF unless enabled)

  • Auto-Apply: Apply changes without preview confirmation
  • Aggressive Mode: Flag borderline cases (use for marketing content)
  • Stats Only: Report issues without suggesting fixes

What This Skill CAN Do

  • Detect AI cliches and suggest natural replacements
  • Identify passive voice overuse and suggest active alternatives
  • Flag structural issues (monotonous sentence length, list overuse)
  • Remove meta-commentary that adds no value
  • Handle Hugo frontmatter correctly (skip YAML, edit content only)
  • Preserve code blocks and technical terminology
  • Show before/after comparisons for all changes

What This Skill CANNOT Do

  • Rewrite content entirely (use targeted fixes only)
  • Change technical accuracy for stylistic reasons (meaning is sacred)
  • Remove domain-specific jargon that is appropriate in context
  • Fix factual errors (style-only skill, not a fact-checker)
  • Generate new content (use voice skills instead)
  • Polish away authentic imperfections (see Wabi-Sabi)

Instructions

Phase 1: ASSESS

Goal: Read file, identify skip zones, scan for AI patterns.
Step 1: Read and classify the file
Read the target file. Identify file type (blog post, docs, README). Skip frontmatter (YAML between 
---
markers), code blocks, inline code, and blockquotes.
Step 2: Scan for issues by category
CategoryWhat to FindReference
AI Cliches"delve", "leverage", "utilize", "robust"
references/cliche-replacements.md
News AI Tells"worth sitting with", "consequences extend beyond", "that's the kind of", dramatic rhythm
references/detection-patterns.md
Copula Avoidance"serves as a", "boasts a", "features a"
references/detection-patterns.md
Passive Voice"was done by", "has been", "will be"
references/detection-patterns.md
StructuralMonotonous sentence lengths, excessive lists, boldface overuse, dramatic AI rhythm
references/detection-rules.md
Meta-commentary"In this article", "Let me explain", "As we've discussed"
references/cliche-replacements.md
Dangling -ing"highlighting its importance", "underscoring the significance"
references/detection-patterns.md
Puffery/Legacy"testament to", "indelible mark", "enduring legacy"
references/detection-patterns.md
Generic Closers"future looks bright", "continues to evolve"
references/detection-patterns.md
Curly Quotes\u201C \u201D \u2018 \u2019 (ChatGPT-specific)
references/detection-patterns.md
Step 3: Count and classify issues
Record each issue with line number, category, and severity weight:
  • AI Cliche (Tier 1): weight 3
  • News AI Tell (Tier 1-News): weight 3 (pseudo-profound, philosophizing, meta-significance)
  • Copula Avoidance (Tier 1b): weight 3
  • Meta-commentary: weight 2
  • Dangling -ing clause (Tier 2b): weight 2
  • Significance puffery (Tier 2c): weight 2
  • Generic positive conclusion (Tier 2d): weight 2
  • Dramatic AI rhythm (Tier 1-News): weight 2
  • Structural issue: weight 2
  • Fluff phrase: weight 1
  • Passive voice: weight 1
  • Redundant modifier: weight 1
  • Curly quotes (Tier 3b): weight 1
Gate: Issues documented with line numbers and categories. Total severity score calculated. Proceed only when gate passes.

Phase 2: DECIDE

Goal: Determine editing approach based on severity.
Step 1: Choose approach by issue count
Severity ScoreApproach
0-5Report "Content appears natural". Stop.
6-15Apply targeted fixes
16-30Group by paragraph, fix systematically
30+Paragraph-by-paragraph review
Step 2: Prioritize fixes
  1. Structural Issues (affect overall readability)
  2. AI Cliches (most obvious tells)
  3. Meta-commentary (usually removable)
  4. Passive Voice (case-by-case judgment)
Step 3: Wabi-sabi check
Before proposing any fix, ask: "Would removing this imperfection make it sound MORE robotic?" If yes, do NOT flag it. Preserve:
  • Run-on sentences that convey enthusiasm
  • Fragment punches that create rhythm
  • Loose punctuation that matches conversational flow
  • Self-corrections mid-thought ("well, actually...")
Gate: Approach selected. Fixes prioritized. Wabi-sabi exceptions noted. Proceed only when gate passes.

Phase 3: EDIT

Goal: Generate edit report, get confirmation, apply changes.
Step 1: Generate the edit report
=================================================================
 ANTI-AI EDIT: [filename]
=================================================================

 ISSUES FOUND: [total]
   AI Cliches: [count]
   Passive Voice: [count]
   Structural: [count]
   Meta-commentary: [count]

 CHANGES:

 Line [N]:
   - "[original text]"
   + "[replacement text]"
   Reason: [specific explanation]

 [Continue for all changes]

=================================================================
 PREVIEW
=================================================================
[Show complete edited content]

=================================================================
 Apply changes? [Waiting for confirmation]
=================================================================
Step 2: Apply changes after confirmation
Use the Edit tool for each change. Verify each edit applied correctly.
Gate: All changes applied. File re-read to confirm no corruption. Proceed only when gate passes.

Phase 4: VERIFY

Goal: Confirm edits preserved meaning and improved naturalness.
Step 1: Re-read edited file completely
Step 2: Verify no meaning was lost or changed
Step 3: Verify no new AI patterns were introduced by edits
Step 4: Confirm frontmatter and code blocks are untouched
Step 5: Report final summary
markdown
## Edit Summary
File: [path]
Issues Found: [count]
Issues Fixed: [count]
Issues Skipped: [count with reasons]
Meaning Preserved: Yes/No
Gate: All verification steps pass. Edit is complete.

Examples

Example 1: Blog Post (Heavy Editing)

User says: "De-AI this blog post" Actions:
  1. Read file, skip frontmatter, scan all categories (ASSESS)
  2. Score 22 -- systematic paragraph-by-paragraph approach (DECIDE)
  3. Generate report with 10 changes, show preview, apply after confirmation (EDIT)
  4. Re-read, verify meaning preserved, no new AI patterns (VERIFY) Result: 67% shorter intro, all AI cliches removed, voice preserved

Example 2: Technical Docs (Light Editing)

User says: "Check this for AI patterns" Actions:
  1. Read file, identify technical context, scan for patterns (ASSESS)
  2. Score 7 -- targeted fixes only, preserve technical terms (DECIDE)
  3. Replace "utilizes" with "uses", remove throat-clearing, show preview (EDIT)
  4. Verify technical accuracy unchanged (VERIFY) Result: Clearer prose, same information, technical terms untouched

Error Handling

Error: "File Not Found"

Cause: Path incorrect or file does not exist Solution:
  1. Verify path with 
    ls -la [path]
  2. Use glob pattern to search: 
    Glob **/*.md
  3. Confirm correct working directory

Error: "No Issues Found"

Cause: Content is already natural, or scanner missed patterns Solution:
  1. Report "Content appears natural -- no AI patterns detected"
  2. Show sentence length statistics for manual verification
  3. Check structural patterns (monotony, list overuse) even if no word-level flags

Error: "Frontmatter Corrupted After Edit"

Cause: Edit tool matched content inside YAML frontmatter Solution:
  1. Fall back to treating entire file as content
  2. Re-read file to verify YAML integrity
  3. If corrupted, restore from git: 
    git checkout -- [file]

Anti-Patterns

Anti-Pattern 1: Changing Meaning While Fixing Style

What it looks like: Removing "edge cases" from "This solution robustly handles edge cases" -- losing meaningful technical information Why wrong: Style edits must never change what the content says Do instead: "This solution handles edge cases reliably" -- fix style, keep meaning

Anti-Pattern 2: Over-Correcting Natural Informal Language

What it looks like: Removing "So basically" from a casual blog post because it sounds informal Why wrong: "So basically" is natural spoken rhythm. Blog posts can be conversational. Do instead: Leave natural voice markers alone. Only remove AI-generated patterns.

Anti-Pattern 3: Ignoring Technical Context

What it looks like: Flagging "leverage" in "Use a lever to leverage mechanical advantage" Why wrong: "Leverage" is technically correct when discussing actual mechanics Do instead: Only flag words when used as corporate-speak, not in their literal or technical sense

Anti-Pattern 4: Wholesale Rewrites Instead of Targeted Edits

What it looks like: Completely rewriting a paragraph instead of fixing specific patterns Why wrong: Loses author voice, may introduce new AI patterns, harder to review Do instead: Make the minimum changes needed. Multiple small edits beat one big rewrite.

Anti-Pattern 5: Reporting Without Actionable Fixes

What it looks like: "Line 15: Contains AI-sounding language" with no specific fix Why wrong: Useless feedback -- the user needs to know WHAT to change and HOW Do instead: Show exact original text, exact replacement, and reason for the change

References

This skill uses these shared patterns:
  • Anti-Rationalization - Prevents shortcut rationalizations
  • Verification Checklist - Pre-completion checks
  • Wabi-Sabi Authenticity - Preserves human imperfections

Domain-Specific Anti-Rationalization

RationalizationWhy It's WrongRequired Action
"It's just a style word, keep it"AI cliches are the most obvious tellsCheck against cliche list, replace if matched
"Fixing this would lose the flow"Flow from AI patterns is synthetic flowRemove and let natural rhythm emerge
"Technical content needs formal language"Formal does not mean AI-soundingKeep technical terms, remove corporate-speak
"The author probably wrote it that way"If 5+ AI patterns cluster, it's generatedApply systematic editing regardless
"Minor issues, not worth fixing"Minor issues accumulate into AI tellsFix all detected patterns

Reference Files

  • ${CLAUDE_SKILL_DIR}/references/cliche-replacements.md
    : Complete list of 80+ AI phrases with replacements
  • ${CLAUDE_SKILL_DIR}/references/detection-patterns.md
    : Regex patterns for automated detection
  • ${CLAUDE_SKILL_DIR}/references/detection-rules.md
    : Inline detection rules and structural checks
  • ${CLAUDE_SKILL_DIR}/references/examples.md
    : Before/after examples from real edits