tool-humanizer

name

tool-humanizer

description

Remove AI-generated writing patterns and restore natural human voice. Detects and fixes 50+ AI tells: inflated symbolism, promotional language, hedging, corporate buzzwords, robotic structure, em dash overuse, rule of three, vague attributions, negative parallelisms, conjunctive phrase abuse. Three modes: quick (obvious patterns), standard (full scan + score), deep (voice-matched using research_context/research-profile.md). Called automatically by sci-communication as a post-processing step for blog posts, social threads, and newsletters. Also invoked standalone: "humanize this", "de-AI this", "make this sound human", "remove AI patterns", "clean up this writing". Does NOT trigger for formal academic writing (sci-writing handles that). Does NOT trigger for content that is intentionally formal or academic.

Humanizer

Strip AI-generated patterns from text. Make it sound like a person wrote it.

Outcome

Input text returned with AI patterns removed, scored before and after on a 0-10 human-ness scale. Change log shows exactly what was fixed. Output either replaces the original or is saved alongside it.

Context Needs

File	Load level	How it shapes this skill
research_context/research-profile.md	full	Replacements use the researcher's actual vocabulary, rhythm, and connectors instead of generic "human" phrasing
research_context/samples.md	tone refs	Reference for what on-voice sounds like
context/learnings.md	## tool-humanizer section	Patterns the user cares about most, false positives to skip

Load if they exist. Works standalone with generic replacements.

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Instructions

Step 1: Detect Mode

Choose from context or ask: "Quick pass, full cleanup, or voice-matched?"

Mode	What happens	Best for
quick	Remove obvious AI cliches + buzzwords. Single pass, no scoring.	Fast social edits, internal docs
standard	Full pattern scan (50+ detections) + human-ness score + change log	Any content going public
deep	Full scan + replace with voice-profile patterns. Loads research_context/research-profile.md	Blog posts, landing pages, emails — anything that must match the researcher's voice

Default: standard. When called by another skill as a post-processing step, use deep if voice-profile exists, standard if not.

Step 2: Load Context

If mode is deep, load research_context/research-profile.md. Extract:

• Preferred vocabulary (use these as replacements)
• Avoided words (flag these as AI tells even if not in pattern library)
• Linguistic habits (connectors, intensifiers, rhythm patterns)
• Samples for tone reference

Read context/learnings.md → ## tool-humanizer for known false positives or priority patterns.

Step 3: Score Original

Rate the input text 0-10 on the human-ness scale:

Score	Meaning
0-3	Obviously AI — multiple cliches, robotic structure, hedging everywhere
4-5	AI-heavy — some human touches but needs major work
6-7	Mixed — could go either way, lacks distinctive voice
8-9	Human-like — natural voice, minimal AI patterns
10	Indistinguishable from a skilled human writer

Scoring factors:

• AI pattern count per 500 words (fewer = better)
• Sentence length variance (higher variance = more human)
• Specificity ratio (concrete terms vs vague qualifiers)
• Structural variety (not every paragraph same shape)

Step 4: Apply Pattern Detection + Removal

Read references/pattern-library.md for the full detection list. Categories:

1. AI cliches & openers — "In today's fast-paced world", "Let's dive in", "It's no secret"
2. Hedging language — "It's important to note", "arguably", "one might argue"
3. Corporate buzzwords — "leverage", "utilize", "facilitate", "optimize"
4. Robotic structure — rhetorical Q+A, obsessive parallelism, always-three lists, "Here are the top X"
5. Overused transitions — "Moreover", "Furthermore", "Additionally", "Nevertheless"
6. Promotional inflation — "transformative", "game-changer", "unprecedented"
7. Wikipedia AI tells — inflated symbolism, em dash overuse, rule of three, vague attributions, negative parallelisms, superficial -ing analyses, excessive conjunctive phrases
8. Vocabulary tells — "delve", "tapestry", "multifaceted", "landscape", "nuanced", "foster", "realm"

Read references/replacement-guide.md for what to replace with.

Step 5: Enhance Human Markers

After removing AI patterns, add natural voice signals:

• Varied sentence rhythm — break up same-length sentences
• Contractions — "it's" not "it is" (unless formal context)
• Active voice — flip passive constructions
• Confident assertions — remove hedging unless genuinely uncertain
• Specific examples — flag vague references for the user to make specific

In deep mode, apply voice-profile patterns:

• Insert preferred connectors and transitions
• Match sentence length patterns from samples
• Use researcher's vocabulary as replacements for generic terms

Step 6: Score Revised + Output

Score the revised text. Show the change summary:

ORIGINAL: 4.2/10
REVISED:  8.4/10

Changes:
  [N] AI cliches removed
  [N] buzzwords replaced
  [N] hedging phrases cut
  [N] structural patterns fixed
  [N] voice markers added

Flags for review:
  [paragraph/line] — [what needs manual attention]

If called standalone, present the cleaned text directly. If called as a post-processing step by another skill, return the cleaned text silently (the calling skill handles output).

Step 7: Log Learnings

If the user gives feedback ("that was too aggressive", "keep the em dashes", "good catch on X"), log to context/learnings.md → ## tool-humanizer.

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Pipeline Mode

When called by another skill (not standalone), this skill:

1. Receives text as input
2. Runs Steps 2-6 silently
3. Returns cleaned text
4. Only shows the score summary if the change was significant (delta > 2 points)

The calling skill is responsible for saving the final output.

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Rules

Updated automatically when the user flags issues. Read before every run.

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Self-Update

If the user flags an issue — too aggressive, missed a pattern, false positive — update the ## Rules section immediately with the correction and today's date. Also log to context/learnings.md under ## tool-humanizer.

Scoring Thresholds

Score	Label	Action
90-100	Clean	No changes needed
70-89	Light	Fix flagged patterns only
50-69	Moderate	Rewrite flagged sentences
0-49	Heavy	Full rewrite recommended