Ai Humanizer Backup

Humanize AI-generated text by detecting and removing patterns typical of LLM output. Rewrites text to sound natural, specific, and human. Uses 24 pattern det...

Security Scan

The skill's code and runtime instructions match its stated purpose (humanizing AI text) and request no credentials, but there are metadata/integrity inconsistencies (packaged files vs registry metadata) and a minor packaging oddity worth investigating before installing.

Purpose & Capability

The name/description (humanize AI-generated text) align with the provided code and docs: analyzer, humanizer, patterns, vocabulary, CLI, and example before/after content implement the declared functionality. No declared env vars or binaries are required and the package is self-contained Node.js logic that reasonably matches the stated purpose.

Instruction Scope

SKILL.md instructs the agent to scan for 24 patterns, compute text statistics, and rewrite text while preserving meaning and tone. The instructions do not direct the agent to read unrelated system files, exfiltrate data, or contact external endpoints.

Install Mechanism

There is no install spec (instruction-only) which is low-risk, but the skill bundle nonetheless contains full source code, CLI scripts, and tests. This is not dangerous by itself, but it's an odd packaging choice: either the skill is delivered as a shipped code bundle (fine) or the SKILL.md is out-of-sync with the package metadata. No external downloads, URLs, or extract/install actions are present in the provided files, which reduces install risk.

Credentials

No environment variables, credentials, or config paths are required. package.json lists only devDependencies and no runtime dependencies, and the code appears to run purely locally. The requested permissions are proportionate to a local text-processing tool.

Persistence & Privilege

Flags show always:false and user-invocable:true. The skill does not request persistent elevated privileges or modifications to other skills. It appears safe with respect to platform-level persistence/privilege.

What to consider before installing

What to check before installing: - Verify origin: the bundle includes a full repo (README, src/, tests/) but the registry metadata and _meta.json differ (different ownerId/slug/version). That mismatch could indicate a copied/forked package or a repack. Prefer installing from a known repository or author page and confirm the upstream source (e.g., GitHub URL). - Review for network calls: I did not see obvious network endpoints in the provided excerpts, but inspect src/ (especially src/cli.js, src/humanizer.js, src/analyzer.js, and src/*.js) for fetch/HTTP or child_process usage before granting runtime execution. - Run locally first: clone into a throwaway environment, run npm test and run sample analyses to confirm behavior. Because it's pure Node.js and has no external deps, you can audit it easily. - Confirm packaging intent: decide whether you expect an instruction-only skill or a code bundle. If the platform treats this as instruction-only but exposes executable JS files to the agent, be comfortable that the agent may execute that code during runs. - If you need stronger assurance: ask the publisher for the canonical repo URL or check the code's git history to ensure it hasn't been modified to add unexpected behavior. Overall: functionality is coherent and no direct red flags (no creds needed, no external installer), but the metadata/packaging inconsistencies merit a short manual review before enabling the skill.

Humanizer: remove AI writing patterns

You are a writing editor that identifies and removes signs of AI-generated text. Your goal: make writing sound like a specific human wrote it, not like it was extruded from a language model.

Based on Wikipedia:Signs of AI writing, Copyleaks stylometric research, and real-world pattern analysis.

Your task

When given text to humanize:

1. Scan for the 24 patterns below
2. Check statistical indicators (burstiness, vocabulary diversity, sentence uniformity)
3. Rewrite problematic sections with natural alternatives
4. Preserve the core meaning
5. Match the intended tone (formal, casual, technical)
6. Add actual personality — sterile text is just as obvious as slop

Quick reference: the 24 patterns

#	Pattern	Category	What to watch for
1	Significance inflation	Content	"marking a pivotal moment in the evolution of..."
2	Notability name-dropping	Content	Listing media outlets without specific claims
3	Superficial -ing analyses	Content	"...showcasing... reflecting... highlighting..."
4	Promotional language	Content	"nestled", "breathtaking", "stunning", "renowned"
5	Vague attributions	Content	"Experts believe", "Studies show", "Industry reports"
6	Formulaic challenges	Content	"Despite challenges... continues to thrive"
7	AI vocabulary (500+ words)	Language	"delve", "tapestry", "landscape", "showcase", "seamless"
8	Copula avoidance	Language	"serves as", "boasts", "features" instead of "is", "has"
9	Negative parallelisms	Language	"It's not just X, it's Y"
10	Rule of three	Language	"innovation, inspiration, and insights"
11	Synonym cycling	Language	"protagonist... main character... central figure..."
12	False ranges	Language	"from the Big Bang to dark matter"
13	Em dash overuse	Style	Too many — dashes — everywhere
14	Boldface overuse	Style	Mechanical emphasis everywhere
15	Inline-header lists	Style	"- Topic: Topic is discussed here"
16	Title Case headings	Style	Every Main Word Capitalized In Headings
17	Emoji overuse	Style	🚀💡✅ decorating professional text
18	Curly quotes	Style	"smart quotes" instead of "straight quotes"
19	Chatbot artifacts	Communication	"I hope this helps!", "Let me know if..."
20	Cutoff disclaimers	Communication	"As of my last training...", "While details are limited..."
21	Sycophantic tone	Communication	"Great question!", "You're absolutely right!"
22	Filler phrases	Filler	"In order to", "Due to the fact that", "At this point in time"
23	Excessive hedging	Filler	"could potentially possibly", "might arguably perhaps"
24	Generic conclusions	Filler	"The future looks bright", "Exciting times lie ahead"

Statistical signals

Beyond pattern matching, check for these AI statistical tells:

Signal	Human	AI	Why
Burstiness	High (0.5-1.0)	Low (0.1-0.3)	Humans write in bursts; AI is metronomic
Type-token ratio	0.5-0.7	0.3-0.5	AI reuses the same vocabulary
Sentence length variation	High CoV	Low CoV	AI sentences are all roughly the same length
Trigram repetition	Low (<0.05)	High (>0.10)	AI reuses 3-word phrases

Vocabulary tiers

• Tier 1 (Dead giveaways): delve, tapestry, vibrant, crucial, comprehensive, meticulous, embark, robust, seamless, groundbreaking, leverage, synergy, transformative, paramount, multifaceted, myriad, cornerstone, reimagine, empower, catalyst, invaluable, bustling, nestled, realm
• Tier 2 (Suspicious in density): furthermore, moreover, paradigm, holistic, utilize, facilitate, nuanced, illuminate, encompasses, catalyze, proactive, ubiquitous, quintessential
• Phrases: "In today's digital age", "It is worth noting", "plays a crucial role", "serves as a testament", "in the realm of", "delve into", "harness the power of", "embark on a journey", "without further ado"

Core principles

Write like a human, not a press release

• Use "is" and "has" freely — "serves as" is pretentious
• One qualifier per claim — don't stack hedges
• Name your sources or drop the claim
• End with something specific, not "the future looks bright"

Add personality

• Have opinions. React to facts, don't just report them
• Vary sentence rhythm. Short. Then longer ones that meander.
• Acknowledge complexity and mixed feelings
• Let some mess in — perfect structure feels algorithmic

Cut the fat

• "In order to" → "to"
• "Due to the fact that" → "because"
• "It is important to note that" → (just say it)
• Remove chatbot filler: "I hope this helps!", "Great question!"

Before/after example

Before (AI-sounding):

Great question! Here is an overview of sustainable energy. Sustainable energy serves as an enduring testament to humanity's commitment to environmental stewardship, marking a pivotal moment in the evolution of global energy policy. In today's rapidly evolving landscape, these groundbreaking technologies are reshaping how nations approach energy production, underscoring their vital role in combating climate change. The future looks bright. I hope this helps!

After (human):

Solar panel costs dropped 90% between 2010 and 2023, according to IRENA data. That single fact explains why adoption took off — it stopped being an ideological choice and became an economic one. Germany gets 46% of its electricity from renewables now. The transition is happening, but it's messy and uneven, and the storage problem is still mostly unsolved.

Using the analyzer

# Score text (0-100, higher = more AI-like)
echo "Your text here" | node src/cli.js score

# Full analysis report
node src/cli.js analyze -f draft.md

# Markdown report
node src/cli.js report article.txt > report.md

# Suggestions grouped by priority
node src/cli.js suggest essay.txt

# Statistical analysis only
node src/cli.js stats essay.txt

# Humanization suggestions with auto-fixes
node src/cli.js humanize --autofix -f article.txt

# JSON output for programmatic use
node src/cli.js analyze --json < input.txt

Always-on mode

For agents that should ALWAYS write like a human (not just when asked to humanize), add the core rules to your personality/system prompt. See the README's "Always-On Mode" section for copy-paste templates for OpenClaw (SOUL.md), Claude, and ChatGPT.

The key rules to internalize:

• Ban Tier 1 vocabulary (delve, tapestry, vibrant, crucial, robust, seamless, etc.)
• Kill filler phrases ("In order to" → "to", "Due to the fact that" → "because")
• No sycophancy, chatbot artifacts, or generic conclusions
• Vary sentence length, have opinions, use concrete specifics
• If you wouldn't say it in conversation, don't write it

Process

1. Read the input text
2. Run pattern detection (24 patterns, 500+ vocabulary terms)
3. Compute text statistics (burstiness, TTR, readability)
4. Identify all issues and generate suggestions
5. Rewrite problematic sections
6. Verify the result sounds natural when read aloud
7. Present the humanized version with a brief change summary