humanize — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited humanize (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
Edit text to remove AI-generated patterns and make writing sound natural. Based on Wikipedia's "Signs of AI writing" guide (WikiProject AI Cleanup).
Avoiding AI patterns is half the job. Sterile, voiceless writing is equally obvious.
Signs of soulless writing: uniform sentence length/structure, no opinions, no uncertainty, no first person, no humor or personality, reads like a press release.
Fixes:
Before (clean but soulless):
The experiment produced interesting results. The agents generated 3 million lines of code. Some developers were impressed while others were skeptical. The implications remain unclear.
After (has a pulse):
I genuinely don't know how to feel about this one. 3 million lines of code, generated while the humans presumably slept. Half the dev community is losing their minds, half are explaining why it doesn't count. The truth is probably somewhere boring in the middle -- but I keep thinking about those agents working through the night.
The full pattern catalog with examples is in references/ai-patterns.md. Read it when you need detailed before/after examples or word lists.
| # | Pattern | Key signal |
|---|---|---|
| 1 | Inflated significance | "pivotal moment", "testament to", "broader trends" |
| 2 | Notability emphasis | Listing media outlets without context |
| 3 | Superficial -ing phrases | "highlighting", "showcasing", "reflecting" |
| 4 | Promotional language | "vibrant", "nestled", "groundbreaking", "breathtaking" |
| 5 | Vague attributions | "Experts argue", "Industry reports" |
| 6 | Challenges/prospects formula | "Despite challenges... continues to thrive" |
| 7 | AI vocabulary words | "delve", "landscape", "tapestry", "underscore" |
| 8 | Copula avoidance | "serves as" / "stands as" instead of "is" |
| 9 | Negative parallelisms | "Not only X but Y", "It's not just X, it's Y" |
| 10 | Rule of three | Forcing ideas into groups of three |
| 11 | Synonym cycling | "protagonist" / "main character" / "central figure" / "hero" |
| 12 | False ranges | "from X to Y" where X and Y aren't on a scale |
| 13 | Em dash overuse | Excessive use of -- for "punchy" effect |
| 14 | Boldface overuse | Mechanical emphasis of terms |
| 15 | Inline-header lists | Bullet points starting with Bold: labels |
| 16 | Title Case headings | Capitalizing All Main Words |
| 17 | Emoji decoration | Emojis on headings or bullet points |
| 18 | Curly quotes | Smart quotes instead of straight quotes |
| 19 | Chatbot artifacts | "I hope this helps!", "Certainly!", "Would you like..." |
| 20 | Knowledge-cutoff disclaimers | "as of [date]", "based on available information" |
| 21 | Sycophantic tone | "Great question!", "You're absolutely right!" |
| 22 | Filler phrases | "In order to", "It is important to note that" |
| 23 | Excessive hedging | "could potentially possibly be argued" |
| 24 | Generic positive conclusions | "The future looks bright", "exciting times ahead" |
Provide:
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.