eigent-ai Overview
Detects and rewrites 29 AI writing patterns across content, language, style, and communication categories. Patterns include: significance inflation, vague attributions, copula avoidance, synonym cycling, em dash overuse, sycophantic tone, chatbot artifacts, and more. Also supports voice calibration from your own writing samples.
Source Repository
- GitHub: blader/humanizer
- Install upstream:
npx skills add blader/humanizer
/humanize
Full 29-pattern detection and rewrite. Analyzes the input for all AI-sounding patterns, rewrites the content, then runs a second-pass "obviously AI?" audit before returning the final version.
Workflow
- Receive the text to humanize and any brand voice notes.
- Scan for all 29 patterns — flag each instance found.
- Rewrite the text: fix flagged patterns, preserve the core argument and facts.
- Run a second-pass audit: read the rewrite cold and ask "does any sentence still sound AI-generated?"
- Return the final rewritten text with a brief summary of the main changes made.
Example prompts
| Use case | Task prompt |
|---|---|
| Blog post | Humanize this blog post draft. Remove any AI-sounding patterns. Preserve the core argument but make it read like a person who actually has opinions wrote it. |
| Rewrite this LinkedIn post. It currently sounds like ChatGPT wrote it. Cut the significance inflation, remove the em dashes, and make it direct. | |
| Press release | Run the 29-pattern check on this press release. Flag every AI pattern you find, then rewrite it. The brand voice is confident and plain-spoken, not corporate. |
/voice-calibrate
Accepts 2–3 writing samples from the target author, extracts their stylistic fingerprint (sentence rhythm, vocabulary, punctuation habits), then applies that fingerprint to any AI-generated text.
Workflow
- Receive 2–3 writing samples from the target author.
- Analyze samples for: average sentence length, punctuation habits, vocabulary range, tonal register, structural patterns (how they open/close paragraphs), and idioms they favor.
- Document the fingerprint as a short style profile.
- Apply the fingerprint to the target AI text, rewriting to match the author's natural voice.
- Return the rewritten text with the style profile so it can be reused in follow-up requests.
Example prompts
| Use case | Task prompt |
|---|---|
| Newsletter | Here are 3 of my past newsletters [attached]. Use them to learn my voice, then rewrite this AI-drafted issue to match how I actually write. |
| CEO post | Calibrate to the CEO's voice using these 5 LinkedIn posts. Then rewrite this product announcement so it sounds like her, not our content team. |
| Personal brand | I want to post consistently on LinkedIn but don't have time to write from scratch. Learn my voice from these samples and rewrite these 5 AI drafts to match it. |
/audit-ai
Scores text 0–100 for AI detectability across all 29 pattern categories. Highlights specific phrases most likely to trigger AI detectors and returns a prioritized fix list ranked by severity.
Workflow
- Receive the text to audit.
- Score each of the 29 patterns 0–3 (0 = not present, 3 = severe).
- Compute an overall AI detectability score (0–100, higher = more detectable).
- Highlight the top phrases that are most suspicious.
- Return the score, highlighted phrases, and a prioritized fix list (must-fix vs. nice-to-fix).
Example prompts
| Use case | Task prompt |
|---|---|
| Pre-publish | Audit this article before we publish it. Give me an AI score, highlight the top 10 most suspicious phrases, and tell me which ones I absolutely must fix. |
| SEO content | Score these 5 blog posts for AI detectability. Rank them worst to best and give me a fix list for the 3 worst offenders. |
| Team workflow | Build me a pre-publish checklist based on the 29 patterns so our editors know what to review every time before anything goes live. |
The 29 AI Writing Patterns
Organized into four categories:
Content patterns: significance inflation, vague attributions, unnecessary hedging, false balance, over-explaining obvious things, unsupported universal claims
Language patterns: copula avoidance ("utilizing" instead of "using"), synonym cycling (rotating synonyms to avoid repetition), em dash overuse, passive voice stacking, gerund chains, adverb padding
Style patterns: sycophantic openers, chatbot sign-off artifacts, hollow transitional phrases ("It's worth noting that…"), conclusion telegraphing ("In conclusion…"), bullet-point everything bias
Communication patterns: fence-sitting on opinions, corporate hedging language, artificial enthusiasm, filler affirmations ("Certainly!", "Great question!"), over-structured responses, meta-commentary about the writing itself