// Detect and rewrite AI writing patterns to make content sound authentically human. Supports voice calibration from the author's own writing samples. Use when asked to humanize text, remove AI patterns, calibrate voice, audit content for AI detectability, or check if writing sounds like ChatGPT. Triggers: /humanize, /voice-calibrate, /audit-ai
Transform git commit history into polished user-facing changelogs and release notes. Use when the user wants a CHANGELOG.md from a version tag or date range, weekly product updates, app store release notes, or Keep a Changelog formatted output with internal noise filtered out.
Create videos programmatically using React (Remotion) or generate mathematical/conceptual animations (Manim). Use when the user asks to make a video, animation, product demo, tutorial, social clip, or branded video pipeline. Triggers: /remotion-video, /video-animation, /video-toolkit
Full legal plugin suite for commercial contract review, privacy/DPA analysis, and AI governance vendor reviews. Use when the user invokes /commercial-legal:review, /privacy-legal:dpa-review, /ai-governance-legal:vendor-ai-review, renewal tracking, DSAR responses, or EU AI Act triage.
Review contracts with clause-by-clause risk scoring, market benchmarks, negotiability ratings, and redline suggestions. Use when the user pastes an NDA, SaaS/MSA, M&A LOI, or payment agreement and wants a Contract Safety Score, CUAD-based risk breakdown, or /review as [position] analysis.
In-house legal workflows for contract review against playbooks, NDA triage with GREEN/YELLOW/RED ratings, compliance briefings, and vendor checks. Use when the user invokes /review-contract, /triage-nda, /legal-risk-assessment, or /vendor-check for organisation-standard legal analysis.
Product management workflows covering the full work lifecycle — discovery, strategy, execution, launch, and growth. Write PRDs, plan OKRs, and structure meeting notes. Useful beyond PMs — applicable to any knowledge worker managing projects, stakeholders, or strategy. Triggers: /write-prd, /plan-okrs, /meeting-notes
| name | humanizer |
| description | Detect and rewrite AI writing patterns to make content sound authentically human. Supports voice calibration from the author's own writing samples. Use when asked to humanize text, remove AI patterns, calibrate voice, audit content for AI detectability, or check if writing sounds like ChatGPT. Triggers: /humanize, /voice-calibrate, /audit-ai |
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.
npx skills add blader/humanizerFull 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.
| 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. |
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.
| 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. |
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.
| 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. |
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