gtm-humanizer

name	gtm-humanizer
description	Rewrite AI-generated text — especially DRAFTED EMAILS AND MESSAGES — so they read like a real person wrote them, not a chatbot. Use as the final pass on any email, Slack message, follow-up, case study, or customer-facing copy a GTM Superintelligence agent drafts, and whenever the user says "humanize this," "sounds like AI," "too robotic," "de-AI this," "sounds like ChatGPT," or "make it natural." Detects common AI-writing tells, scores how AI it sounds (0–100), and rewrites in a chosen voice profile. Bans em dashes by default. Auto-loads humanizer-context.md from the project root for brand/sender voice.
user-invocable	true
argument-hint	"your text" [--mode detect|rewrite|edit] [--voice casual|professional|technical|warm|blunt] [--file path] [--purpose email|message|marketing|general] [--score]
allowed-tools	["Read","Write","Edit","Grep","Glob"]

GTM Humanizer

Take text that reads like a chatbot produced it and rewrite it so a specific, busy human clearly wrote it. Built for GTM: cold emails, follow-ups, Slack messages, handoff notes, case studies — the copy that has to earn a reply, not just be grammatically fine.

The failure mode this fixes: text that's smooth, generic, over-helpful, and stake-free — it could have been sent to anyone, by anyone, about anything. Human writing has a point of view, specifics, and one clear ask.

How to run

1. Load context. If humanizer-context.md exists at the project root, read it first and obey it — it defines the sender/brand voice and hard rules (in this repo it sets a GTM sender voice and bans em dashes). Project rules win over defaults.
2. Detect. Scan the text against patterns.md (the catalog of AI-writing tells). List each tell you find with the offending span.
3. Score (0–100, lower = more human). Roughly: count distinct tells, weight the loud ones (em dashes, "I hope this finds you well," hype adjectives, rule-of-three) higher. Bands: 0–15 reads human · 16–35 mostly human · 36–60 mixed · 61–100 obviously AI.
4. Rewrite in the chosen --voice (see voice-profiles.md; default professional, lean blunt for cold outreach). Fix every detected tell. Keep the author's real meaning, facts, and any specific detail (names, numbers, quotes) — specificity is what proves a human wrote it. Do not invent facts to sound human.
5. Re-check the rewrite against the catalog. If new tells crept in, fix them. Report the before/after score.

Modes

• --mode detect — only report tells + score; don't rewrite.
• --mode rewrite (default) — return the humanized text.
• --mode edit --file <path> — rewrite in place in a file.

Hard rules (always, unless context overrides)

• No em dashes. Use periods or commas.
• No throat-clearing openers: "I hope this email finds you well," "I wanted to reach out," "I'm excited to," "As an AI," "In today's fast-paced world."
• No hype adjectives: seamless, robust, leverage, synergy, game-changer, cutting-edge, best-in-class, unlock, delve, elevate, tapestry, robustly.
• No rule-of-three padding ("streamline, optimize, and accelerate").
• One clear ask. Lead with the point. Cut anything that restates what you just said.
• Keep it the right length for the channel (email: short and skimmable; Slack: shorter, no bold-text theatre; case study: quotes and outcomes over adjectives).

Output

By default return just the rewritten text (ready to paste/send). If --score or detect mode, also print the tell list and the before→after score. Never add commentary the user didn't ask for.

See patterns.md for the full tell catalog and voice-profiles.md for the voices.