/ig-humanizer

name: ig-humanizer
description: "Remove the AI tells readers react to in an Instagram caption or carousel slide: 2026 vocabulary by density, reveal bridges, staccato stacks, stacked triads, performed sincerity, emoji storms; caps em dashes. Includes --mode audit (first-125 hook, length, hashtags, emoji, CTA, media) and --mode profile. Not for beating AI detectors (no edit reliably does). Not for writing from scratch (use ig-caption-writer or ig-carousel-planner). Keywords: humanize, de-AI caption, audit before posting."

Instagram Humanizer V3

Rewrites any caption or carousel slide text to remove the AI tells that human readers notice, and audits a finished caption against the 2026 Instagram checklist. Based on Wikipedia's "Signs of AI writing" taxonomy, the 2025-2026 stylometry literature, our own Instagram caption corpus (n=284, prevalence only), and Instagram-specific patterns (the first-125 fold, the lowercase-casual caption register, sized hashtags, sends-and-saves structure). **V3 (2026-09):** recalibrated on 2026 evidence. Vocabulary is scored by density, em dashes are capped instead of banned (and the cap is generous here: 29% of human captions use one), forced rhythm is now a tell instead of a fix, and there is an over-correction guard.

**What this skill does not do:** it does not make text "pass" GPTZero, Pangram, Turnitin or Originality. Those are trained classifiers keyed on the instruction-tuning style signature; prompt-style "sound like a real person" rewrites are caught 92-95% of the time, and light mechanical rewriting raises detectability. On caption-length text (under 300 words) detector scores are noise. The real value is elsewhere: expert human readers cite vocabulary (53%) and sentence structure (36%) as what gives AI text away, and on Instagram a caption that reads as a brand account earns neither the save nor the send. This skill removes what those readers react to.

What changed in V3

Evidence tier in brackets: [strong] = replicated across 2+ independent 2025-2026 studies or our own corpus; [vendor] = single platform or vendor dataset; [weak] = one study or expert-panel report.

• **Vocabulary moved from a delete-list to density scoring.** The 2023-24 words

(delve, tapestry, realm, journey) are decaying as humans avoid them [strong]. The durable 2026 markers are common words (significant, crucial, notably, comprehensive, insights, robust, leverage, foster, landscape, nuanced, streamline, elevate) plus grammar: nominalisations and "-ing" clause openers at 5.3x the human rate [strong]. AI vocabulary appears in 10% of human captions in our corpus [strong], so one marker in a paragraph is not a verdict. Three is.

• **Em dash is no longer a tell.** GPT-5.4 emits 1.43 per 1,000 words, below

the 3.23 human baseline [strong], and **29% of human Instagram captions contain one** [strong: corpus]. A caption's em dash is never a tell on its own, and a blanket ban over-sterilises captions. New rule: cap at about 1 per 100 words (1-2 per caption), replace only the excess with a comma, colon, parentheses or a rewrite. Never a period (a split dash stacks fragments). Zero em dashes across a long caption is its own tell now.

• **Forced burstiness is the #1 2026 tell, not the fix.** Mechanical

long/short alternation is a learnable humanizer fingerprint [weak], and "Short. Punchy. Done.", "No X. No Y. Just Z.", one-word lines for drama and "The result?" reveals are the current top reader-cited tells [strong]. Captions are mid-length, so Pass 2 is an anti-uniformity guard only: fix a paragraph that reads machine-flat, never manufacture variance.

• **Rule of three is still a tell, at density.** Tricolon runs at 2x

expert-human rate across 2026 frontier models [strong], and 23% of human captions contain one [strong: corpus]. So one natural triple with concrete items stays. Stacked, perfectly parallel or hollow triads and a third triad in a caption get scrubbed.

• **Fingerprint injection was half wrong.** Named entities and concreteness are

supported [strong]; an odd-precision number with a referent in the first 125 chars is the strongest hook. Bare numbers are not a discriminator, and inserted hedges and confessions backfire: performed hesitancy is 2x more common in LLM text, and sincerity announcements ("let me be honest", "real talk", "POV:" on something that is not a POV) are a named 2026 tell [strong]. Pass 3 asks for a flat, dated, uncomfortable fact instead.

• **Over-correction guard.** Humanizer output has its own fingerprint [weak].

Pass 4 checks whether Passes 1-3 introduced the very patterns they were meant to remove. Edits are proportional to real problems. When in doubt, leave it.

When to use

• Before publishing any AI-drafted caption or carousel (rewrite mode)
• Pre-publish review of a finished caption (audit mode, see `sub-skills/post-audit.md`)
• When a caption feels off and you cannot pinpoint why

Input

Any text: a caption, the on-image text of a carousel, or a Reel caption. Optional: target voice samples (the user's past captions).

Output

• Rewritten text with AI tells removed
• A diff showing what changed and why
• Caption char count (flagging if the hook spills past the first 125 chars)
• Per-paragraph tell density (markers per paragraph; 3+ triggered a rewrite)
• Reader-read confidence: "reads human", "mixed", "reads AI" (a reader-tell

estimate, not a detector score)

Modes

# Default: scrub AI tells (forensic + strict) and fix Instagram-format issues
ig-humanizer <text>

# Forensic only - minimum touch, just kill model leakage
ig-humanizer --mode forensic <text>

# Audit - detection-only pass-fail review, no rewrite
# Runs the 2026 Instagram checklist: first-125 hook, caption length, sized
# hashtags, emoji limits, CTA quality, media reminder.
# Returns Blockers + Warnings + suggested fixes. See sub-skills/post-audit.md.
ig-humanizer --mode audit <text>

# Profile