ig-humanizer

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.