writing-humanize

Este extracto es una copia guardada durante la revisión. La fuente externa contiene la versión completa y actual.

---
name: writing-humanize
description: Review and rewrite text to reduce AI detection signals and increase authenticity. Flags overrepresented AI vocabulary, structural signatures (uniform paragraph lengths, formulaic openers/closers, transition-word overuse), burstiness flatness, hedging, abstract nominalizations, and missing specificity. Produces flagged audit with line-level rewrites. Use on cover letters, personal statements, blog posts, essays, and documentation where the author's voice must read as genuinely human.
---

# Writing Humanize

Audit text for AI detection signals and rewrite to increase authenticity, sentence-rhythm variation, and authorial specificity.

## When to Use This Skill

- Cover letters, personal statements, graduate school essays
- Blog posts or articles where voice and credibility matter
- Documentation written with AI assistance that needs a human editorial pass
- Any text being submitted to a platform with AI detection screening

## The Two Metrics That Drive AI Detection

GPTZero and similar detectors use two core signals:

**Perplexity** — how unpredictable each word choice is. AI runs low (predictable, safe word choices). Human writing surprises.

**Burstiness** — how much sentence-length complexity varies. AI runs flat. Humans alternate: a short punch. Then a long, clause-heavy sentence that builds toward something.

Both must improve. Fixing vocabulary without fixing rhythm leaves the structural fingerprint intact.

## Audit Protocol

Run in sequence. Flag every instance with location and rewrite suggestion.

### Step 1 — Vocabulary Scan

Check for flagged terms from `references/word-lists.md`:
- **Tier 1** (50–269× overrepresented): delve, realm, nuanced, multifaceted, tapestry, robust, pivotal, seamless, leverage, harness, underscore, testament, cutting-edge, intricate
- **Tier 2** (transition overuse): moreover, furthermore, additionally, it is worth noting
- **Em dash overuse**: flag every em dash where a comma or period would be natural

For each hit: identify the sentence, explain why it signals AI, offer a concrete replacement.

### Step 2 — Structural Signature Scan

Check paragraph-level patterns:

| Signal | What to Look For | Fix |
|--------|-----------------|-----|
| Uniform length | Every paragraph 3–5 sentences, similar word counts | Break one short (1–2 sentences). Expand one long. |
| Formulaic opener | "In today's rapidly evolving landscape..." or similar throat-clearing | Cut entirely or replace with a specific claim or scene |
| Formulaic closer | "In conclusion, it is clear that..." | End on a specific image, an open question, or a direct call |
| Transition-word boundary | moreover/furthermore starting 2+ consecutive paragraphs | Remove or replace with a direct pivot sentence |
| Participial opening overuse | 3+ sentences opening with a present participle ("Leveraging...", "Building on...") | Rewrite as declarative or imperative |

### Step 3 — Burstiness Check

Count sentence lengths (in words) across the full text. Calculate:
- Mean sentence length
- Longest sentence / shortest sentence ratio
- Run of 3+ consecutive sentences within 3 words of the mean (flat zone)

**Target**: ratio ≥ 3:1 (longest to shortest). Flat zones of 4+ sentences should be broken.

Report the distribution. Rewrite one flat zone as an example.

### Step 4 — Authenticity Scan

Check for tells that AI describes rather than experiences:

| Tell | Example | Fix |
|------|---------|-----|
| No anecdote / all abstract | "I have experience managing cross-functional teams" | Specific: who, when, what broke, what happened |
| Hedging density | might, could, arguably, it's important to note (>3 per 300 words) | Commit to the claim or cut it |
| Abstract nominalization | "implementation", "utilization", "optimization" hiding who did what | "We shipped", "Sarah rebuilt", "I cut latency by 40ms" |
| Positivity bias | No tension, no failure, no contradiction | Add one thing that went wrong or one position h