declawedlisted

# Declawed Strip every mark of AI writing from a text and make it good in its genre. Not "make it pass a detector" — make it read like a specific person with a specific point wrote it for a specific audience. ## Why this is a loop, not a style guide The worst tells — above all the **"not X but Y"** family — are not vocabulary mistakes. They are emergent properties of how LLMs generate text: preference tuning rewards balanced, contrastive, comprehensive-sounding framing, so the contrast move is baked into the model's priors. Two consequences drive this skill's architecture: 1. **You cannot reliably see your own slop.** The same priors that produce the pattern make it invisible on re-read. Detection must be mechanical — regex against a fixed catalog — never "does this look AI to me?" 2. **Rewriting reintroduces slop.** Ask a model to remove "it's not just X, it's Y" and it produces "this is less about X than Y" — the same move in a wig. So every rewrite gets re-scanned, and the loop runs until the scan is clean. Workflow: **Scan → Diagnose → Rewrite by meaning → Re-scan → (repeat) → Register check.** ## Phase 0: Fix the target Before touching the text, establish: - **Genre and venue** — academic article, tweet, reddit post, LinkedIn, email, blog, docs, marketing. Genre decides which tells are fatal and what "good" means; see [references/tones.md](references/tones.md). If it's stated or obvious from the text, use it. If not, default to general prose (neutral register, pl