Humanizer playbook

Audit any draft against the full anti-AI-writing canon, then rewrite the failing parts so the output reads like a sharp human typed it. Three-layer detection: rule packs (Vale) → semantic check → LLM rewrite. Single-pass operator workflow.

Source synthesis: Wikipedia WikiProject AI Cleanup field guide (rule-source-of-truth), blader/humanizer v2.5.1 (workflow + audit pattern), Cole Schaefer (Honey Copy sentence craft), Joanna Wiebe (Copyhackers conversion canon), per-claim factuality eval (Hamel + Shreya, Evals are systematic data analysis on your LLM application, start with error analysis, not tests, Build LLM-as-judge as binary true/false, one judge per pesky failure mode, and validate against human labels).

When to use

Any customer-facing draft before publish: LP, email, blog, content, ad, video script. Especially before any LLM-drafted content that the team will sign their name to.

The 11 detection rule packs

Negative parallelism is THE BIG ONE because every LLM produces it dozens of times per response. Detection alone catches the gap; humanizer closes it with a rewrite layer.

Three layers

1. Detection. Vale rule packs flag every violation with line, match, severity, and fix hint.
2. Semantic check. LLM ingests surrounding paragraph + Vale finding + voice DNA principles → produces a single replacement sentence/clause that fixes the violation, preserves the underlying claim, matches the asset's voice. Non-destructive: writes to a sibling file for human review.
3. Audit signature pass. After detection + optional rewrite, ask the LLM the blader/humanizer audit question:

> "What makes this obviously AI generated?"

Record the answer. If it names new tells the rule packs missed, add them to the rule-pack queue. This is the auto-improvement loop.

Output

• Per-violation table: rule × severity × line × match × why × fix-hint.
• Single-line summary: PASS / FAIL N errors M warnings.
• Sibling file .humanized.md with rewrites applied span by span (when rewrite flag is on).

Exit codes

• 0, clean (warnings allowed unless strict).
• 1, error-level violations found.
• 2, file/tool not available.

Composes with

• Voice-enforce gate, voice-enforce is the gate; humanizer is the editor that produces the rewrite.
• CRO rubric, distinct surface. CRO fails on structure; humanizer fails on voice.
• Preflight, humanizer is the strict superset of voice-enforce.

Refusal patterns

• Substrate gap. If brand-voice canon is missing or stale, refuse to rewrite, would invent voice instead of inheriting it.
• Internal-spec asset (substrate, raw, intel), bypass automatically.
• Empty asset / non-markdown, exit 2 with explanation.

Calibration

Track under taste-type "voice-enforcement" and "humanizer-rewrite." Brier signal: post-publish reader-detection rate. Look for low AI-detection rate on the published page.

Common failure modes

• Rewriting without the brand-voice canon loaded → drift. The rewrite invents voice instead of inheriting it.
• Treating the rule packs as static. Every new model variant produces new tells; the audit-layer feedback loop must run.
• Auto-merging rewrites without human review.
• Running humanizer AFTER claim-verify. Run voice gates before truth gates so substrate-cited claims are not over-rewritten.
• Stopping at detection. Detection without rewrite leaves operators to fix by hand.

Why this matters

Detection alone catches the gap. Without the rewrite layer, operators get a fail report and fix by hand, that is the gate's role, not the editor's. With the humanizer, the operator gets a draft + a reviewable rewrite in one pass. The compound is read-time-to-ship reduction.