| name | revisao-humanizada |
| description | Humanizes narrative copy by removing AI tells, varying rhythm, and stripping templated phrasing before publish |
| version | 0.1.0 |
Revisao Humanizada
Final pass on any narrative copy headed for a human audience. Strips the typical AI fingerprints: em-dashes, triadic patterns, hedging, monotone sentence length, and recycled connectors. Mirrors the global humanizer skill behavior, scoped to the marketing-engine pipeline.
When to invoke
- Right before publishing any caption, post body, blog article, email, ad copy, video script, landing page section.
- After the first draft from
copywriter-curto, caption-multi-platform, or any LLM-generated long-form text.
- When a reviewer flags copy as sounding generic, robotic, or template-like.
- During QA pass on a scheduled piece in
.specs/pieces/.
- Whenever the brand voice score from a manual read drops below the threshold defined in
.specs/strategy/BRAND.md.
Inputs
text: string. The draft copy to humanize.language: string. Target language code (pt-BR, en, es).platform: string, optional. Platform context (ig, tiktok, youtube, linkedin, email, blog).brand_voice_path: string, optional. Path to .specs/strategy/BRAND.md for tone constraints.preserve_terms: array of strings, optional. Brand terms, product names, and CTAs that must stay verbatim.
Process
- Read
text and inventory AI tells: em-dashes, sentences starting with In conclusion, Moreover, Furthermore, triads like fast, simple, and effective.
- Read
brand_voice_path if provided. Note the brand tone, banned words, and required phrasing.
- Call
llm-router with task: humanization. Send the draft, language, platform, brand voice excerpt, and the preserve_terms list.
- Apply rewrites: vary sentence length (short, medium, long, fragment), break triadic patterns, replace em-dashes with comma or period, remove hedge words (
perhaps, it could be argued).
- Verify preserve_terms still appear verbatim in the output.
- Confirm the output language matches
language.
- Score the result against an internal AI-tell checklist. If three or more tells remain, run one more pass.
- Return the humanized text plus a short diff of what changed.
Outputs
text: string. Humanized copy.changes: array of strings. Bullet log of edits applied.ai_tells_remaining: integer. Count of tells the final pass could not remove.passes_used: integer. How many humanization rounds ran.
Examples
Example 1: caption rewrite
Input: { text: "In conclusion, color analysis is fast, simple, and effective.", language: "en", platform: "ig" }
Output: { text: "Color analysis works. It is quick. The results stick.", changes: ["removed In conclusion", "broke triad fast/simple/effective"], ai_tells_remaining: 0, passes_used: 1 }
Example 2: long-form blog paragraph
Input: a 200-word draft full of em-dashes and triads. Output: same paragraph rewritten with mixed sentence rhythm, em-dashes converted, brand terms intact.
Failure modes
- Preserve_terms vanish in rewrite: re-inject them in the exact original positions and rerun humanization with stronger constraint.
- Output language drifts: force the language constraint in the next call and reject the previous result.
- Result still scores high on AI tells after two passes: surface the issue, return the best result, and flag for human edit.
Related skills
copywriter-curto: produces the draft this skill polishes.caption-multi-platform: each variant is humanized before being scheduled.llm-router: actually runs the LLM call for the humanization pass.compliance-generic: should run after humanization to confirm no banned phrasing slipped in.
