content-machine

Name: content-machine
Availability: InStock
Author: naopensedoit2-design

Generate platform-specific social media content matching a writer's voice.

Updated Apr 25, 2026

Search Tags:#social media #content generation #repurposing #hook writing #voice matching #platform mechanics #newsletter optimization

Ready to run

npx skills add https://github.com/naopensedoit2-design/Claramente-nao-sou-um-Escritor --skill content-machine-naopensedoit2-design

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: content-machine
Source: https://github.com/naopensedoit2-design/Claramente-nao-sou-um-Escritor/tree/main/.local/secondary_skills/content-machine
Command: npx skills add https://github.com/naopensedoit2-design/Claramente-nao-sou-um-Escritor --skill content-machine-naopensedoit2-design

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

It solves the problem of writing content that sounds good but underperforms because it ignores platform-specific mechanics like truncation windows, hook physics, and engagement signals.

Core Features & Use Cases

Voice Analysis: Extracts writing traits from 3-5 examples (or uses a web-sourced reference) to calibrate tone, pacing, POV, emoji density, and signature phrases.
Platform Mechanics Calibration: Applies current 2025-2026 constraints for LinkedIn, X/Twitter, Instagram, TikTok, and newsletters so the output fits how each platform actually delivers content.
Hook & Structure Engineering: Generates named-pattern hooks and enforces platform-specific structure, including CTA rules and banned openers.
Repurposing Waterfall: Converts one long-form piece into multiple assets across channels (blog, X thread, LinkedIn post/carousel, newsletter section, Instagram carousel, TikTok/Reel script).

Quick Start

Ask it: Create 2 LinkedIn posts (800-1,000 characters) in my voice using the contrarian hook pattern, based on these 4 example posts: [paste examples], and include a single question CTA tailored to mobile truncation.

Frequently Asked Questions about content-machine

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I repurpose long-form content into platform-ready social media posts?▼

Repurposing long-form content into platform-ready social media posts requires a repurposing waterfall that converts a single piece into multiple assets like X threads, LinkedIn carousels, and TikTok scripts while applying platform-specific truncation and engagement mechanics.

What is voice matching for social media content generation?▼

Voice matching for social media content generation is the process of extracting writing traits like tone, pacing, POV, and emoji density from 3-5 example posts to calibrate AI output so it matches your specific writing style rather than producing generic copy.

How do I write social media hooks that avoid banned openers and improve engagement?▼

Writing social media hooks that improve engagement involves selecting named-pattern hooks and enforcing platform-specific structure rules, including banned openers and single-CTA validation, to align with how platforms actually deliver and truncate content.

Can I generate a content calendar and email newsletters using my own writing voice?▼

Yes, you can generate content calendars and email newsletters using your own writing voice by providing 3-5 example posts or a web-sourced reference, which calibrates the tone and pacing for newsletter optimization and cross-platform social media output.

Does platform mechanics calibration affect LinkedIn post character limits and truncation?▼

Platform mechanics calibration directly affects LinkedIn post character limits and truncation by applying current 2025-2026 constraints, ensuring the generated content fits how LinkedIn actually delivers posts to mobile users and aligns with engagement signals.

Why does my AI-generated social media content sound generic and underperform on platforms?▼

AI-generated social media content sounds generic and underperforms because it ignores platform-specific mechanics like truncation windows, hook physics, and engagement signals, whereas voice-extracted content with enforced structure and single-CTA validation aligns with platform delivery.