content-os

name	content-os
description	Content OS orchestrator - the master skill that produces ALL content types from one seed idea (forward mode) or splits long-form content into short-form pieces (backward mode). Invokes research, writing, quality review, and visual generation skills in a coordinated pipeline. Long-form content goes through full quality gates; short-form gets quick accuracy pass.

Content OS: Multi-Format Content Orchestrator

The "produce everything" button. Give one seed idea → get all content types. Or give long-form content → get it split into short-form pieces.

Quick Start

Forward Mode (Seed → All Content)

User: "Content OS: Statins myth-busting for Indians"

Output:
├── Long-form (quality-passed)
│   ├── YouTube script (Hinglish)
│   ├── Newsletter (B2C - patients)
│   ├── Newsletter (B2B - doctors)
│   ├── Editorial
│   └── Blog post
├── Short-form (accuracy-checked)
│   ├── 5-10 tweets
│   ├── 1 thread
│   └── Carousel content
└── Visual
    ├── Instagram carousel slides
    └── Infographic concepts

Backward Mode (Long-form → Split)

User: "Content OS: [paste your blog/script/newsletter]"

Output:
├── 5-10 tweets (key points)
├── 1 thread (condensed narrative)
├── Carousel slides (visual summary)
└── Snippets (quotable sections)

How It Works

Mode Detection

• Forward Mode: Input is a topic/idea (short text, question, or concept)
• Backward Mode: Input is existing long-form content (>500 words)

Forward Mode Pipeline

PHASE 1: RESEARCH
│
├── PubMed MCP
│   └── Search for relevant papers, trials, guidelines
│
├── knowledge-pipeline (RAG)
│   └── Query AstraDB for ACC/ESC/ADA guidelines, textbooks
│
├── social-media-trends-research (optional)
│   └── Check trending angles, audience questions
│
└── OUTPUT: research-brief.md
    └── Synthesized knowledge with citations

PHASE 2: LONG-FORM CONTENT (Full Quality Pipeline)
│
├── youtube-script-master
│   └── Hinglish script → Quality Review → Final
│
├── cardiology-newsletter-writer
│   └── B2C newsletter → Quality Review → Final
│
├── medical-newsletter-writer
│   └── B2B newsletter → Quality Review → Final
│
├── cardiology-editorial
│   └── Editorial → Quality Review → Final
│
└── cardiology-writer
    └── Blog post → Quality Review → Final

PHASE 3: SHORT-FORM CONTENT (Quick Accuracy Pass)
│
├── x-post-creator-skill
│   └── 5-10 tweets → Accuracy Check → Final
│
├── twitter-longform-medical
│   └── Thread → Accuracy Check → Final
│
└── Extract carousel content from long-form

PHASE 4: VISUAL CONTENT
│
├── carousel-generator
│   └── Generate Instagram slides from key points
│
└── cardiology-visual-system
    └── Infographic concepts (if data-heavy)

PHASE 5: OUTPUT
│
└── Organized folder structure with all content

Backward Mode Pipeline

PHASE 1: ANALYZE
│
└── Parse long-form content
    ├── Extract key points
    ├── Identify data/statistics
    ├── Find quotable sections
    └── Determine topic/theme

PHASE 2: SPLIT (Quick Accuracy Pass)
│
├── Generate tweets (5-10)
│   └── One key point per tweet
│
├── Generate thread
│   └── Condensed narrative
│
├── Extract carousel content
│   └── Key points for slides
│
└── Create snippets
    └── Quotable sections

PHASE 3: VISUAL
│
└── carousel-generator
    └── Generate slides from extracted content

PHASE 4: OUTPUT
│
└── All short-form pieces organized

Quality Gates

Long-Form Quality Pipeline (FULL)

Each long-form piece goes through:

1. scientific-critical-thinking

   • Evidence rigor check
   • Citation verification
   • Claim accuracy
   • Statistical interpretation

2. peer-review

   • Methodology review
   • Logical consistency
   • Completeness check
   • Counter-argument consideration

3. content-reflection

   • Pre-publish QA
   • Audience appropriateness
   • Clarity check
   • Structure review

4. authentic-voice

   • Anti-AI pattern removal
   • Voice consistency
   • Natural language check

Short-Form Accuracy Pass (QUICK)

Each short-form piece gets:

1. Data Interpretation Check
   • Are trial results stated correctly?
   • Are statistics accurately represented?
   • Is the study conclusion not misrepresented?
   • Are effect sizes/NNT/HR correctly stated?

This is a sanity check, not full review. User can iterate manually.

Skills Invoked

Research Skills

Skill	Purpose
knowledge-pipeline	RAG + PubMed synthesis
PubMed MCP	Direct paper search
social-media-trends-research	Trending angles

Writing Skills

Skill	Content Type	Quality Gate
youtube-script-master	YouTube script (Hinglish)	Full
cardiology-newsletter-writer	Patient newsletter	Full
medical-newsletter-writer	Doctor newsletter	Full
cardiology-editorial	Editorial	Full
cardiology-writer	Blog post	Full
x-post-creator-skill	Tweets	Quick
twitter-longform-medical	Thread	Quick

Quality Skills

Skill	Purpose	Used For
scientific-critical-thinking	Evidence rigor	Long-form
peer-review	Methodology check	Long-form
content-reflection	Pre-publish QA	Long-form
authentic-voice	Anti-AI cleanup	Long-form

Visual Skills

Skill	Purpose
carousel-generator	Instagram slides
cardiology-visual-system	Infographics

Repurposing Skills

Skill	Purpose
cardiology-content-repurposer	Backward mode splitting

Output Structure

/output/content-os/[topic-slug]/
├── research/
│   └── research-brief.md           # Foundation for all content
│
├── long-form/                       # Full quality pipeline
│   ├── youtube-script.md           ✓ Quality passed
│   ├── newsletter-b2c.md           ✓ Quality passed
│   ├── newsletter-b2b.md           ✓ Quality passed
│   ├── editorial.md                ✓ Quality passed
│   └── blog.md                     ✓ Quality passed
│
├── short-form/                      # Quick accuracy pass
│   ├── tweets.md                   ✓ Accuracy checked
│   ├── thread.md                   ✓ Accuracy checked
│   └── snippets.md                 ✓ Accuracy checked
│
├── visual/
│   ├── carousel/
│   │   └── slide-01.png...
│   └── infographic-concepts.md
│
└── summary.md                       # What was produced

Invocation Examples

Forward Mode

"Content OS: GLP-1 agonists cardiovascular benefits"
"Content OS: Statin myths for Indian patients"
"Content OS: When to get a CAC score"
"Content OS: SGLT2 inhibitors in heart failure"

Backward Mode

"Content OS: [paste your 2000-word blog post]"
"Content OS: [paste your YouTube script]"
"Content OS: [paste your newsletter]"

Configuration

What Gets Produced (Forward Mode)

Content Type	Default	Can Skip
YouTube Script	Yes	Yes
Newsletter B2C	Yes	Yes
Newsletter B2B	Yes	Yes
Editorial	Yes	Yes
Blog	Yes	Yes
Tweets	Yes	Yes
Thread	Yes	Yes
Carousel	Yes	Yes

Customization

"Content OS: Statins - only YouTube and tweets"
"Content OS: Heart failure - skip editorial"
"Content OS: CAC scoring - long-form only"

Integration with Existing System

Content OS orchestrates skills that already exist in your system. It doesn't replace them - it coordinates them.

You can still use individual skills directly:

• youtube-script-master for just a script
• x-post-creator-skill for just tweets
• carousel-generator for just slides

Content OS is for when you want everything at once.

Notes

• Long-form content takes longer due to quality pipeline
• Short-form is faster (quick accuracy pass only)
• Research phase runs once, shared by all content
• Visual content generated from text output
• All content uses same research foundation for consistency

Voice & Quality Standards

All content follows:

• YouTube: Peter Attia depth + Hinglish (70% Hindi / 30% English)
• Twitter/Writing: Eric Topol Ground Truths style
• B2B (Doctors): JACC editorial voice
• Anti-AI: No "It's important to note", no excessive hedging
• Citations: Q1 journals, specific statistics, NNT/HR/CI when relevant