content-os
About
Content OS is a master orchestrator skill that generates all content types from a single seed idea in forward mode or splits long-form content into short-form pieces in backward mode. It coordinates a pipeline of specialized skills for research, writing, quality review, and visual generation, applying full quality gates to long-form output and a quick accuracy pass to short-form content. Use it as a unified "produce everything" button for multi-format content creation workflows.
Quick Install
Claude Code
Recommended/plugin add https://github.com/majiayu000/claude-skill-registrygit clone https://github.com/majiayu000/claude-skill-registry.git ~/.claude/skills/content-osCopy and paste this command in Claude Code to install this skill
Documentation
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:
-
scientific-critical-thinking
- Evidence rigor check
- Citation verification
- Claim accuracy
- Statistical interpretation
-
peer-review
- Methodology review
- Logical consistency
- Completeness check
- Counter-argument consideration
-
content-reflection
- Pre-publish QA
- Audience appropriateness
- Clarity check
- Structure review
-
authentic-voice
- Anti-AI pattern removal
- Voice consistency
- Natural language check
Short-Form Accuracy Pass (QUICK)
Each short-form piece gets:
- 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-masterfor just a scriptx-post-creator-skillfor just tweetscarousel-generatorfor 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
GitHub Repository
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