platform-content-analyzer - Claude MCP Skill

# Platform Content AnalyzerA comprehensive skill for analyzing and adapting content across different platforms while maintaining core message integrity and maximizing platform-specific engagement.

## Platform Characteristics Matrix

### LinkedInAudience: Professionals, decision-makers, industry expertsTone: Professional, insightful, value-drivenContent Length: 1300-3000 characters optimalBest Format: Native text, documents, native videoEngagement Style: Thoughtful comments, professional networking

Al

gorithm Preference: Dwell time, meaningful conversations

### Twitter/XAudience: Tech enthusiasts, thought leaders, real-time discussersTone: Concise, witty, conversationalContent Length: 280 chars per tweet, threads for depthBest Format: Threads, images, quick videoEngagement Style: Retweets, quick reactions, quote tweets

Al

gorithm Preference: Early velocity, controversy, replies

### RedditAudience: Community-focused, skeptical, technically savvyTone: Authentic, humble, community-firstContent Length: Varies by subreddit title crucialBest Format: Text posts, linked content with contextEngagement Style: Upvotes, detailed discussions

Al

gorithm Preference: Early upvotes, comment engagement

### GitHubAudience: Developers, open-source contributorsTone: Technical, collaborative, documentation-focusedContent Length: Comprehensive READMEs, clear docsBest Format: Markdown, code examples, diagramsEngagement Style: Stars, forks, issues, PRs

Al

gorithm Preference: Activity, star velocity

### Hacker NewsAudience: Entrepreneurs, developers, tech intellectualsTone: Intellectually honest, technically deepContent Length: Title is everything, discussions go deepBest Format: Show HN posts, technical articlesEngagement Style: Substantive comments, technical critique

Al

gorithm Preference: Early upvotes, comment quality

### MediumAudience: Professionals seeking in-depth contentTone: Narrative, educational, thought-leader

shipContent Length: 5-10 minute read 1000-2000 wordsBest Format: Long-form articles with visualsEngagement Style: Claps, highlights, responses

Al

gorithm Preference: Read time, completion rate

## Content Adaptation Framework

###

1. Core Message Extraction

pythondef extract_core_messagecontent:
  return         main_value_prop: ,      # What problem solved        target_audience: ,       # Who benefits        key_differentiator: ,    # Why unique        call_to_action: ,        # What next        proof_points: [],          # Evidence/credibility        emotional_hook:
  # Why should they care    #

##

2. Platform-Specific Transformation

#### LinkedIn → Twitter/XLinked

In 1500 chars:Weve solved AI tool chaos. After 2 months of development...Twitter Thread:1/ Weve solved AI tool chaos 🧵2/ The problem: Your team uses 12 different AI prompts for the same task.3/ The solution: DollhouseMCP

- an open-source platform that centralizes AI customization.4/ Key insight: Built on Model Context Protocol MCP for native Claude integration.[Continue thread...]

#### LinkedIn → RedditLinkedIn:🧠 Weve solved AI tool chaos.Reddit r/programming:[Show HN] DollhouseMCP

- Open-source AI customization platform for teams struggling with prompt sprawl

Body: Hey r/programming, After watching my team struggle with scattered AI prompts and inconsistent implementations, I built this. Its not revolutionary, but it solves a real problem we had...

#### LinkedIn → GitHubLinkedIn:Announcing DollhouseMCP...Git

Hub README:
  # DollhouseMCP Centralized AI customization platform for consistent team workflows

## The Problem

Teams using AI tools face...

## Installation

bashnpm install @dollhousemcp/mcp-server

## Quick Start[Clear technical documentation]

###

3. Tone Adaptation Patterns

#### Professional → CasualProfessional: Weve identified a significant opportunity in the marketCasual: We found something interesting

Reddit: So we stumbled onto this gap that nobodys talking about

#### Marketing → TechnicalMarketing: Revolutionary AI customization platformTechnical: MCP-based tool for managing AI model contexts

Hacker News: Built a protocol layer for AI persona management

#### Promotional → EducationalPromotional: Try DollhouseMCP todayEducational: Heres how we approached the context management problem

Community: Sharing what we learned building this

## Platform Content Templates

### Linked

In Post → Multi-Platform

#### Original LinkedIn Post🧠 Weve solved AI tool chaos.Two months a

go, I faced a problem every developer knows: scattered AI prompts, disconnected tools, and no way to maintain consistency.The result DollhouseMCP

- an open-source platform that centralizes AI customization.What makes it different:✅ Natural language management✅ Team consistency✅ MCP-native architecture✅ Community-poweredWhats your biggest AI workflow challenge#AI #OpenSource #Developer

Tools

#### Twitter/X AdaptationTweet 1:Your team is using 12 different AI prompts for the same task.We fixed that.🧵 Introducing DollhouseMCP:Tweet 2:The problem is real:
  - Scattered prompts across Slack/Docs/repos

- Everyones got their better version

- No consistency = no scalability

Tweet 3:Our solution: centralized AI customization

- Single source of truth

- Version control for prompts

- Team-wide consistency

- Built on @Anthropics MCP[Continue...]

#### Reddit Adaptation r/programmingTitle: I got tired of our teams AI prompt chaos, so I built an open-source solutionBody:Like many of you, our team started using AI tools heavily this year. The problem Everyone had their own prompts, stored wherever, with no sharing or versioning.I spent 2 months building DollhouseMCP...[Technical details, not marketing speak]Code: github.com/...Docs: Complete API reference included

Would love feedback from anyone dealing with similar challenges.

#### Hacker News AdaptationTitle: Show HN: DollhouseMCP – Open-Source AI Context ManagementBody:I built this to solve a specific problem: managing AI customizations across a team without the chaos of scattered prompts.Technical approach:
  - Built on Anthropics Model Context Protocol

- TypeScript/Node.js implementation

- Local-first with optional syncWhy another tool MCP is powerful but needs better abstractions for team use.Git

Hub: [link]Demo: [link]Happy to answer technical questions.

## Engagement Metric Translation

### Cross-Platform Success Indicators

javascriptconst platformMetrics =   linkedin:
  good:
  engagementRate: 0.03, comments: 10 ,    viral:
  engagementRate: 0.06, comments: 50   ,  twitter:
  good:
  retweetRatio: 0.1, replyRatio: 0.05 ,    viral:
  retweetRatio: 0.3, replyRatio: 0.15   ,  reddit:
  good:
  upvoteRatio: 0.8, comments: 20 ,    viral:
  upvoteRatio: 0.95, comments: 100   ,  hacker

News:
  good:
  points: 50, comments: 20 ,    viral:
  points: 500, comments: 200   #

# Content Recycling Strategy

###

1. LinkedIn Long-Form → Platform Series

- LinkedIn article → Medium post minimal changes

- LinkedIn article → Twitter thread key points

- LinkedIn article → Reddit series split by topic

- LinkedIn article → Git

Hub blog post add code

###

2. Success Story AdaptationLinkedIn: Professional case studyTwitter: Before/after screen

shot threadReddit: Technical deep-diveGit

Hub: Implementation guideHN: Lessons learned post

###

3. Product Launch CascadeDay 1:
  - GitHub: Release + documentation

- HN: Show HN postDay 2:
  - LinkedIn: Thought leader

ship angle

- Twitter: Feature highlights

Day 3:
  - Reddit: Community-specific posts

- Medium: Technical deep-dive

## Platform-Specific Optimization

### Ha

shtag Strategy by PlatformLinkedIn: 3-5 professional ha

shtags #AI #EnterpriseGradeTwitter: 1-2 trending ha

shtags #BuildInPublicInstagram: 10-30 ha

shtags mix of sizes

Reddit: No ha

shtags use flair instead

### Link HandlingLinkedIn: Native content  linksTwitter: Links ok, but thread first

Reddit: Link posts need substantial comment

sHN: Direct link, let title carry weight

### Visual Content RequirementsLinkedIn: 1200x627px 1.91:1Twitter: 1200x675px 16:9Instagram: 1080x1080px 1:1Reddit: Varies by subredditGit

Hub: Markdown-embedded images

## Cross-Promotion Tactics

### The Hub-and-Spoke ModelHub: Detailed GitHub repositorySpokes:├── Linked

In: Business value focus├── Twitter: Technical highlights├── Reddit: Community discussions├── HN: Technical deep-dive└── Medium: Narrative journey

### The Cascade Method

1. Start with highest-effort content blog/article

2. Break into platform-specific pieces

3. Each piece links to appropriate next step

4. Monitor and cross-pollinate discussions

## Analytics Correlation

### Multi-Platform Success Patterns

- LinkedIn success → Twitter follow-up performs well

- Reddit success → HN likely to engage

- GitHub stars → Developer platform traction

- Medium reads → Linked

In article engagement

## Content Audit Framework

### Pre-Publication Checklist- [ ] Core message clear across all versions- [ ] Platform tone appropriate- [ ] CTAs platform-optimized- [ ] Visuals formatted correctly- [ ] Timing coordinated- [ ] Cross-promotion plan ready

### Post-Publication Tracking

pythondef track_cross_platform_performance:
  metrics =         linkedin: check_linkedin_analytics,        twitter: check_twitter_analytics,        reddit: check_reddit_karma,        github: check_github_stars,        hn: check_hn_points            identify_top_performer    adjust_strategy_for_laggards    plan_next_content_cycle

## Platform Migration Paths

### Successful User Journey ExamplesTwitter Discovery → GitHub Star → LinkedIn Follow → CustomerReddit Discovery → GitHub Fork → Community ContributorHN Feature → GitHub Traffic → Documentation PR → Core ContributorLinked

In Article → Medium Deep-Dive → Newsletter Subscriber

## Integration with Other SkillsComplements:
  - linkedin-engagement-optimizer: Linked

In-specific tactics

- conversation-audio-summarizer: Multi-platform audio content

- reddit-content-strategist: Reddit deep expertise

## Success Metrics

Platform-agnostic success:
  - Message consistency: 90%+ retained

- Engagement quality: Substantive across all

- Cross-platform traffic: 20%+ referral rate

- Time efficiency: 5 platforms from 1 source

- Brand consistency: Recognizable everywhere