integrated_content_OS

Transforms research and knowledge base material into information-dense infographic copy—clear, scannable, authoritative, and reference-worthy. Use when converting clinical algorithms, decision trees, comparison data (drug vs drug, treatment options), trial results, myth vs truth content, patient preparation guides, checklists, or sequential process workflows into structured copy ready for single-image infographic formats. Supports multiple infographic types including hero stats, dense multi-section layouts, side-by-side comparisons, myth-busters, step-by-step processes, and patient checklists. Outputs structured copy with compressed noun-heavy phrasing, specific numbers, trial citations, and JSON-formatted handoff data for downstream infographic generation.

Manages all other skills by detecting capability gaps, proposing new skills, and keeping the system registry in sync. Use when the user encounters something the system cannot do, wants to log a capability gap, review the skill backlog, propose or build a new skill, analyze recurring unmet needs, or run sync pipelines to update context files (CLAUDE.md, GEMINI.md, AGENTS.md) after skill changes.

Template-driven medical infographics using the AntV Infographic framework. Use when creating structured infographics from clinical data, trial results, or patient education content using pre-built templates for hero stats, comparison layouts, process flows, myth-busters, and checklists. Outputs publication-ready HTML/PNG infographics with JACC/NEJM-grade design tokens.

Declarative, grammar-based charting using AntV G2 for complex medical visualizations. Use when creating forest plots, Kaplan-Meier curves, funnel plots, waterfall charts, or any statistical/clinical chart that requires grammar-of-graphics composition with medical-journal-grade styling and accessibility-validated color palettes.

Publication-grade design tokens and utilities for Nature/JACC/NEJM quality graphics. Use when generating charts, infographics, animations, or any visual content that requires medical-journal-grade color palettes, typography, accessibility-validated contrast ratios, and consistent branding. Provides Python token APIs, colorblind-safe palettes, G2 chart templates, AntV infographic templates, Vizzu animation presets, and Manim integration for animated medical explainers.

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.

Parallel search across PubMed, Perplexity, and your knowledge base. Searches all sources simultaneously and synthesizes findings with citations. Faster evidence gathering for clinical questions.

Rapid topic mastery for video/content prep. Takes a topic → generates 5 research questions → parallel PubMed + web search → outputs McKinsey-style brief in 5 minutes. Use BEFORE recording videos or writing content.

Unified visual content system for cardiology thought leadership. Automatically routes requests to the optimal tool—Fal.ai for blog imagery, Gemini for infographics, Mermaid for flowcharts/pathways, Marp for slides, Plotly for data visualization. One skill handles all visual needs within Claude Code.

Generate world-class medical infographics using carousel-level visual language. Templates include hero stats, multi-section layouts, comparisons, myth-busters, process flows, and patient checklists. Default 1080x1350 for Instagram.

Three-agent SEO audit pipeline. Scrapes your content → analyzes SERP competitors → generates prioritized optimization report with P0/P1/P2 recommendations. Use BEFORE and AFTER publishing to maximize organic reach.

Multi-model consensus scoring for content ideas. Scores the same idea with Claude, GPT-4o, Gemini, and Grok in parallel, then aggregates for a balanced verdict. Reduces single-model bias and improves viral predictions.

Track and analyze cardiology content creators (Topol, Attia, York Cardiology, Indian channels). Discovers content patterns, topics, engagement, and gap opportunities for your Hinglish content strategy.

Analyze YOUR video recordings before publishing. Evaluates voice (pace, pitch, volume), facial expressions (emotions, eye contact, smiles), and content (filler words, structure). Helps improve your Hinglish YouTube delivery over time.

Comprehensive academic textbook chapter writing system for medical/scientific content. Use when the user wants to: (1) Write a full textbook chapter (5,000-15,000 words) on any medical/scientific topic, (2) Generate a detailed table of contents with section word counts, (3) Research topics via PubMed MCP and compile 20-30 references, (4) Write section-by-section with proper citations in Vancouver format, (5) Create publishable academic content with Eric Topol-inspired voice and authentic human prose, (6) Get approval at TOC stage before writing begins, (7) Export well-structured chapters for textbook publication.

Transform AI-sounding writing into natural, human prose. Use when creating or editing any written content—articles, essays, reports, emails, social posts, scripts, or marketing copy—to avoid patterns that signal AI authorship. Triggers on requests to "make it sound human," "less AI," "more natural," improve voice/tone, or any writing task where authentic style matters.

Browser automation for ChatGPT Plus and Gemini Advanced web interfaces. Uses Playwright MCP to interact with your paid subscriptions without API costs. Supports both models for writing comparison.

Transform long-form cardiology content (YouTube transcripts, newsletters, PDFs, knowledge bases) into high-quality thought leadership content across multiple formats. Use when the user wants to repurpose medical/cardiology content into: (1) Short newspaper articles (Inshorts style), (2) Atomic essays, (3) Tweets, (4) Twitter threads, or (5) Medium-style blogs. Maintains authentic interventional cardiologist voice with clinical authority, uses 4A framework, targets specific patient archetypes, and leverages PubMed for evidence-based citations when needed.

Comprehensive cardiology editorial writing system for thought leadership newsletters. Use when the user wants to: (1) Identify and score recent landmark trials from top cardiology journals (NEJM, JAMA, Lancet, JACC, EHJ, etc.), (2) Write evidence-based editorials in Eric Topol's style from Ground Truth, (3) Create 500-word commentaries on clinical trials with PubMed citations, (4) Analyze trial importance using hybrid rules + LLM scoring, (5) Write editorials from full papers OR abstract-only scenarios, (6) Build thought leadership content for cardiologists, or (7) Synthesize recent cardiology advances for peers and referring physicians.

Create evidence-based cardiology newsletters for thought leadership in Eric Topol's authoritative Ground Truths style. Use when the user wants to analyze trending medical topics with engagement predictions, conduct data-driven topic selection, research medical literature using PubMed, or write comprehensive well-referenced newsletters that build professional authority as an interventional cardiologist. Handles complete workflow from trend analysis to final draft with smooth analytical flow between topics.

Write rigorous, accurate cardiology science for general audiences—not doctors. Use when the user wants to: (1) Explain clinical trials or research in plain English, (2) Write science content an 8th grader can understand WITHOUT dumbing it down, (3) Create thought leadership for the intelligent public rather than medical peers, (4) Transform complex cardiology findings into stories and narratives, (5) Write pieces where readers DON'T need another LLM to understand the explanation. Maintains full scientific rigor with PubMed citations for verification while avoiding academic language, trial acronyms, and intimidating statistics.

Transform thought dumps into polished cardiology content in Eric Topol's Ground Truths voice. Use when the user wants to write cardiology articles, newsletters, video scripts, or educational content from scattered ideas, clinical observations, or research notes—combining Eric Topol's evidence-based clarity with Peter Attia's deep-dive rigor.

Identify landmark cardiology trials and write evidence-based editorials in Eric Topol's authoritative Ground Truths style. Use when the user wants to: (1) Discover and evaluate recent important trials from top cardiology journals (NEJM, JACC, Lancet, EHJ, Circulation), (2) Assess trial importance using systematic scoring, (3) Write 500-word editorials on cardiology/interventional cardiology advances for physician audiences, (4) Create thought leadership content that demonstrates deep domain expertise. Supports both full-text and abstract-only scenarios with PubMed integration for references.

Generate scientifically accurate, engaging cardiology tweets for thought leadership. Use when creating social media content for a cardiologist targeting patients, health enthusiasts, health optimizers, people with lifestyle diseases, and caregivers. Produces 10 tweets per batch using permutations of cardiology seed ideas and modifiers. Incorporates feedback to improve output quality over time.

End-to-end YouTube content creation for cardiology channels. Use when user wants to create YouTube videos about cardiology, heart health, or cardiovascular topics. Triggers on greetings with content intent, requests for video ideas or scripts, help with cardiology YouTube channels, heart health video content requests, or any cardiology content creation conversation. Handles complete workflow from ideation through social listening, topic selection, and full script delivery.

Instagram carousel generator. Creates 1080x1080px branded slides from text/markdown input. Use this skill when you need to generate Instagram carousel slides with Dr. Shailesh Singh's brand colors, typography, and footer. Supports both @heartdocshailesh and @dr.shailesh.singh accounts.

Comprehensive content marketing toolkit for discovering viral content opportunities, social listening, knowledge gap analysis, and content demand assessment across platforms. Use when researching trending topics in a niche, finding what's going viral, assessing content knowledge gaps, planning content calendars, or identifying high-demand content opportunities. Integrates with perplexity-search for real-time trend data and research-lookup for deep analysis.

Pre-publication quality assurance for cardiology thought leadership content. Use AFTER any content is drafted to evaluate scientific rigor, voice authenticity, positioning alignment, audience calibration, and credibility risk. Provides structured critique with specific revision suggestions. Works on any content type—tweets, threads, newsletters, editorials, video scripts.

Write data-driven, evidence-first long-form Twitter posts on medicine and cardiology. Use when the user wants to: (1) Create thought leadership content in the style of Eric Topol, Peter Attia, Andrew Huberman, or Rhonda Patrick, (2) Present clinical evidence with charts, data, and Q1 journal citations for educated non-specialist audiences, (3) Write confident, matter-of-fact medical content that is rigorous without being inaccessible, (4) Explain trials, drugs, or medical phenomena using data visualization and systematic evidence review, (5) Build authority through methodological rigor and clear conclusions backed by evidence. NOT for newsletters or Substack. For Twitter long-form posts only.

Performs comprehensive, multi-layered research on any topic with structured analysis and synthesis of information from multiple sources. Uses file-based research tracking, parallel investigation threads, and context-efficient patterns for deep investigations. ALL MEDICAL CITATIONS FROM PUBMED MCP ONLY.

Manage Model Context Protocol (MCP) servers - discover, analyze, and execute tools/prompts/resources from configured MCP servers. Use when working with MCP integrations, need to discover available MCP capabilities, filter MCP tools for specific tasks, execute MCP tools programmatically, access MCP prompts/resources, or implement MCP client functionality. Supports intelligent tool selection, multi-server management, and context-efficient capability discovery.

Create evidence-based medical newsletters for interventional cardiologists in Eric Topol's authoritative Ground Truths style. Use when the user wants to analyze trending medical topics with engagement predictions, conduct data-driven topic selection, research medical literature using PubMed, or write comprehensive well-referenced newsletters that build professional authority. Handles complete workflow from trend analysis to final draft.

Unified writing system with intelligent model routing. Default: Claude. Options: GLM-4.7 (cheapest), GPT-4o/mini, Gemini, Grok. Includes browser automation for web interfaces. Cost-aware routing based on task complexity.

Programmatic social media and marketing research using free tools: pytrends (Google Trends), yars (Reddit without API keys), and Perplexity MCP (Twitter/TikTok/Web). Use when finding trending topics in a niche, tracking keyword velocity and volume, monitoring Reddit discussions, discovering what's going viral, or researching content opportunities before writing. Zero-cost research stack with built-in rate limiting. Complements content-marketing-social-listening skill with executable code.

Write data-driven, evidence-first long-form Twitter posts on medicine and cardiology. Use when the user wants to: (1) Create thought leadership content in the style of Eric Topol, Peter Attia, Andrew Huberman, or Rhonda Patrick, (2) Present clinical evidence with charts, data, and Q1 journal citations for educated non-specialist audiences, (3) Write confident, matter-of-fact medical content that is rigorous without being inaccessible, (4) Explain trials, drugs, or medical phenomena using data visualization and systematic evidence review, (5) Build authority through methodological rigor and clear conclusions backed by evidence. NOT for newsletters or Substack. For Twitter long-form posts only.

Analyzes medical education content ideas from PDFs/DOCX files to predict viral potential (views, likes, shares, AVD) and provides actionable insights. Use when the user needs to evaluate content ideas for engagement potential, get predictive analytics on video performance, research trending topics in medical/health niches, analyze YouTube comments and audience sentiment, identify knowledge gaps and myths, or prioritize content ideas based on viral likelihood.

Create scientifically rigorous, engaging X (Twitter) posts for cardiology thought leadership. Use when generating social media content for a cardiologist targeting patients, caregivers, health optimizers, people with lifestyle diseases (hypertension, diabetes, cholesterol), and sedentary individuals seeking prevention. Produces batches of 10 unique posts using strategic combinations of 300+ cardiology seed ideas, 215+ modifiers, 5 audience archetypes, awareness levels, and proven copywriting frameworks (4A, Magical Multipliers). Features self-improvement through accumulated feedback.

Unified YouTube script creation for cardiology channels in Hinglish. Uses the COMPLETE research-engine pipeline (channel scraping, comment analysis, narrative monitoring, gap finding, view prediction) combined with RAG + PubMed for evidence. Data-driven topic selection, 15-30 min educational videos with 6-point voice check.