Agent skill

cardiology-content-repurposer

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.

Stars 2
Forks 0

Install this agent skill to your Project

npx add-skill https://github.com/drshailesh88/integrated_content_OS/tree/main/skills/cardiology/cardiology-content-repurposer

SKILL.md

Cardiology Content Repurposer

Overview

Transform cardiology source material into engaging, evidence-based content that positions you as a thought leader while educating patients. Maintains clinical authority with conversational approachability.

When to Use This Skill

Use when the user provides:

  • YouTube video transcripts about cardiology topics
  • Medical newsletters or articles
  • Knowledge from books/PDFs
  • Any long-form medical content that needs repurposing for patient education

Core Workflow

Step 1: Analyze Source Material

Silently extract:

  • Key points, themes, statistics, stories
  • Clinical insights and evidence
  • Multiple angles and subtopics
  • Opportunities for different formats and archetypes

Step 2: Review Guidelines

Before writing, review:

  • references/voice-and-principles.md for authentic cardiologist voice, audience archetypes, and awareness levels
  • references/twitter-writing-guide.md for 4A framework, headline structures, and thread formatting
  • references/content-formats.md for specific requirements of each output type

Step 3: Generate Content

Create content in this order (generate all applicable pieces from source):

  1. Short Newspaper Articles (Inshorts style)

    • Multiple pieces, <400 chars each
    • See content-formats.md for specs
  2. Atomic Essays

    • Multiple pieces, 600-700 chars
    • Use 4A framework for different angles
    • See content-formats.md for specs
  3. Tweets (Single)

    • Multiple punchy tweets, 280 chars max
    • Thought leadership, not random quotes
    • See content-formats.md for specs
  4. Twitter Threads

    • Multiple threads, 4-12 tweets each
    • Apply skimmability rhythms from twitter-writing-guide.md
    • See content-formats.md for structure options
  5. Blogs (Medium-style)

    • 800-2000 words, in-depth
    • Critical: If source is transcript/script without references, use PubMed to cite evidence
    • See content-formats.md for citation requirements

Step 4: Apply Quality Standards

For every piece:

  • Voice: Write as experienced interventional cardiologist with first-person authority
  • No em-dashes: Avoid — unless absolutely necessary
  • Natural language: Vary sentence structure, avoid AI patterns
  • Audience fit: Only create content for archetypes where topic genuinely fits
  • Awareness match: Only write for awareness levels that make sense for the topic
  • No dumbing down: Audience isn't medical but isn't dumb

Step 5: PubMed Integration (for Blogs)

When source material is transcript/script without solid references:

  1. Identify factual claims needing backing
  2. Use PubMed:search_articles to find supporting evidence
  3. Use PubMed:get_article_metadata for details
  4. Cite naturally: [Study Name, Journal Year]
  5. Focus on RCTs, meta-analyses, major trials

Step 6: Present Output

List all generated content numbered by type:

SHORT NEWSPAPER ARTICLES
1. [Title]
   [Body]

2. [Title]
   [Body]

ATOMIC ESSAYS
1. [Title]
   [Essay]

2. [Title]
   [Essay]

TWEETS
1. [Tweet]
2. [Tweet]

TWITTER THREADS
Thread 1: [Theme]
• Tweet 1: [Hook]
• Tweet 2: [Content]
• Tweet 3: [Content]
• Tweet 4: [CTA]

BLOGS
Blog 1: [Title]
[Full blog with sections and citations]

Content Multiplication Strategy

Use modifiers to create variations:

  • Tips, Stats, Steps, Lessons, Examples, Reasons, Mistakes, Questions, Stories, Benefits

Use 4A framework for angles:

  • Actionable: "Here's how" (step-by-step)
  • Analytical: "Show me numbers" (data-driven)
  • Aspirational: "Make me believe" (stories)
  • Anthropological: "Explain why" (psychology)

Critical Reminders

  • Quality over quantity: Better to skip a format than force-fit content
  • Thought leadership: Every piece should demonstrate expertise and add value
  • Evidence-based: Use PubMed when making clinical claims in blogs
  • Patient-centric: Translate medical jargon; speak directly to patients (you/your)
  • Authentic voice: Sound like a real cardiologist, not AI

If No Source Provided

Politely ask: "Please provide the source material you'd like me to repurpose (transcript, newsletter, PDF, etc.)"

Iteration

If user requests changes, revise specifically and re-present. Only proceed on explicit 'proceed' or equivalent.

Expand your agent's capabilities with these related and highly-rated skills.

drshailesh88/integrated_content_OS

pufferlib

This skill should be used when working with reinforcement learning tasks including high-performance RL training, custom environment development, vectorized parallel simulation, multi-agent systems, or integration with existing RL environments (Gymnasium, PettingZoo, Atari, Procgen, etc.). Use this skill for implementing PPO training, creating PufferEnv environments, optimizing RL performance, or developing policies with CNNs/LSTMs.

2 0
Explore
drshailesh88/integrated_content_OS

fluidsim

Framework for computational fluid dynamics simulations using Python. Use when running fluid dynamics simulations including Navier-Stokes equations (2D/3D), shallow water equations, stratified flows, or when analyzing turbulence, vortex dynamics, or geophysical flows. Provides pseudospectral methods with FFT, HPC support, and comprehensive output analysis.

2 0
Explore
drshailesh88/integrated_content_OS

metabolomics-workbench-database

Access NIH Metabolomics Workbench via REST API (4,200+ studies). Query metabolites, RefMet nomenclature, MS/NMR data, m/z searches, study metadata, for metabolomics and biomarker discovery.

2 0
Explore
drshailesh88/integrated_content_OS

geniml

This skill should be used when working with genomic interval data (BED files) for machine learning tasks. Use for training region embeddings (Region2Vec, BEDspace), single-cell ATAC-seq analysis (scEmbed), building consensus peaks (universes), or any ML-based analysis of genomic regions. Applies to BED file collections, scATAC-seq data, chromatin accessibility datasets, and region-based genomic feature learning.

2 0
Explore
drshailesh88/integrated_content_OS

zinc-database

Access ZINC (230M+ purchasable compounds). Search by ZINC ID/SMILES, similarity searches, 3D-ready structures for docking, analog discovery, for virtual screening and drug discovery.

2 0
Explore
drshailesh88/integrated_content_OS

astropy

Comprehensive Python library for astronomy and astrophysics. This skill should be used when working with astronomical data including celestial coordinates, physical units, FITS files, cosmological calculations, time systems, tables, world coordinate systems (WCS), and astronomical data analysis. Use when tasks involve coordinate transformations, unit conversions, FITS file manipulation, cosmological distance calculations, time scale conversions, or astronomical data processing.

2 0
Explore

Didn't find tool you were looking for?

Be as detailed as possible for better results