Agent skill
research-paper-extractor
Install this agent skill to your Project
npx add-skill https://github.com/drshailesh88/integrated_content_OS/tree/main/skills/cardiology/research-paper-extractor
SKILL.md
Research Paper Extractor
Extract text from cardiology research paper PDFs - FREE, runs locally.
Cost: ZERO
- Text extraction:
pdfplumber(free, local) - Structuring: You ask me (Claude) in this conversation - you're already paying for the subscription
No API calls. No extra costs.
How It Works
STEP 1: Extract text (free, local)
python scripts/extract_paper.py trial.pdf --output trial.md
STEP 2: Ask Claude (your existing subscription)
"Read trial.md and structure this for my content workflow"
DONE - No extra cost.
Quick Start
Install (one time)
pip3 install pdfplumber
Extract text from PDF
# Save to file
python scripts/extract_paper.py paper.pdf --output extracted.md
# Just first 5 pages (faster)
python scripts/extract_paper.py paper.pdf --pages 5 --output extracted.md
Then ask Claude Code
After extracting, just tell me:
"Read /path/to/extracted.md and give me:
- Study design, population, intervention
- Primary/secondary endpoints with HR, CI, p-values
- Safety data and conclusions
- Content angles for YouTube, Twitter, Newsletter"
I'll structure it for your content workflow.
Example Workflow
# 1. Download PDF from NEJM/JACC/Lancet
# 2. Extract text
python scripts/extract_paper.py ~/Downloads/declare-timi-58.pdf --output declare.md
# 3. In Claude Code:
# "Read declare.md and structure the trial data.
# Give me content angles for my YouTube channel."
Output you'll get from me:
DECLARE-TIMI 58 Summary:
Study: RCT, N=17,160, T2DM with CV risk
Intervention: Dapagliflozin 10mg vs placebo
Duration: 4.2 years median follow-up
Primary (MACE): HR 0.93 (0.84-1.03), p=0.17 - Non-inferior, not superior
Key Secondary (CV death/HF hosp): HR 0.83 (0.73-0.95), p=0.005 ✓
Content Angles:
🎬 YouTube: "SGLT2 inhibitors: The HF story hidden in a 'negative' trial"
🐦 Twitter: "DECLARE: Primary endpoint NS, but NNT 111 for HF hosp. Bury the lede much?"
📧 Newsletter: "Why 'negative' trials often have positive stories"
Why This Approach?
| Approach | Cost |
|---|---|
| ❌ Anthropic API per extraction | ~$0.05-0.15 per paper |
| ❌ OpenAI API per extraction | ~$0.05-0.20 per paper |
| ✅ This approach | $0 - uses your subscription |
You're already paying for Claude Code. Use it.
Integration with Your Skills
After I structure the data, you can use it with:
cardiology-trial-editorial→ Write 500-word editorialx-post-creator-skill→ Generate tweets with accurate statsyoutube-script-master→ Script with verified datacardiology-newsletter-writer→ Deep dive newsletter
Limitations
- Works best with native PDFs (not scanned images)
- Very long papers: use
--pages 10to extract key sections - Tables may need manual review
Zero cost. Maximum utility. Uses what you already pay for.
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