claude-scientific-writer

Claude Scientific Writer

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A deep research and writing tool that combines the power of AI-driven deep research with well-formatted written outputs. Generate publication-ready scientific papers, reports, posters, grant proposals, literature reviews, and more academic documents—all backed by real-time literature search and verified citations.

Scientific Writer performs comprehensive research before writing, ensuring every claim is supported by real, verifiable sources. Features include real-time research lookup and web search powered by the Parallel Chat API, intelligent paper detection, comprehensive document conversion, and AI-powered diagram generation with Nano Banana Pro. You have the option of using it as a claude code plugin, python package or a native CLI

Quick Start

Prerequisites

• Python 3.10-3.12
• ANTHROPIC_API_KEY (required)
• PARALLEL_API_KEY (required for research lookup, web search, and deep research)
• OPENROUTER_API_KEY (optional, for AI image generation: schematics, figures, slides, and infographics)

Installation Options

Option 1: Claude Code Plugin (Recommended) ⭐

The easiest way to use Scientific Writer is as a Claude Code plugin. See the Plugin Installation section below.

Option 2: Install from PyPI (CLI/API Usage)

pip install scientific-writer

Option 3: Install from source with uv

git clone https://github.com/K-Dense-AI/claude-scientific-writer.git
cd claude-scientific-writer
uv sync

Configure API keys

# .env file (recommended) — see .env.example for the full list
echo "ANTHROPIC_API_KEY=your_key" > .env
echo "PARALLEL_API_KEY=your_parallel_key" >> .env            # research lookup and web search
echo "OPENROUTER_API_KEY=your_openrouter_key" >> .env        # optional: AI image generation
# or export in your shell
export ANTHROPIC_API_KEY='your_key'

Usage Options

Use as Plugin (Recommended)

After installing the plugin and running /claude-scientific-writer:scientific-writer-init, simply ask Claude:

> Create a Nature paper on CRISPR gene editing. Present experimental_data.csv 
  (efficiency across 5 cell lines), include Western_blot.png and flow_cytometry.png 
  showing 87% editing efficiency (p<0.001). Compare with literature benchmarks.

> Generate an NSF grant proposal presenting preliminary data from quantum_results.csv 
  (99.2% gate fidelity), circuit_topology.png, and error_rates.csv. 
  Include 5-year timeline with milestones_budget.xlsx.

> @research-lookup Find papers on mRNA vaccine efficacy (2022-2024). Compare 
  with our trial_outcomes.csv (n=500, 94% efficacy) and antibody_titers.png.

Use the CLI

# If installed via pip
scientific-writer

# If installed from source with uv
uv run scientific-writer

Use the Python API

import asyncio
from scientific_writer import generate_paper