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description: Multiagent AI system automating the scientific research pipeline from hypothesis generation to publication-ready LaTeX manuscripts.
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category: Research
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requires: []
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examples:
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- Generate a novel research hypothesis for this climate dataset using Denario.
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- Write a journal-formatted LaTeX manuscript based on my recent computational results.
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---
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# Denario
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Denario is a multiagent AI system designed to automate scientific research workflows from initial data analysis through publication-ready manuscripts. Built on AG2 and LangGraph frameworks, it orchestrates multiple specialized agents to handle hypothesis generation, methodology development, computational analysis, and paper writing.
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## Instruction
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- Initialize the research project by defining the data context and available computational tools.
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- Orchestrate specialized agents to generate novel research hypotheses based on the data description.
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- Develop a structured methodology that outlines the computational experiments needed to test the hypothesis.
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- Execute the methodology to generate computational results, analysis, and visualizations.
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- Synthesize all findings into a journal-formatted LaTeX manuscript using specific styles like `Journal.APS`.
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- Ensure research reproducibility by maintaining structured outputs for every stage of the pipeline.
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## When to Use This Skill
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Use this skill when:
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- Writing journal-formatted LaTeX papers from research results
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- Automating the complete research pipeline from data to publication
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## Installation
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Install denario using uv (recommended):
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```bash
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uv init
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uv add "denario[app]"
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```
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Or using pip:
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```bash
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uv pip install "denario[app]"
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```
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For Docker deployment or building from source, see `references/installation.md`.
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## Output
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- A defined research hypothesis and a detailed methodology document.
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- Computational findings and visualizations ready for scientific interpretation.
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- A complete LaTeX source package and a formatted PDF of the final manuscript.
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## LLM API Configuration
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- OpenAI
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- Other LLM services compatible with AG2/LangGraph
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Store API keys securely using environment variables or `.env` files. For detailed configuration instructions including Vertex AI setup, see `references/llm_configuration.md`.
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Store API keys securely using environment variables or `.env` files.
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## Core Research Workflow
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Denario follows a structured four-stage research pipeline:
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Denario follows a structured four-stage research pipeline.
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### 1. Data Description
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Define the research context by specifying available data and tools:
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Define the research context by specifying available data and tools
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```python
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from denario import Denario
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den = Denario(project_dir="./my_research")
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den.set_data_description("""
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Available datasets: time-series data on X and Y
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Tools: pandas, sklearn, matplotlib
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Research domain: [specify domain]
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""")
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```
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### 2. Idea Generation
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Generate research hypotheses from the data description:
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Generate research hypotheses from the data description.
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```python
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den.get_idea()
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```
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This produces a research question or hypothesis based on the described data. Alternatively, provide a custom idea:
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This produces a research question or hypothesis based on the described data. Alternatively, provide a custom idea
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```python
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den.set_idea("Custom research hypothesis")
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```
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### 3. Methodology Development
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Develop the research methodology:
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Develop the research methodology
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```python
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den.get_method()
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```
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This creates a structured approach for investigating the hypothesis. Can also accept markdown files with custom methodologies:
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This creates a structured approach for investigating the hypothesis. Can also accept markdown files with custom methodologies
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```python
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den.set_method("path/to/methodology.md")
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```
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### 4. Results Generation
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Execute computational experiments and generate analysis:
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Execute computational experiments and generate analysis
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```python
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den.get_results()
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```
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This runs the methodology, performs computations, creates visualizations, and produces findings. Can also provide pre-computed results:
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This runs the methodology, performs computations, creates visualizations, and produces findings. Can also provide pre-computed results
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```python
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den.set_results("path/to/results.md")
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```
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### 5. Paper Generation
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Create a publication-ready LaTeX paper:
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```python
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from denario import Journal
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Create a publication-ready LaTeX paper
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den.get_paper(journal=Journal.APS)
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```
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The generated paper includes proper formatting for the specified journal, integrated figures, and complete LaTeX source.
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