alterlab-denario

name	alterlab-denario
description	Runs Denario (AstroPilot-AI), a multiagent AI system for scientific research assistance that automates end-to-end research workflows from a described dataset through idea, methodology, computational results, and a publication-ready LaTeX paper. Built on AG2 + LangGraph with a cmbagent analysis backend. Use when driving the Denario pipeline (Denario.get_idea/get_method/get_results/get_paper), generating research ideas from a dataset description, auto-developing methodology, executing analysis agents, or emitting a journal-formatted (APS/AAS/JHEP/ICML/NeurIPS/PASJ) LaTeX manuscript. Part of the AlterLab Academic Skills suite.
license	GPL-3.0
allowed-tools	Read WebFetch Bash(uv:*) Bash(python:*)
compatibility	Requires the denario Python package (Python 3.12+) and at least an OPENAI_API_KEY (required for the analysis/results module). GOOGLE_API_KEY (Gemini), ANTHROPIC_API_KEY (Claude), and PERPLEXITY_API_KEY (citation search) are optional. LaTeX is needed to compile the paper. Needs network access.
metadata	{"skill-author":"AlterLab","version":"1.0.0"}

Denario

Overview

Denario (by AstroPilot-AI) is a multiagent AI system designed to automate scientific research workflows from a described dataset through publication-ready manuscripts. It implements agents with AG2 and LangGraph, using cmbagent as the research-analysis backend, to handle hypothesis generation, methodology development, computational analysis, and paper writing.

Source: https://github.com/AstroPilot-AI/Denario | Docs: https://denario.readthedocs.io | Paper: arXiv:2510.26887 (v1.0, Nov 2025).

When to Use This Skill

Use this skill when:

• Analyzing datasets to generate novel research hypotheses
• Developing structured research methodologies
• Executing computational experiments and generating visualizations
• Conducting literature searches for research context
• Writing journal-formatted LaTeX papers from research results
• Automating the complete research pipeline from data to publication

Installation

Install with uv (recommended). Quote the extra so zsh does not glob [app]:

uv init
uv add "denario[app]"

The [app] extra pulls in the Streamlit GUI (DenarioApp); omit it for headless/library use. For Docker deployment or building from source, see references/installation.md.

LLM API Configuration

On init, Denario reads provider keys from the environment via its KeyManager (no config object). The relevant variables:

• OPENAI_API_KEY — required (the analysis/results module needs it; OpenAI models are the cmbagent-mode defaults).
• GOOGLE_API_KEY — optional, a Gemini API key (the default LLM for the faster mode="fast" path). Note this is a plain Gemini key, not a Vertex AI service-account JSON.
• ANTHROPIC_API_KEY — optional (Claude).
• PERPLEXITY_API_KEY — optional, only for citation search.

Set them in the shell or a .env (loaded with python-dotenv before importing denario). Google Vertex AI is also supported as a backend; see references/llm_configuration.md for that and .env/Docker details.

Core Research Workflow

Denario follows a structured four-stage research pipeline:

1. Data Description

Define the research context by specifying available data and tools:

from denario import Denario

den = Denario(project_dir="./my_research")
den.set_data_description("""
Available datasets: time-series data on X and Y
Tools: pandas, sklearn, matplotlib
Research domain: [specify domain]
""")

2. Idea Generation

Generate research hypotheses from the data description:

den.get_idea()

This produces a research question or hypothesis based on the described data. get_idea() and get_method() take a mode argument: mode="fast" (default; LangGraph backend, faster but less reliable) or mode="cmbagent" (cmbagent backend, slower but more reliable). Alternatively, provide a custom idea:

den.set_idea("Custom research hypothesis")

3. Methodology Development

Develop the research methodology:

den.get_method()

This creates a structured approach for investigating the hypothesis. Can also accept markdown files with custom methodologies:

den.set_method("path/to/methodology.md")

4. Results Generation

Execute computational experiments and generate analysis:

den.get_results()

This runs the methodology, performs computations, creates visualizations, and produces findings. Can also provide pre-computed results:

den.set_results("path/to/results.md")

5. Paper Generation

Create a publication-ready LaTeX paper:

from denario import Journal

den.get_paper(journal=Journal.APS)

The generated paper includes proper formatting for the specified journal, integrated figures, and complete LaTeX source.

Available Journals

get_paper(journal=...) defaults to Journal.NONE (plain LaTeX, unsrt bibliography). The Journal enum (from denario import Journal) supports:

• Journal.NONE — generic LaTeX, no journal preset
• Journal.AAS — American Astronomical Society (e.g. ApJ)
• Journal.APS — American Physical Society (Physical Review, PRL, PRA, ...)
• Journal.ICML — International Conference on Machine Learning
• Journal.JHEP — Journal of High Energy Physics (incl. JCAP)
• Journal.NeurIPS — Conference on Neural Information Processing Systems
• Journal.PASJ — Publications of the Astronomical Society of Japan

Launching the GUI

Run the graphical user interface:

denario run

This launches a web-based interface for interactive research workflow management.

Common Workflows

End-to-End Research Pipeline

from denario import Denario, Journal

# Initialize project
den = Denario(project_dir="./research_project")

# Define research context
den.set_data_description("""
Dataset: Time-series measurements of [phenomenon]
Available tools: pandas, sklearn, scipy
Research goal: Investigate [research question]
""")

# Generate research idea
den.get_idea()

# Develop methodology
den.get_method()

# Execute analysis
den.get_results()

# Create publication
den.get_paper(journal=Journal.APS)

Hybrid Workflow (Custom + Automated)

# Provide custom research idea
den.set_idea("Investigate the correlation between X and Y using time-series analysis")

# Auto-generate methodology
den.get_method()

# Auto-generate results
den.get_results()

# Generate paper
den.get_paper(journal=Journal.APS)

Literature / Novelty Check

Use den.check_idea(mode="semantic_scholar") (or mode="futurehouse") to test whether an idea is original against existing literature before committing to method/results. See references/examples.md.

Detailed References

For comprehensive documentation:

• Installation options: references/installation.md
• LLM configuration: references/llm_configuration.md
• Complete API reference: references/research_pipeline.md
• Example workflows: references/examples.md

Troubleshooting

Common issues and solutions:

• API key errors: Ensure environment variables are set correctly (see references/llm_configuration.md)
• LaTeX compilation: Install TeX distribution or use Docker image with pre-installed LaTeX
• Package conflicts: Use virtual environments or Docker for isolation
• Python version: Requires Python 3.12 or higher