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Multiagent AI system for scientific research assistance that automates research workflows from data analysis to publication. This skill should be used when generating research ideas from datasets, developing research methodologies, executing computational experiments, performing literature searches, or generating publication-ready papers in LaTeX format. Supports end-to-end research pipelines with customizable agent orchestration.
Passed 23 of 23 test cases with the skill, and 5 of 23 without it.
and 23 more in the full report
18 fixed5 passed either way0 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 18 test cases the skill fixed. Same model, same prompt, with and without the skill.
When initializing a new research project on dataset analysis with Denario, what parameter name is passed to the Denario class constructor to designate the working directory?
| Checked | Without skill | With skill |
|---|---|---|
| Uses project_dir as the parameter keyword argument in Denario initialization | Missed | Passed |
The instructions your model reads when the skill loads.
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.
Use this skill when:
Install denario using uv (recommended):
uv init
uv add "denario[app]"
Or using pip:
uv pip install "denario[app]"
For Docker deployment or building from source, see references/installation.md.
Denario requires API keys from supported LLM providers. Supported providers include:
test cases passed, vs 5 / 23 without
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