Agent skill · research science · aiskillstore
denario
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.
Why this skill is useful
Adds executable scripts for automating the entire research workflow from data analysis to publication in LaTeX format.
What it needs
Requires denario installed locally. Requires google vertex ai, openai account access. About 3k tokens when loaded. Last updated 2026-08-07. 405 stars on the source repository.
What this skill does
Denario Overview 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. 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 denario using uv (recommended): Or using pip: For Docker deployment or building from source, see references/installation.md. LLM API Configuration Denario requires API keys from supported LLM providers. Supported providers include: Google Vertex AI OpenAI Other LLM services compatible with AG2/LangGraph Store API keys securely using environment variables or .env files. For detailed configuration instructions including Vertex AI setup, see references/llmconfiguration.md. Core Research Workflow Denario follows a structured four-stage research pipeline: 1. Data Description Define the research context by specifying available data and tools: 2. Idea Generation Generate research hypotheses from the data description: This produces a research question or hypothesis based on the described data. Alternatively, provide a custom idea: 3. Methodology Development Develop the research methodology: This creates a structured approach for investigating the hypothesis. Can also accept markdown files with custom methodologies: 4. Results Generation Execute computational experiments and generate analysis: This runs the methodology, performs computations, creates visualizations, and produces findings. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/denario