synthetic-sciences/openscience is an AI workbench that carries out scientific research by reading papers, forming hypotheses, writing and running code, conducting experiments, analyzing results, and preparing reports. Researchers use it for work in machine learning, biology, physics, and chemistry with remote or local models. Catalogue add-ons extend its scientific workflows through skills and instructions.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add synthetic-sciences/openscience --skill denariogit clone --depth 1 https://github.com/synthetic-sciences/openscienceWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/synthetic-sciences/openscience/denario)<a href="https://agentmods.dev/skills/synthetic-sciences/openscience/denario"><img src="https://agentmods.dev/badge/skills/synthetic-sciences/openscience/denario/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/synthetic-sciences/openscience/denario"><img src="https://agentmods.dev/badge/skills/synthetic-sciences/openscience/denario.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00069 | $0.01283 |
| Opus 5.5 | $0.00028 | $0.00513 |
| Sonnet 5.5 | $0.00014 | $0.00257 |
| Haiku 4.5 | $0.00007 | $0.00128 |
Grade A, and why
denario scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
How it starts
The opening of the file, as written. The whole thing — 221 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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):
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.
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/llm_configuration.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:
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. Alternatively, provide a custom idea:
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago Changed · +6 lines acea416e0e32
- 1mo ago First seen · 215 lines · 69 tokens per session scan A be7e618ec0a8
denario is a skill published in the GitHub repository synthetic-sciences/openscience (4,121 stars, last pushed 2d ago), licensed Apache-2.0. It adds 69 tokens to every session and 1,283 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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