Maintainer of this skill?
Automated security scan — not a certification.
by aipoch
The open-source AI research workbench for scientific research and agent workflows. Local-first, model-agnostic desktop app with extensible skills, MCP tools and connectors, Python/R execution and traceable artifacts for reproducible research on macOS, Windows and Linux.
# Add to your Claude Code skills
git clone https://github.com/aipoch/open-scienceLast scanned: 7/20/2026
{
"issues": [],
"status": "PASSED",
"scannedAt": "2026-07-20T06:44:38.978Z",
"npmAuditRan": true,
"pipAuditRan": true,
"promptInjectionRan": true
}See how open-science compares with popular alternatives.
open-science is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by aipoch. The open-source AI research workbench for scientific research and agent workflows. Local-first, model-agnostic desktop app with extensible skills, MCP tools and connectors, Python/R execution and traceable artifacts for reproducible research on macOS, Windows and Linux. It has 5,229 GitHub stars.
Yes. open-science passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.
Clone the repository with "git clone https://github.com/aipoch/open-science" and add it to your Claude Code skills directory (see the Installation section above).
open-science is primarily written in TypeScript. It is open-source under aipoch on GitHub, so you can review or fork the full source.
Yes. SkillsLLM lists many other AI Agents skills you can browse and compare side by side. Open the AI Agents category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh open-science against similar tools.
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⚠️ Third-Party Software Notice
This skill is third-party open-source software developed and hosted independently on GitHub. SkillsLLM is an informational directory and does not control or maintain the underlying repository.
Any security checks, ratings, or warnings displayed by SkillsLLM are automated and limited in scope. They do not constitute a security certification or guarantee that the software is safe, error-free, or free from malicious code, vulnerabilities, compromised dependencies, or prompt-injection risks.
Review the source code, permissions, dependencies, and configuration before installing or running any third-party skill. Use is at your own risk. To the maximum extent permitted by applicable law, SkillsLLM is not liable for losses arising from third-party software.
Automated security scan — not a certification.
AIPOCH Open-Science is an AI research workbench for scientists and researchers, developed by AIPOCH with an open-source, local-first, model-agnostic approach. It enables reproducible, inspectable research with scientific AI agents, Python and R execution, scientific data connectors, and cross-platform support for macOS, Windows, and Linux. Create a project, describe your research goal in plain language, and let the agents read files, search the web, run code, query scientific data sources, and produce reports, tables, and figures with traceable provenance—all in one workspace.
AIPOCH Open-Science supports computational and data-intensive research across disciplines, including machine learning, statistics, life sciences, chemistry, materials science, physics and environmental science. It supports the research process from literature review and hypothesis development to code execution, data analysis, simulation, visualization, and the production of traceable research outputs.
Completed research sessions can also be exported as portable .science packages for review, handoff, and archiving, with selected conversation branches, file versions, Notebook records, and verification evidence.
💡 AIPOCH Open-Science v1.0.0 (updated October 2026). This milestone brings together the local-first research workspace, scientific agents, Python/R Notebook execution, literature tools, and portable
.scienceresearch packages. See the 1.0.0 release notes for changes since v0.37.2 and CLI upgrade changes. Downloads continue to follow the latest published release.
.science Research PackagesOpen the official download page and choose the installer for your computer:
| Your computer | Choose |
|---|---|
| macOS 12+ — Apple Silicon (M1 or newer) | The macOS DMG for Apple Silicon / ARM64 |
| macOS 12+ — Intel | The macOS DMG for Intel / x64 |
| Windows x64 | The Windows x64 installer (code-signed) |
| Linux x64 | The Linux x64 AppImage or Debian package |
| Linux ARM64 | The Linux ARM64 AppImage or Debian package |
Download from the official download page; see download verification if needed.
On macOS, you can also install with Homebrew:
brew install --cask open-science
For the standalone CLI on macOS or Linux, use the separate AIPOCH tap:
brew install aipoch/tap/open-science
The CLI includes Node and runs without the desktop app. See the standalone runtime guide for startup, prerequisites, shared profiles and upgrades.
Windows reinstalls preserve research data. For a full cleanup, see the data reset tool, which permanently deletes local data after confirmation.
Follow the setup wizard: Environment → Data location → Agent runtime → Model provider → Notebook runtime.
Complete the required environment and agent-runtime checks and test your model connection. Python/R Notebook setup is optional; Notebook and data-location settings can be changed later.
@ to reference project files or / to choose a skill.Screenshots in this README illustrate the workflow. Labels, catalogs, and other interface details may differ from the version you install.
Consider a representative bioinformatics task: reproduce a published differential-expression analysis, compare the regenerated results with the paper, and deliver the report, tables, and figures needed for review. The screenshots below are representative views from documented AIPOCH Open-Science workflows; they illustrate each stage rather than one continuous session.
Describe the research question, source paper and datasets, required methods or thresholds, expected outputs, and acceptance criteria. Upload supporting files or reference an existing project artifact with @, so the agent starts from explicit inputs instead of hidden context.
The agent can combine scientific skills, permissioned research connectors, searches, file operations, and Python or R code in the shared Notebook. Generated figures can be reviewed beside the research summary, while the artifact record exposes captured producer code and execution evidence for inspection.
The final response summarizes what reproduced, what differed, and which limitations matter. Generated Markdown reports, CSV tables, images, and other research artifa