skills/43-wentorai-research-plugins/skills/tools/code-exec/SKILL.md
7 code execution skills. Trigger: running code, interactive notebooks, Jupyter, Colab, sandboxed execution. Design: execution environment guides with setup instructions and best practices.
npx skillsauth add brycewang-stanford/Awesome-Agent-Skills-for-Empirical-Research code-exec-skillsInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Select the skill matching the user's need, then read its SKILL.md.
| Skill | Description | |-------|-------------| | google-colab-guide | Run and manage Google Colab notebooks for Python and ML research | | jupyter-notebook-guide | Best practices for computational research notebooks with reproducible workflows | | kaggle-api-guide | Download datasets, manage competitions and notebooks via Kaggle API | | overleaf-cli-guide | Sync and manage Overleaf LaTeX projects from the command line | | python-reproducibility-guide | Reproducible Python environments, notebooks, and literate programming | | r-reproducibility-guide | Create reproducible research workflows with R and RMarkdown/Quarto | | sandbox-execution-guide | Secure sandboxed code execution environments for reproducible research computing |
tools
Autonomous multi-round research review loop using MiniMax API. Use when you want to use MiniMax instead of Codex MCP for external review. Trigger with "auto review loop minimax" or "minimax review".
tools
Autonomous research review loop using any OpenAI-compatible LLM API. Configure via llm-chat MCP server or environment variables. Trigger with "auto review loop llm" or "llm review".
tools
Autonomous multi-round research review loop using MiniMax API. Use when you want to use MiniMax instead of Codex MCP for external review. Trigger with "auto review loop minimax" or "minimax review".
tools
Autonomous research review loop using any OpenAI-compatible LLM API. Configure via llm-chat MCP server or environment variables. Trigger with "auto review loop llm" or "llm review".