菜单
Execute research code inside isolated Docker containers for safe replication, experiments, and benchmarks. Use when the user selects Docker as the execution environment or asks to run code safely, in isolation, or in a sandbox.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
Search, read, and query research papers via Feynman's alphaXiv-backed alpha tools. Use when the user asks about academic papers, wants to find research on a topic, needs to read a specific paper, ask questions about a paper, inspect a paper's code repository, or manage paper annotations.
Bounded research experiment loop that tries hypotheses, measures benchmark evidence, keeps what works, and records what fails. Use when the user asks to optimize a research metric, run an experiment loop, improve model/retrieval/evaluation performance iteratively, or benchmark a research hypothesis.
Explain research, papers, or technical ideas in plain English with minimal jargon, concrete analogies, and clear takeaways. Use when the user says "ELI5 this", asks for a simple explanation of a paper or research result, wants jargon removed, or asks what something technically dense actually means.
Inspect visible research run state, scheduled research follow-ups when available, and durable watch artifacts. Use when the user asks what's running for a research workflow or wants research-run status.
Run explicitly chosen research benchmark or replication jobs on Modal's serverless infrastructure. Use when a Feynman research workflow needs burst remote GPU compute and the Modal CLI is available.
Preview Markdown, LaTeX, PDF, or code artifacts when preview commands are visible, or fall back to shell/browser tools. Use when the user wants to review a written artifact, export a report, or view a rendered document.
| name | docker |
| description | Execute research code inside isolated Docker containers for safe replication, experiments, and benchmarks. Use when the user selects Docker as the execution environment or asks to run code safely, in isolation, or in a sandbox. |
| allowed-tools | Bash(docker:*) |
Run research code inside Docker containers while Feynman stays on the host. The container gets the project files, runs the commands, and results sync back.
/replicate or /autoresearchFor Python research code (most common):
docker run --rm -v "$(pwd)":/workspace -w /workspace python:3.11 bash -c "
pip install -r requirements.txt &&
python train.py
"
For projects with a Dockerfile:
docker build -t feynman-experiment .
docker run --rm -v "$(pwd)/results":/workspace/results feynman-experiment
For GPU workloads:
docker run --rm --gpus all -v "$(pwd)":/workspace -w /workspace pytorch/pytorch:latest bash -c "
pip install -r requirements.txt &&
python train.py
"
| Research type | Base image |
|---|---|
| Python ML/DL | pytorch/pytorch:latest or tensorflow/tensorflow:latest-gpu |
| Python general | python:3.11 |
| Node.js | node:20 |
| R / statistics | rocker/r-ver:4 |
| Julia | julia:1.10 |
| Multi-language | ubuntu:24.04 with manual installs |
For iterative experiments (like /autoresearch), create a named container instead of --rm. Choose a descriptive name based on the experiment:
docker create --name <name> -v "$(pwd)":/workspace -w /workspace python:3.11 tail -f /dev/null
docker start <name>
docker exec <name> bash -c "pip install -r requirements.txt"
docker exec <name> bash -c "python train.py"
This preserves installed packages across iterations. Clean up with:
docker stop <name> && docker rm <name>
--network none for full isolation