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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.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
Fetch and analyze AlphaFold-predicted protein structures from the AlphaFold DB or EBI.
Autonomous experiment loop that tries ideas, measures results, keeps what works, and discards what doesn't. Use when the user asks to optimize a metric, run an experiment loop, improve performance iteratively, or automate benchmarking.
Search, read, and query research papers via the `alpha` CLI (bioRxiv-backed). 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.
Evaluate biomarker performance with ROC analysis, AUC, sensitivity, and specificity.
Monitor bioRxiv and medRxiv preprints for a topic, author, or keyword. Use for staying current with emerging biomedical research before peer review.
Search and summarize clinical trials from ClinicalTrials.gov and EU CTR. Use for drug development, competitive intelligence, and evidence synthesis.
| 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 Darwin 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 "$(pwd)":/workspace -w /workspace python:3.11 bash -c "
pip install -r requirements.txt &&
python train.py
"
For projects with a Dockerfile:
docker build -t darwin-experiment .
docker run --rm -v "$(pwd)/results":/workspace/results darwin-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