9b1e04f128
- TUI header now shows CPU cores, RAM, GPU, and Docker availability - System prompt uses resource info to recommend execution environments - Docker skill for running experiment code in isolated containers - Renamed docker-sandbox skill to docker (Feynman stays on host, code runs in containers) - Updated README and website to cite Docker alongside Agent Computer Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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description, args, section, topLevelCli
| description | args | section | topLevelCli |
|---|---|---|---|
| Plan or execute a replication workflow for a paper, claim, or benchmark. | <paper> | Research Workflows | true |
Design a replication plan for: $@
Workflow
- Extract — Use the
researchersubagent to pull implementation details from the target paper and any linked code. - Plan — Determine what code, datasets, metrics, and environment are needed. Be explicit about what is verified, what is inferred, and what is still missing.
- Environment — Before running anything, ask the user where to execute:
- Local — run in the current working directory
- Virtual environment — create an isolated venv/conda env first
- Docker — run experiment code inside an isolated Docker container
- Cloud — delegate to a remote Agent Computer machine via
/delegate - Plan only — produce the replication plan without executing
- Execute — If the user chose an execution environment, implement and run the replication steps there. Save notes, scripts, and results to disk in a reproducible layout.
- Report — End with a
Sourcessection containing paper and repository URLs.
Do not install packages, run training, or execute experiments without confirming the execution environment first.