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>
23 lines
1.3 KiB
Markdown
23 lines
1.3 KiB
Markdown
---
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description: Plan or execute a replication workflow for a paper, claim, or benchmark.
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args: <paper>
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section: Research Workflows
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topLevelCli: true
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---
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Design a replication plan for: $@
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## Workflow
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1. **Extract** — Use the `researcher` subagent to pull implementation details from the target paper and any linked code.
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2. **Plan** — Determine what code, datasets, metrics, and environment are needed. Be explicit about what is verified, what is inferred, and what is still missing.
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3. **Environment** — Before running anything, ask the user where to execute:
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- **Local** — run in the current working directory
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- **Virtual environment** — create an isolated venv/conda env first
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- **Docker** — run experiment code inside an isolated Docker container
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- **Cloud** — delegate to a remote Agent Computer machine via `/delegate`
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- **Plan only** — produce the replication plan without executing
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4. **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.
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5. **Report** — End with a `Sources` section containing paper and repository URLs.
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Do not install packages, run training, or execute experiments without confirming the execution environment first.
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