env-and-assets-bootstrap

Use this as the Rigor Setup skill. The installed slug remains env-and-assets-bootstrap for compatibility.

Use the shared operating principles in ../ai-research-reproduction/references/agent-operating-principles.md; this skill should keep setup planning conservative while leaving environment-specific judgment to the model.

When to apply

• After repo intake identifies a credible reproduction target.
• When environment creation or asset path preparation is needed before running commands.
• When the repo depends on checkpoints, datasets, or cache directories.
• When the user explicitly wants setup help before any run attempt.

When not to apply

• When the repository already ships a ready-to-run environment that does not need translation.
• When the task is only to scan and plan.
• When the task is only to report results from commands that already ran.
• When the request is a generic conda or package-management question outside repo reproduction.

Clear boundaries

• This skill prepares environment and asset assumptions.
• It does not own target selection.
• It does not own final reporting.
• It does not perform paper lookup except by forwarding gaps to the optional paper resolver.

Input expectations

• target repo path
• selected reproduction goal
• relevant README setup steps
• any known OS or package constraints

Output expectations

• conservative environment setup notes
• candidate conda commands
• asset path plan
• checkpoint and dataset source hints
• unresolved dependency or asset risks

Notes

Use references/env-policy.md, references/assets-policy.md, scripts/bootstrap_env.py, scripts/plan_setup.py, and scripts/prepare_assets.py. Use scripts/bootstrap_env.sh only as a POSIX wrapper around the Python bootstrapper when a shell entrypoint is more convenient.