Env And Assets Bootstrap

# 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.