Parses a README, extracts environment and asset requirements, builds a conda env spec, and generates step-by-step setup notes so the repo actually runs without path errors or missing checkpoints.
Best for: Engineers reproducing a published deep learning project without wasting hours on dependency hell.
Creator's repository · lllllllama/RigorPilot-Skills
License: MIT
--- name: env-and-assets-bootstrap description: Rigor Setup skill for README-first deep learning repo reproduction. Use when the task is specifically to prepare a conservative conda-first environment, checkpoint and dataset path assumptions, cache location hints, and setup notes before any run on a README-documented repository. Do not use for repo scanning, full orchestration, paper interpretation, final run reporting, or generic environment setup that is not tied to a specific reproduction target. --- # 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 `../../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.