env-and-assets-bootstrap

env-and-assets-bootstrap

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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.

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更新于 6/16/2026
SKILL.md
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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.

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