env-and-assets-bootstrap

Habilidad de configuración Rigor para la reproducción de repositorios de deep learning con enfoque README-first. Úsala cuando la tarea sea específicamente preparar un entorno conservador basado en conda, suposiciones de rutas de checkpoints y datasets, sugerencias de ubicación de caché y notas de configuración antes de cualquier ejecución en un repositorio documentado con README. No la uses para escaneo de repositorios, orquestación completa, interpretación de artículos, informes de ejecución final ni configuración genérica de entorno que no esté vinculada a un objetivo de reproducción específico.

npx skills add https://github.com/lllllllama/rigorpilot-skills --skill 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 ../../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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