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env-and-assets-bootstrap

Precio sin confirmar★ 484 Estrellas de GitHubRegistro actualizado · 23 sept 2026agent-skill

Resumen

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.

Leer documentación completa

Documentación de origen, no instrucciones para este sitio. Revisa los permisos antes de ejecutar comandos.

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.

Ver fuente ↗

Metadatos del archivo

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.

Ver texto original

---
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
`../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.

Revisar el código fuente

Precio y costes de ejecución

Obtener el skill: Precio sin confirmar
Ejecutarlo: Requisitos sin confirmar. Consulta los costes del agente, API y servicios en la fuente.
Licencia: MIT
Precio sin confirmar: No hemos confirmado el precio. Los enlaces existentes al código y a la instalación siguen disponibles.

Obtener gratis no significa ejecutar gratis. El precio no es una evaluación de seguridad. Enviar información de precio →

La fuente requiere revisión

La fuente cambió o no pudo sincronizarse. Revísala antes de instalar.

Revisar antes de instalar: Evitar instalación automática

Licencia: MIT

Dependency or permission surface needs review
Permission surface may require sandboxing
The skill references an external shared operating principles file (`../ai-research-reproduction/references/agent-operating-principles.md`) that is not included in the skill directory; this could break if the parent repository structure changes.
The `bootstrap_env.py` script imports from `plan_setup` without ensuring that module is available in the same directory; this is fine within the skill but could be fragile if the skill is copied standalone.
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
Stars/forks activity: 484 stars, 16 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution

Abrir auditoría completa

Las herramientas son indicios de metadatos, no compatibilidad probada. Los prompts son sugerencias.

Empieza con una tarea pequeña

1Lee la fuente y confirma entradas, resultados, dependencias y permisos.
2Pide un plan al agente. Aprueba la configuración y los costes antes de probar en un entorno aislado.
3Comprueba resultados y archivos modificados. Informa solo de lo ejecutado y conserva la revisión de la fuente.

Consulta dependencias, claves API y costes externos en la fuente. Un repositorio público no implica servicios gratuitos.

Fuente y notas de uso

Indexado

Los metadatos y revisiones son orientativos. Popularidad, descubrimiento y ejecución correcta son hechos distintos.

Repositorio fuente: lllllllama/RigorPilot-Skills
Licencia: MIT
Versión: 1.0.0
Último push de GitHub: 6 sept 2026
Registro actualizado: 23 sept 2026
Ruta de instrucciones: skills/env-and-assets-bootstrap/SKILL.md @ 20b8c3ef2652

Versión declarada en el registro; consulta las versiones de la fuente.

Calidad

70/100

Sólido

Confianza

58/100

Do not auto-install

Auditoría

74/100

Requiere revisión

Abrir auditoría completa Ver informe de evaluación

Dependency or permission surface needs review
Permission surface may require sandboxing
The skill references an external shared operating principles file (`../ai-research-reproduction/references/agent-operating-principles.md`) that is not included in the skill directory; this could break if the parent repository structure changes.
The `bootstrap_env.py` script imports from `plan_setup` without ensuring that module is available in the same directory; this is fine within the skill but could be fragile if the skill is copied standalone.
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
Stars/forks activity: 484 stars, 16 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution

Verified installs: —
Resultados: —

Copiar no es instalar. Los recuentos requieren un informe de instalación correcta, no garantizan calidad general.

Acceso para agentes

La API Registry expone señales de decisión, confianza, auditoría, casos de uso e instalación sin raspar la interfaz.

Abrir API de instalación Formato de texto LLM Abrir Manifest Plan de resolución automática

Más detalles

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Creador: lllllllama
Fuente: lllllllama/RigorPilot-Skills
Indexado por: Índice comunitario de OpenAgentSkill

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Resumen

env-and-assets-bootstrap

When to apply

When not to apply

Clear boundaries

Input expectations

Output expectations

Notes

Revisar el código fuente

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Fuente y notas de uso

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