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lllllllama/env-and-assets-bootstrap

Skill de configuración de Rigor para la reproducción de repositorios de deep learning priorizando README. Úsalo cuando la tarea sea preparar un entorno conservador basado en conda, supuestos de rutas de checkpoints y datasets, sugerencias de ubicaciones de caché y notas de configuración antes de ejecutar un repositorio documentado con README. No lo uses para escaneo de repositorios, orquestación completa, interpretación de artículos, informes de ejecución final o configuración genérica de entorno no vinculada a un objetivo de reproducción específico.

¿Qué es env-and-assets-bootstrap?

Este skill, llamado env-and-assets-bootstrap, está diseñado específicamente para preparar un entorno de reproducción conservador y basado en conda para repositorios de deep learning documentados con README. Su objetivo principal es establecer supuestos sobre rutas de checkpoints y datasets, proporcionar sugerencias de ubicaciones de caché y generar notas de configuración detalladas antes de cualquier ejecución real. Es útil para investigadores y desarrolladores que necesitan reproducir resultados de manera confiable, evitando configuraciones genéricas o tareas no relacionadas como escaneo de repositorios, interpretación de artículos o informes finales. Funciona analizando el README del repositorio objetivo y creando un entorno reproducible que minimice errores de dependencias y recursos.

Compatible con~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/lllllllama/ai-paper-reproduction-skill/tree/main/skills/env-and-assets-bootstrap

Installed? Explore more Investigación y análisis de datos skills: affaan-m/uspto-database, affaan-m/scholar-evaluation, affaan-m/literature-review · View all 6 →

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¿Qué hace env-and-assets-bootstrap?

Skill de configuración de Rigor para la reproducción de repositorios de deep learning priorizando README. Úsalo cuando la tarea sea específicamente preparar un entorno conservador basado en conda, supuestos de rutas de checkpoints y datasets, sugerencias de ubicaciones de caché y notas de configuración antes de cualquier ejecución en un repositorio documentado con README. No lo uses para escaneo de repositorios, orquestación completa, interpretación de artículos, informes de ejecución final o configuración genérica de entorno no vinculada a un objetivo de reproducción específico.

Individual skills in this repo

This repo contains 4 individual skills — each has its own dedicated page.

lllllllama/ai-paper-reproduction

Main orchestrator for README-first AI repo reproduction. Use when the user wants an end-to-end, minimal-trustworthy reproduction flow that reads the repository first, selects the smallest documented inference or evaluation target, coordinates intake, setup, trusted execution, optional trusted training, optional repository analysis, and optional paper-gap resolution, enforces conservative patch rules, records evidence assumptions deviations and human decision points, and writes the standardized `repro_outputs/` bundle. Do not use for paper summary, generic environment setup, isolated repo scanning, standalone command execution, silent protocol changes, or broad research assistance outside repository-grounded reproduction.

lllllllama/minimal-run-and-audit

RigorPilot trusted execution and reporting skill for README-first deep learning repo reproduction. Use when the task is specifically to capture or normalize evidence from the selected smoke test or documented inference or evaluation command and write standardized `repro_outputs/` files, including patch notes when repository files changed. Do not use for training execution, initial repo intake, generic environment setup, paper lookup, target selection, hidden scientific-meaning changes, or end-to-end orchestration by itself.

lllllllama/paper-context-resolver

Rigor Paper Context helper for README-first deep learning repo reproduction. Use only when the README and repository files leave a narrow reproduction-critical gap and the task is to resolve a specific paper detail such as dataset split, preprocessing, evaluation protocol, checkpoint mapping, or runtime assumption from primary paper sources while recording conflicts. Do not use for general paper summary, repo scanning, environment setup, command execution, title-only paper lookup, or replacing README guidance by default.

lllllllama/repo-intake-and-plan

Rigor Intake helper for README-first deep learning repo reproduction. Use when the task is specifically to scan a repository, read the README and common project files, extract documented commands, classify inference, evaluation, and training candidates, and return the smallest trustworthy reproduction plan to the main orchestrator. Do not use for environment setup, asset download, command execution, final reporting, paper lookup, or end-to-end orchestration.

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