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env-and-assets-bootstrap

by @lllllllamav
4.8(636)

Prepares the runtime environment and dependencies for AI paper reproduction, automatically detecting missing model checkpoints, datasets, and cache directories, supporting various cloud storage.

environment-setupautomationreproducibilitymodel-trainingGitHub
Installation
npx skills add lllllllama/ai-paper-reproduction-skill --skill env-and-assets-bootstrap
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Before / After Comparison

1
Before

Manually reading READMEs, identifying dependencies, installing Python packages, downloading checkpoints and datasets, and configuring paths. Setting up a research paper environment takes 2-4 hours, and it's easy to miss critical steps.

After

Automatically scanning repository dependencies, one-click environment installation, automatic download of missing resources, and intelligent path configuration. Environment setup only takes 10-20 minutes, standardized and reproducible.

SKILL.md

env-and-assets-bootstrap

env-and-assets-bootstrap

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.sh, and scripts/prepare_assets.py. Weekly Installs510Repositorylllllllama/ai-p…on-skillGitHub Stars1First SeenTodaySecurity AuditsGen Agent Trust HubPassSocketPassSnykPassInstalled onopencode510gemini-cli510deepagents510antigravity510github-copilot510codex510

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Installs127.5K
Rating4.8 / 5.0
Version
Updated2026年5月23日
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4.8(636)
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Timeline

Created2026年3月31日
Last Updated2026年5月23日