Env And Assets Bootstrap
This is the conservative environment setup stage for reproducing deep learning repos from their READMEs. It translates setup instructions into conda environments, figures out where checkpoints and datasets should live, and surfaces any missing dependencies before you try to run anything. Use it after you've picked a reproduction target but before you start executing training or inference commands. It won't select targets for you, won't interpret papers, and won't report final results. The scope is narrow by design: prepare the environment and asset paths, flag what's missing, then hand off. If your repo already has a working environment or you're just doing generic conda troubleshooting, skip this.
- skills.sh installs
- 492.3K
- GitHub stars
- 486
- last updated
- Aug 21, 2026
Install
$ npx -y skills add lllllllama/rigorpilot-skills --skill env-and-assets-bootstrap --agent claude-codeInstalls into .claude/skills of the current project.
Files
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
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- Repository
- github.com/lllllllama/rigorpilot-skills
- Categories
- Documentation, Data Science & ML, Automation & Workflows
- First seen
- Jul 14, 2026
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