env-and-assets-bootstrap
🎯SkillA skill that plans the software setup and file locations you need before re-running a deep learning research project. It suggests install commands and where to put model and data files.
Installation
npx skills add lllllllama/rigorpilot-skills --skill env-and-assets-bootstrapRun it in a terminal; it asks which AI coding tools to install it for (Claude Code, Codex, Cursor and more).
Uses skills.sh · Node.js 22.20+
After installing
Ask for related work and the agent uses it on its own, or call it yourself.
/env-and-assets-bootstrapInstall by hand
Copy the whole skill folder from GitHub to the place below. Copying only SKILL.md can leave out files the skill uses.
Open the skill folder →- This project
.claude/skills/env-and-assets-bootstrap/ - All projects
~/.claude/skills/env-and-assets-bootstrap/
These paths are for Claude Code. For other tools, the command above puts it in the right place.
What this skill does
Overview
env-and-assets-bootstrap is the Rigor Setup skill for reproducing deep learning projects, which means re-running published research code to check its results. After a target run is chosen, it plans the environment (the software the code needs) and the assets (checkpoints, which are saved model files, plus datasets and cache folders). It plans carefully and uses conda, a tool that creates separate Python setups, as the first choice.
Key Features
- Get careful setup notes and conda commands you can try.
- Get a plan for where checkpoints, datasets, and cache folders should go.
- See hints on where the checkpoints and datasets may come from.
- See a list of missing packages or files that could still cause problems.
- Use helper scripts such as
scripts/bootstrap_env.py,scripts/plan_setup.py, andscripts/prepare_assets.py.
Who is this for?
This skill is for researchers who re-run a deep learning paper's code and want a careful setup plan for one specific run. It is not for general conda questions, a first scan of the repository, or final result reports.
Summarized by AI from this skill's SKILL.md. Suggest a better one
SKILL.md
View on GitHub →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.
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