env-and-assets-bootstrap
lllllllama/rigorpilot-skills
Prepare conda environments and asset paths for README-documented deep learning repo reproduction.
What is env-and-assets-bootstrap?
This skill sets up conservative conda-first environments, checkpoint and dataset path assumptions, cache location hints, and setup notes before running a specific reproduction target. Use it after identifying a credible deep learning repository and before attempting any execution.
- Generate conservative conda environment setup commands from README specifications
- Plan checkpoint and dataset directory paths and sourcing strategies
- Identify cache location hints and environment variable requirements
- Document unresolved dependency or asset risks
- Prepare asset acquisition steps without performing downloads
How to install env-and-assets-bootstrap
npx skills add https://github.com/lllllllama/rigorpilot-skills --skill env-and-assets-bootstrap- Target repository path and README documentation
- Identified reproduction goal or experiment to run
- Optional: known OS or package constraints
How to use env-and-assets-bootstrap
- 1.Provide the target repo path and reproduction goal
- 2.Share relevant README setup sections or environment specifications
- 3.Review the generated conda commands and asset path plan
- 4.Check identified risks and unresolved dependencies
- 5.Execute suggested setup steps or adjust for your environment
Use cases
- Setting up a conda environment for a paper's official repository before reproduction
- Planning dataset and checkpoint paths for a deep learning model before training
- Identifying missing dependencies or asset sources from README documentation
- Preparing environment assumptions for a multi-step research workflow
- Documenting setup risks before handing off to execution
- ML researchers reproducing published work
- Research engineers preparing environments for paper implementations
- Teams standardizing setup procedures across repositories
- Users needing conservative pre-flight checks before running unfamiliar codebases
env-and-assets-bootstrap FAQ
No. This skill is specific to preparing environments for documented repository reproduction targets, not general package management.
No. It plans paths and identifies sources; asset acquisition is a separate step.
This skill is not needed if the repository ships a complete, working environment that requires no translation.
No. It assumes a target is already selected and focuses only on environment and asset preparation.
The skill documents risks and gaps, which may be forwarded to a paper resolver or handled manually.
Full instructions (SKILL.md)
Source of truth, from lllllllama/rigorpilot-skills.
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.
Related skills
More from lllllllama/rigorpilot-skills and the wider catalog.
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explore-run
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minimal-run-and-audit
Execute and audit deep learning repo smoke tests with standardized evidence capture and scientific changelog tracking.
paper-context-resolver
Resolve reproduction-critical paper details when README and repo files leave gaps.
repo-intake-and-plan
README-first repository scanner for deep learning reproduction planning.
run-train
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