env-and-assets-bootstrap

lllllllama/ai-paper-reproduction-skill

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 and asset path assumptions for deep learning paper reproduction tasks. Use it after identifying a credible reproduction target to prepare environment creation, checkpoint/dataset paths, and cache location hints before any run attempt.

Generate conservative conda environment setup notes and candidate commands
Plan asset paths for checkpoints, datasets, and cache directories
Identify checkpoint and dataset source hints from README documentation
Flag unresolved dependency or asset risks before execution
Translate repo-specific setup steps into environment assumptions

How to install env-and-assets-bootstrap

npx skills add https://github.com/lllllllama/ai-paper-reproduction-skill --skill env-and-assets-bootstrap

Prerequisites

Target repository path with README documentation
Identified reproduction goal or training task
Knowledge of OS and any package constraints

Claude Code

Cursor

Windsurf

Cline

How to use env-and-assets-bootstrap

1.Identify the target repository and reproduction goal
2.Provide the repo path and relevant README setup sections to the skill
3.Review the generated environment setup notes and candidate conda commands
4.Check the asset path plan for checkpoint and dataset locations
5.Address any flagged dependency or asset risks before proceeding to environment creation

Use cases

Good for

Preparing a conda environment for a PyTorch model reproduction before first run
Planning dataset and checkpoint directory structure for a multi-stage training pipeline
Identifying missing dependencies or conflicting package versions in a README-documented setup
Creating asset path assumptions when a repo references external checkpoints or datasets
Flagging environment risks (CUDA version mismatches, missing system dependencies) before attempting reproduction

Who it's for

ML researchers reproducing published papers
Deep learning engineers setting up complex training environments
Developers preparing reproducible research codebases
Teams standardizing environment setup across multiple reproduction targets

env-and-assets-bootstrap FAQ

Should I use this skill for generic conda setup questions?

No. This skill is specifically for README-documented deep learning repo reproduction. Use it only when preparing a specific reproduction target, not for generic package management.

Does this skill run commands or create environments?

No. It generates conservative setup notes, candidate commands, and asset path plans. You review and execute the recommendations.

What if the repo doesn't document its environment or assets?

The skill will flag these as unresolved risks and forward gaps to the optional paper resolver if available. Conservative assumptions will be made where possible.

Can this skill handle non-conda environments?

This skill is conda-first. For non-conda setups, it may provide limited guidance but is optimized for conda-based reproduction workflows.

Does this skill select which paper or repo to reproduce?

No. Target selection is outside this skill's scope. Provide an already-identified reproduction target and goal.

Full instructions (SKILL.md)

Source of truth, from lllllllama/ai-paper-reproduction-skill.

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 ../../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.