Skills lllllllama/rigorpilot-skills RigorPilot

env-and-assets-bootstrap

Use it when: a reproduction target is chosen and the environment, datasets and model checkpoints must be ready before the first run
It does: plans a conda environment from the README and repo files, and works out where checkpoints, datasets and caches should go
You get: setup notes, candidate conda commands, an asset list with sources, paths and status, and the risks still open
Comes between: RigorPilot's repo-mapping step (repo-intake-and-plan) and its first run (minimal-run-and-audit)

Skill 3 of 5 in the RigorPilot bundle · Before it: paper-context-resolver · After it: minimal-run-and-audit

TLDR

The setup step in RigorPilot, a set of skills that reproduces deep-learning research code by following its README. For the environment it trusts, in order, the README's instructions, the repo's environment files, package metadata, then careful guesses from imports. It reuses an existing environment.yml, otherwise translates the requirements into a simple conda-plus-pip setup, avoids aggressive upgrades and records version doubts. A script that comes with the skill can create the environment, or just print the commands. For assets it finds checkpoints, datasets and caches through README links, configs and code, sticks to documented sources, and records each one's source, local path and status: present, missing, downloaded, skipped or unknown. It targets Linux first and supports Windows and macOS where practical; containers, cluster schedulers and custom CUDA builds are out of scope.

Summarizes the skill at fb3ccdf.

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

skills.sh page · Source on GitHub · All skills from lllllllama/rigorpilot-skills

Installs: 450,018 on skills.sh, Sep 21, 2026
Repo stars: 492 lllllllama/rigorpilot-skills
First seen: Mar 30, 2026 on skills.sh

Weekly installs

-100% over 8 weeks · week to Sep 21

Weekly values

Week to	Installs
Aug 3	11,166
Aug 10	32,229
Aug 17	50,245
Aug 24	41,208
Aug 31	41
Sep 7	29
Sep 14	26
Sep 21	30

Category	category:science Research reproduction, financial data, charts and diagrams.
Type	type:setup One-time configuration or scaffolding of a project, account or tool.
Effect	effect:local Its own steps create or change files, git state or local tools.
Needs	needs:cli A specific command-line tool or desktop app must be installed, such as gh, agent-browser, LibreOffice, Obsidian or a vendor CLI.
Publisher	by:community Everyone else. Unverified vendor claims default to community.
Topics	ml-research
Stack	stack:python

SKILL.md

From lllllllama/rigorpilot-skills at fb3ccdf. Checked daily, last changed Oct 20, 2026.

Frontmatter

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.