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Env And Assets Bootstrap

  • 176k installs
  • 512 repo stars
  • Updated July 26, 2026
  • lllllllama/rigorpilot-skills

This is a copy of env-and-assets-bootstrap by lllllllama - installs and ranking accrue to the original listing.

env-and-assets-bootstrap is a RigorPilot setup skill that prepares conda environments and asset dependencies for developers who need conservative, documented ML reproduction prerequisites before running research code.

About

env-and-assets-bootstrap is the Rigor Setup mode in rigorpilot-skills for conservative environment and asset preparation before a reproduction run. The agent follows a four-level evidence order—README links and paths, config files and default arguments, code-level constants, then careful filename inference—to prepare checkpoints, datasets, and caches transparently. It uses a conda-first environment strategy and records source URLs or identifiers when downloading assets while avoiding mismatched assumptions. Developers reach for this skill after repo-intake-and-plan selects a target and before run-train or ai-research-reproduction execution steps. The goal is reproducible dependency and asset readiness grounded in repository documentation rather than speculative package installs.

  • Conservative conda-first environment setup for deep learning research repos
  • Follows strict order of evidence: README → config → code constants → filename inference
  • Prepares checkpoints, datasets, tokenizers, caches and output directories
  • Records every asset with source, target path and status (present, missing, downloaded, skipped, unknown)
  • Preserves original URLs and never claims unofficial files as canonical

Env And Assets Bootstrap by the numbers

  • 175,905 all-time installs (skills.sh)
  • +25,324 installs in the week ending Jul 28, 2026 (Skillselion tracking)
  • Security screen: LOW risk (skills.sh audit)
  • Data as of Jul 28, 2026 (Skillselion catalog sync)
npx skills add https://github.com/lllllllama/rigorpilot-skills --skill env-and-assets-bootstrap

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Listed on Skillselion
Installs176k
repo stars512
Security audit3 / 3 scanners passed
Last updatedJuly 26, 2026
Repositorylllllllama/rigorpilot-skills

How do you bootstrap ML environments and assets?

Prepare a reproducible conda environment and asset dependencies before running research code or reproduction targets.

Who is it for?

ML engineers about to run a README-documented reproduction who need transparent conda setup and asset preparation first.

Skip if: Developers who only need repository scanning, paper detail lookup, or training execution after the environment is already prepared.

When should I use this skill?

A reproduction target is selected and conda environment plus checkpoints, datasets, or caches must be prepared from documented sources.

What you get

Reproducible conda environment plus documented checkpoint, dataset, and cache assumptions

  • Prepared conda environment
  • Documented asset assumptions
  • Recorded download sources and cache paths

By the numbers

  • Uses a 4-level evidence order for environment and asset assumptions

Files

SKILL.mdMarkdownGitHub ↗

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.

Related skills

How it compares

Use env-and-assets-bootstrap after target selection; use run-train once the conda environment and assets are ready for command execution.

FAQ

What evidence order does env-and-assets-bootstrap follow?

env-and-assets-bootstrap follows four evidence levels: README links and paths, config files and default arguments, code-level constants or path joins, then careful inference from filenames. Rigor Setup prefers documented asset sources.

When should env-and-assets-bootstrap run in a reproduction workflow?

env-and-assets-bootstrap should run after a reproduction target is chosen and before training or full reproduction execution. It prepares conda environments plus checkpoint, dataset, and cache assumptions transparently.

Is Env And Assets Bootstrap safe to install?

skills.sh reports 3 of 3 security scanners passed. Review the Security Audits panel on this page before installing in production.

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