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env-and-assets-bootstrap

env-and-assets-bootstrap

github.com/lllllllamaData & databases486478.8k installsUpdated 5w ago
Repository

About env-and-assets-bootstrap

Rigor Setup is a skill for AI research reproduction that plans environment and asset preparation once a reproduction target is chosen. It is intended for users who need conda environments, checkpoints, datasets, or cache directories set up before any run, with a conservative setup approach.

Key features

  1. 01Conservative environment setup notes
  2. 02Candidate conda commands
  3. 03Asset path planning
  4. 04Checkpoint and dataset source hints
  5. 05Unresolved dependency and asset risks

Use cases

  1. 01Preparing a conda environment for a research repo before reproduction
  2. 02Planning storage paths for checkpoints, datasets, and caches
  3. 03Listing unresolved setup risks before running commands

Install

Copy the command and run it in a terminal.

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

After installing, type/env-and-assets-bootstrapin Claude Code to confirm it loaded.

Bundled files

References and scripts installed along with SKILL.md.

agents/
openai.yaml
references/
assets-policy.md
env-policy.md
scripts/
bootstrap_env.py
bootstrap_env.sh
plan_setup.py
prepare_assets.py

Details

From the project README

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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