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.
Install with the open skills CLI (global, non-interactive — available in every Claude Code session):
npx skills add lllllllama/rigorpilot-skills --skill "env-and-assets-bootstrap" -g -a claude-code -yOr manually — clone and copy the skill directory (SKILL.md + companion files):
git clone --depth 1 https://github.com/lllllllama/rigorpilot-skills /tmp/rigorpilot-skills && cp -r /tmp/rigorpilot-skills/skills/env-and-assets-bootstrap ~/.claude/skills/env-and-assets-bootstrap-lllllllamaThis skill is a directory: SKILL.md is the entry point; the files below ship with it.
---
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.
Give the agent its own dedicated email inbox via AgentMail. Send, receive, and manage email autonomously using agent-owned email addresses (e.g. hermes-agent@agentmail.to).
Operate the Antigravity CLI (agy): plugins, auth, sandbox.
AudioCraft: MusicGen text-to-music, AudioGen text-to-sound.