ai-research-explore
Rigor Explore compatible skill slug for meaningful and potentially novel deep learning research candidates. Use when the…
@lllllllamaInstall SkillDevelopment
Skill
/env-and-assets-bootstrap
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
Claude Code
$ npx -y skills add lllllllama/rigorpilot-skills --skill env-and-assets-bootstrap --agent claude-code
Codex
$ npx skills add lllllllama/rigorpilot-skills --skill env-and-assets-bootstrap -a codex -y
Cursor
$ npx skills add lllllllama/rigorpilot-skills --skill env-and-assets-bootstrap -a cursor -y
Gemini CLI
$ npx skills add lllllllama/rigorpilot-skills --skill env-and-assets-bootstrap -a gemini-cli -y
GitHub Copilot
$ npx skills add lllllllama/rigorpilot-skills --skill env-and-assets-bootstrap -a github-copilot -y
OpenCode
$ npx skills add lllllllama/rigorpilot-skills --skill env-and-assets-bootstrap -a opencode -y
Windsurf
$ npx skills add lllllllama/rigorpilot-skills --skill env-and-assets-bootstrap -a windsurf -y
SKILL.md
env-and-assets-bootstrap.SKILL.mdname: 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.
Read more
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.
Stats
486
Stars
20
Forks
Maintained
Maintenance
Python
Language
MIT
License
Last commit
Created
Ships withrigorpilot-skills
Run research repositories from their README, with bounded execution and auditable evidence. RigorPilot adds section-level results without rewriting the original README. Trusted reproduction is the default; candidate exploration requires explicit authorization.
48611 skills
Stars on GitHub
Momentum
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- 486
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Questions about env-and-assets-bootstrap.
- Which coding agents can use env-and-assets-bootstrap?
- env-and-assets-bootstrap installs in Claude Code, Codex, Cursor, Gemini CLI, GitHub Copilot, OpenCode and Windsurf with the skills CLI, using each agent's flag shown on this page. Flowy's auto-invocation, which fires a skill when your prompt matches, runs in Claude Code only.