How do I install the Env And Assets Bootstrap skill?

Run npx skillmds add om-scogo/env-and-assets-bootstrap in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Env And Assets Bootstrap safe to use?

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Which AI agents work with Env And Assets Bootstrap?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Env And Assets Bootstrap free to use?

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Who published Env And Assets Bootstrap?

om-scogo (@om-scogo) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Env And Assets Bootstrap by om-scogo

Name: Env And Assets Bootstrap
Rating: 4.2 (5 reviews)
Author: om-scogo

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 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.

om-scogo 868039a 4 files · 7.4 KB Updated Sep 8, 2026 0 repo stars

File contents

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.

---name: env-and-assets-bootstrapdescription: 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-bootstrapUse 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 setupplanning 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## NotesUse `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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