env-and-assets-bootstrap
Skill495 starsChanged 6 months agoMITView on GitHub
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.
In this repository11 other files it gives its agents.
- AGENTS.mdAGENTS.md
- ai-research-exploreSkill
- ai-research-reproductionSkill
- analyze-projectSkill
- explore-codeSkill
- explore-runSkill
- minimal-run-and-auditSkill
- paper-context-resolverSkill
- repo-intake-and-planSkill
- run-trainSkill
- safe-debugSkill
The file
We split this skill into the rules it gives an agent
12 rules. About 0 are common, 8 are distinctive, and 4 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈8
Distinctive · estimate
This author's own choices. Worth a look.
4
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 8
Use this as the Rigor Setup skill.
- Very specific: line 11
Use the shared operating principles in ../ai-research-reproduction/references/agent-operating-principles.md.
- Distinctive: line 17
After repo intake identifies a credible reproduction target.
- Distinctive: line 18
When environment creation or asset path preparation is needed before running commands.
- Distinctive: line 19
When the repo depends on checkpoints, datasets, or cache directories.
- Distinctive: line 20
When the user explicitly wants setup help before any run attempt.
- Distinctive: line 24
When the repository already ships a ready-to-run environment that does not need translation.
- Distinctive: line 25
When the task is only to scan and plan.
- Distinctive: line 26
When the task is only to report results from commands that already ran.
- Distinctive: line 27
When the request is a generic conda or package-management question outside repo reproduction.
- Very specific: line 53
Use references/env-policy.md, references/assets-policy.md, scripts/bootstrap_env.py, scripts/plan_setup.py, and scripts/prepare_assets.py.
- Very specific: line 54
Use scripts/bootstrap_env.sh only as a POSIX wrapper around the Python bootstrapper when a shell entrypoint is more convenient.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
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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