Compute Environment Setup
SkillProductivityLets your agent set up a ready-to-run Python or R research computing environment before starting a heavy job.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Compute Environment Setup skill
About this capability
Set up a reproducible Feynman compute environment for research jobs. Use when a task needs Python/R packages, GPU libraries, containers, Modal, SSH, caches, or managed model runtime setup.
What this skill tells your AI
The instructions your AI receives, as published by companion-inc/feynman in skills/compute-env-setup/SKILL.md and read by ahel’s review.
Use this skill before running a research job that needs a nontrivial runtime.
Workflow:
- Identify the job type, hardware need, data size, package stack, secrets, and expected artifacts.
- Choose the smallest working route: local notebook runtime, project virtualenv/conda, Modal, SSH/Slurm, or managed endpoint.
- Verify credentials and CLIs through Feynman Settings or environment status without printing secret values.
- Write an environment note containing package versions, install commands, cache paths, hardware, and failure modes.
- Run a tiny smoke job before the expensive job and save the smoke logs.
The done state is a recorded, reproducible environment plus a successful smoke or a precise missing dependency.
Signals
- GitHub stars
- 9k
- Forks
- 1k
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Gateway key
compute-env-setup- Source
- github.com/companion-inc/feynman