rl-env-from-description

rl-env-from-description is a Model training & evaluation skill for AI agents, published by adithya-s-k in the RL_Envs_101 repository on GitHub. Skillspace rates rl-env-from-description F (45/100) and the safety scan rated it risky; the rating is based on static analysis only.

Risky. This skill has patterns associated with unsafe behaviour. Read the source carefully before running it.

Turns a user's plain-English description of an RL training environment into runnable code across the four target frameworks — OpenEnv, OpenReward (ORS), Verifiers, and NeMo Gym. Use whenever someone describes an environment they want to build ("I want to train an agent that does X", "make an env where the model has to Y"), asks to scaffold a new env, asks to port an existing env to one of these frameworks, or asks how to design tools/rewards/state for a new env. Use even when the user does not explicitly say "RL environment" — descriptions like "agent that browses the web", "tool-calling agent for SQL", or "game-playing agent" all qualify. Drives the full flow — clarifying interview, env-name selection, shared-domain extraction, per-framework implementation, and rollout-based smoke tests.

Grade

F

Quality

45 / 100

Safety

Risky

GitHub stars

154

Unproven: rated by static analysis only, not yet compared head-to-head.

Our two independent classifiers differ on this skill's category, so treat the placement as provisional.

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