generate-nemo-gym-env

generate-nemo-gym-env is a Model training & evaluation skill for AI agents, published by adithya-s-k in the RL_Envs_101 repository on GitHub. Skillspace rates generate-nemo-gym-env C (74/100) and it passed the safety scan; the rating is based on static analysis only.

Builds a NeMo Gym (NVIDIA) variant of an RL environment. Use whenever someone asks to scaffold a NeMo Gym Resources Server, port an existing env to NeMo Gym, expose tools as `app.post()` endpoints with cookie-based sessions, add a post-episode `/verify` reward grader, or deploy a NeMo Gym env to HF Spaces. NeMo Gym is the right framework when the user wants HTTP+REST with cookie session handling, raw `requests`-driven rollouts (no SDK client), Ray-based orchestration, or NVIDIA NeMo / TRL training integration with a `responses_create_params` + `ground_truth` dataset format. Output is a runnable `<env_dir>/nemo_gym/` folder with `server.py`, `pyproject.toml`, `Dockerfile`, `configs/<env>.yaml`, and `rollout.py`. Use for prompts like "wrap my env in NeMo Gym", "make a NeMo resources server for X", or "add a post-episode grader to my env".

Grade

C

Quality

74 / 100

Safety

Passed

GitHub stars

154

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