hud-evals

Start and test the prime-rl inference server. Use when asked to run inference, start vLLM, test a model, or launch the inference server.

How to write and use TOML configs in prime-rl. Use when creating config files, running commands with configs, or overriding config values via CLI.

Deploy HUD environments to the platform for parallel evaluations and training at scale. Use when asked to "deploy my environment", "push to HUD", "run evals at scale", or "set up remote environments".

Create HUD environments with tools and evaluation scenarios for AI agents. Use when asked to "create an environment", "write a HUD env", "add tools for agents", "define evaluation scenarios", "wrap my API for agents", or when building agent-callable tools with the hud-python SDK.

Build, evaluate, and train AI agents on RL environments with HUD. Use whenever someone wants to create an RL environment, benchmark, eval, or training task — for a coding, computer-use, browser, or robotics agent — or run and grade tasks across any model (Claude, OpenAI, Gemini, or open/self-hosted models). Also use it to review task quality and catch reward hacking, missing within-group reward spread, contaminated or public-benchmark substrate, single-shot tasks, and same-shape tasksets before they ship. Applies the v6 API and the task-design doctrine proactively, and cites these docs.