// Discover and inspect verifiers environments through the Prime ecosystem. Use when asked to find environments on the Hub, compare options, inspect metadata, check action status, pull local copies for inspection, or choose environment starting points before evaluation, training, or migration work.
Create or migrate verifiers environments for the Prime Lab ecosystem. Use when asked to build a new environment from scratch, port an eval or benchmark from papers or other libraries, start from an environment on the Hub, or convert existing tasks into a package that exposes load_environment and installs cleanly with prime env install.
Review verifiers environments for correctness, robustness, and ecosystem compatibility. Use when asked for environment code review, quality audit, migration validation, or release readiness checks for local environments or environments pulled from the Hub.
Train models with verifiers environments using hosted RL or prime-rl. Use when asked to configure RL runs, tune key hyperparameters, diagnose instability, set up difficulty filtering, or create practical train and eval loops for new environments.
Run and analyze evaluations for verifiers environments using prime eval. Use when asked to smoke-test environments, run benchmark sweeps, resume interrupted evaluations, compare models, inspect sample-level outputs, or produce evaluation summaries suitable for deciding next steps.
Optimize environment system prompts with GEPA through prime gepa run. Use when asked to improve prompt performance without gradient training, compare baseline versus optimized prompts, run GEPA from CLI or TOML configs, or interpret GEPA outputs before deployment.
Run interactive brainstorming across verifiers environments, evaluations, GEPA, and RL training. Use when the user wants ideation, literature scanning, concept teaching, roadmap planning, or research program design grounded in local CLI sources, verifiers, and RL trainer code.
| name | browse-environments |
| description | Discover and inspect verifiers environments through the Prime ecosystem. Use when asked to find environments on the Hub, compare options, inspect metadata, check action status, pull local copies for inspection, or choose environment starting points before evaluation, training, or migration work. |
Use Prime ecosystem commands to discover environments quickly, inspect quality signals, and pick the right starting point.
prime env list --search "math" --owner primeintellect --show-actions
prime env list --owner primeintellect --tag tools --tag sandbox
prime env list --mine
prime env list --starred
primeintellect first.--show-actions or prime env status.v0.1.8 or newer.prime env info owner/name
prime env status owner/name
prime env pull owner/name -t ./tmp-env
For each candidate, collect:
MultiTurnEnv/ToolEnv, V1 vf.Env with explicit vf.Taskset/vf.Harness objects for framework programs, or CliAgentEnv for sandboxed agent binaries with LLM-API interception.configs/endpoints.toml before comparison evals.gpt-4.1 series, qwen3 instruct series.gpt-5 series, qwen3 thinking series, glm series.prime eval run as the canonical eval path and do not add --skip-upload unless the user explicitly requests that deviation:prime env install owner/name
prime eval run name -m openai/gpt-4.1-mini -n 5
prime env install reverse-text --from-repo
Taskset / optional Harness classes plus load_taskset(config: MyTasksetConfig), optional load_harness(config: MyHarnessConfig), and the canonical load_environment(config: vf.EnvConfig) -> vf.Env shim delegating through vf.load_taskset(config=config.taskset) and vf.load_harness(config=config.harness).Return: