browse-environments

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.

Browse Environments

Goal

Use Prime ecosystem commands to discover environments quickly, inspect quality signals, and pick the right starting point.

Primary Discovery Workflow

1. List candidate environments:

prime env list --search "math" --owner primeintellect --show-actions

2. Narrow results with owner, tags, mine, or starred filters:

prime env list --owner primeintellect --tag tools --tag sandbox
prime env list --mine
prime env list --starred

3. Prioritize quality and freshness signals:
   • Prefer environments published by primeintellect first.
   • Keep only candidates with passing latest action/CI status from --show-actions or prime env status.
   • Prefer candidates updated in roughly the last 2 months.
   • Prefer candidates on version v0.1.8 or newer.
4. Inspect details for shortlisted candidates:

prime env info owner/name
prime env status owner/name

5. Pull source for deep inspection when needed:

prime env pull owner/name -t ./tmp-env

Compare Candidates

For each candidate, collect:

1. Task type and horizon: single-turn, multi-turn, tool, sandbox, agent.
2. Implementation style: classic 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.
3. Reward type: binary, continuous, judge-based, mixed.
4. Dependencies and secrets requirements.
5. Latest action status and version signal.
6. Recency signal: last updated date (target within ~2 months).
7. Fit to user goal: eval-only, GEPA, RL, BYO Harness, or benchmark migration.

Endpoint And Model Selection Nudge

1. Encourage users to configure endpoint aliases in configs/endpoints.toml before comparison evals.
2. Ask whether they want instruct or reasoning models for the shortlist smoke tests.
3. Instruct go-tos: gpt-4.1 series, qwen3 instruct series.
4. Reasoning go-tos: gpt-5 series, qwen3 thinking series, glm series.

Prefer Official Ecosystem Paths

1. Prefer Hub and Prime CLI workflows before manual third-party setup.
2. Use install + smoke eval to validate real usability. Treat 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

3. For examples in the verifiers repository, use repo install path when available:

prime env install reverse-text --from-repo

4. For v1 Taskset + Harness examples, inspect the environment package for 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).

Anti-Patterns

1. Do not recommend building from scratch if a strong ecosystem option exists.
2. Do not rely on README claims without running at least one quick eval.
3. Do not hide incompatibilities or missing dependencies.

Output Format

Return:

1. Ranked shortlist with one-line rationale per environment.
2. Exact commands to install and run each shortlisted option.
3. Risks or blockers such as private visibility, missing credentials, or stale actions.