browse-environments

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1
Source

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.

Install

mkdir -p .claude/skills/browse-environments && curl -L -o skill.zip "https://mcp.directory/api/skills/download/5894" && unzip -o skill.zip -d .claude/skills/browse-environments && rm skill.zip

Installs to .claude/skills/browse-environments

About this skill

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
  1. 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
  1. 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.
  2. Inspect details for shortlisted candidates:
prime env info owner/name
prime env status owner/name
  1. 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.
  2. Reward type: binary, continuous, judge-based, mixed.
  3. Dependencies and secrets requirements.
  4. Latest action status and version signal.
  5. Recency signal: last updated date (target within ~2 months).
  6. Fit to user goal: eval-only, GEPA, RL, 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 gpt-4.1-mini -n 5
  1. For examples in the verifiers repository, use repo install path when available:
prime env install reverse-text --from-repo

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.

inference-server

PrimeIntellect-ai

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

11

toml-config

PrimeIntellect-ai

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.

21

optimize-with-environments

PrimeIntellect-ai

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.

31

create-environments

PrimeIntellect-ai

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.

11

evaluate-environments

PrimeIntellect-ai

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.

11

train-with-environments

PrimeIntellect-ai

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 and oversampling, or create practical train and eval loops for new environments.

21

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