review-environments

10
1
Source

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.

Install

mkdir -p .claude/skills/review-environments && curl -L -o skill.zip "https://mcp.directory/api/skills/download/5274" && unzip -o skill.zip -d .claude/skills/review-environments && rm skill.zip

Installs to .claude/skills/review-environments

About this skill

Review Environments

Goal

Find correctness risks and regressions first, then assess maintainability and ecosystem compliance.

Review Input Modes

  1. Local environment module in ./environments/<env_name>.
  2. Pulled Hub environment via prime env pull owner/name.
  3. Installed package under active workspace.

Review Workflow

  1. Identify environment contract:
  • load_environment(...)
  • base class and rollout behavior
  • rubric and metrics
  1. Verify installability and runtime entrypoint with the canonical eval path. Do not add --skip-upload unless the user explicitly requests that deviation; standard runs save automatically for the private Evaluations tab and prime eval tui:
prime env install <env>
prime eval run <env> -m gpt-4.1-mini -n 5
  1. Trace reward pipeline and validate scoring semantics.
  2. Run targeted checks for tool/stateful behavior where applicable.

Endpoint And Model Selection Nudge

  1. Encourage endpoint alias setup in configs/endpoints.toml for reproducible review runs.
  2. Ask whether review coverage should prioritize instruct or reasoning behavior.
  3. Instruct go-tos: gpt-4.1 series, qwen3 instruct series.
  4. Reasoning go-tos: gpt-5 series, qwen3 thinking series, glm series.

Critical Review Criteria

  1. Reward correctness:
  • Prefer deterministic, explicit checks or LLM judges.
  • Flag best-effort keyword or style heuristics unless explicitly approved.
  1. Environment self-containment:
  • Flag any requirement for user-managed background services before load_environment().
  • Require environment-managed lifecycle for sandboxes/sessions.
  1. Migration fidelity:
  • For ports, verify one-to-one equivalence of prompts, tool traces, and scoring logic.
  • Flag any assumptions made without user decision.
  1. Secrets handling:
  • Ensure required keys are validated in load_environment() with vf.ensure_keys(...).
  1. Performance and scaling:
  • Identify obvious bottlenecks in dataset loading, rubric calls, or tool execution.

Findings Format

Return findings first, sorted by severity:

  1. P0/P1 bugs and behavioral mismatches.
  2. P2 quality risks and maintainability issues.
  3. Test gaps and missing eval coverage. Include file paths, exact lines, impact, and concrete fix direction.

If No Findings

State explicitly that no defects were found, then list residual risk and untested areas.

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

browse-environments

PrimeIntellect-ai

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.

21

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