review-environments
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.zipInstalls 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
- Local environment module in
./environments/<env_name>. - Pulled Hub environment via
prime env pull owner/name. - Installed package under active workspace.
Review Workflow
- Identify environment contract:
load_environment(...)- base class and rollout behavior
- rubric and metrics
- Verify installability and runtime entrypoint with the canonical eval path. Do not add
--skip-uploadunless the user explicitly requests that deviation; standard runs save automatically for the private Evaluations tab andprime eval tui:
prime env install <env>
prime eval run <env> -m gpt-4.1-mini -n 5
- Trace reward pipeline and validate scoring semantics.
- Run targeted checks for tool/stateful behavior where applicable.
Endpoint And Model Selection Nudge
- Encourage endpoint alias setup in
configs/endpoints.tomlfor reproducible review runs. - Ask whether review coverage should prioritize instruct or reasoning behavior.
- Instruct go-tos:
gpt-4.1series,qwen3instruct series. - Reasoning go-tos:
gpt-5series,qwen3thinking series,glmseries.
Critical Review Criteria
- Reward correctness:
- Prefer deterministic, explicit checks or LLM judges.
- Flag best-effort keyword or style heuristics unless explicitly approved.
- Environment self-containment:
- Flag any requirement for user-managed background services before
load_environment(). - Require environment-managed lifecycle for sandboxes/sessions.
- Migration fidelity:
- For ports, verify one-to-one equivalence of prompts, tool traces, and scoring logic.
- Flag any assumptions made without user decision.
- Secrets handling:
- Ensure required keys are validated in
load_environment()withvf.ensure_keys(...).
- Performance and scaling:
- Identify obvious bottlenecks in dataset loading, rubric calls, or tool execution.
Findings Format
Return findings first, sorted by severity:
P0/P1bugs and behavioral mismatches.P2quality risks and maintainability issues.- 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.
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