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Ejecuta cualquier Skill en Manus con un clic

review-environments

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Actualizado9 de marzo de 2026 a las 16:39

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.

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Fuente

13point5

13point5/swe-grep-oss

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SKILL.md

readonly

name	review-environments
description	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.

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:

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.toml for reproducible review runs.
Ask whether review coverage should prioritize instruct or reasoning behavior.
Instruct go-tos: gpt-4.1 series, qwen3 instruct series.
Reasoning go-tos: gpt-5 series, qwen3 thinking series, glm series.

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() with vf.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/P1 bugs and behavioral mismatches.
P2 quality 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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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.

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evaluate-environments

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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.

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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.

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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.

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name	review-environments
description	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.

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:

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.toml for reproducible review runs.
Ask whether review coverage should prioritize instruct or reasoning behavior.
Instruct go-tos: gpt-4.1 series, qwen3 instruct series.
Reasoning go-tos: gpt-5 series, qwen3 thinking series, glm series.

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() with vf.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/P1 bugs and behavioral mismatches.
P2 quality 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.