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rl-task-audit

Audit reinforcement-learning repositories for task, commands, rewards, logging, and metrics.

1|Updated May 14, 2026
Search Tags:#metrics#experiment planning#baseline#reinforcement learning#contract#repository audit#safety gates
One-click install
npx skills add https://github.com/junhyekh/rlxp --skill rl-task-audit
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: rl-task-audit
Source: https://github.com/junhyekh/rlxp/tree/main/plugins/rl-experiment-assistant/skills/rl-task-audit
Command: npx skills add https://github.com/junhyekh/rlxp --skill rl-task-audit

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps you inspect a reinforcement-learning repository, understand the experiment surface, and decide what planning or contract work should happen next without accidentally starting training.

Core Features & Use Cases

  • Repository Audit: Identifies the task, commands, reward signals, terminations, curriculum, domain randomization, logging, and metrics in an RL codebase.
  • Planning Handoff: Routes work to metric definition, experiment contract creation, or baseline planning when the user is ready for the next step.
  • Safety Guardrails: Preserves user intent, verifies commands against the live repository, and avoids launching simulators or training from the audit stage.
  • Use Case: A researcher asks for a quick assessment of a new RL repo, and this Skill organizes the task context so the team can define metrics and launch rules confidently.

Quick Start

Use the rl-task-audit skill to analyze the target repository, summarize the RL task and experiment surface, and recommend the appropriate next planning step.

Frequently Asked Questions about rl-task-audit

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I audit a reinforcement learning repository to identify reward structure and metrics?▼

Yes, the audit preserves user intent and applies dry-run safety gates that verify commands against the live repository, preventing training or simulator launches during the inspection stage.

What is the best way to plan next steps after inspecting an RL codebase?▼

The best way to plan next steps is routing the audit results to metric definition, experiment contract creation, or baseline planning based on the identified task context and experiment surface.

Can I use this audit to check for domain randomization and curriculum in an RL repo?▼

The audit applies to task-scoped experiment workflows that may continue into metric definition, contract creation, baseline planning, or compatibility-driven legacy dispatch, making it suitable for complex RL codebases.

Why does an RL repository audit require dry-run safety gates?▼

A limitation is that it strictly avoids launching simulators or training from the audit stage, meaning you cannot use it to execute or test reinforcement learning training runs directly.

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