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Run a quick smoke test to verify the RL framework is working end-to-end. Use when checking basic functionality after changes or before committing.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
Analyzes code quality, complexity, and technical debt using PMAT (Pragmatic AI Labs MCP Agent Toolkit). Use this skill when: - User mentions "code quality", "complexity", "technical debt", "grades", or "health score" - Reviewing code, refactoring, or conducting root cause analysis (Five Whys) - Creating pull requests or preparing commits - Investigating performance or quality issues Supports 25+ languages including Rust, Python, TypeScript, JavaScript, Go, C++, Java, etc. Provides Technical Debt Grading (TDG), Repo Health Scores, 5-Whys debugging, cyclomatic/cognitive complexity, and dead code detection.
Locates and reads configuration files for agents (e.g., MuZero, PPO) or games (e.g., CartPole, Atari). Use this to check hyperparameters or network settings.
Instructions for the agent on how to run python scripts correctly in this environment.
Runs static analysis (pylint) on Python files to ensure code quality standards are met. Use this before finalizing any code changes or when the user asks to "check the code".
Generates comprehensive, LLM-optimized codebase context using PMAT (Pragmatic AI Labs MCP Agent Toolkit). Use this skill when: - Starting work on unfamiliar codebases - Onboarding to new projects or repositories - Need quick understanding of project architecture - Preparing for refactoring or feature implementation - Creating documentation or technical specifications Outputs highly compressed markdown (60-80% reduction) optimized for LLM consumption. Supports 25+ languages with architecture visualization, complexity heatmaps, and dependency graphs.
Analyzes polyglot codebases with multiple programming languages using PMAT (Pragmatic AI Labs MCP Agent Toolkit). Use this skill when: - Working with projects containing multiple programming languages - Assessing cross-language integration patterns and quality - Understanding language distribution and architectural boundaries - Comparing quality metrics across language ecosystems - Identifying language-specific best practices violations Supports 25+ languages including Rust, Python, TypeScript, JavaScript, Go, C++, Java, Ruby, PHP, Swift, Kotlin, C, C#, Scala, Haskell, Elixir, Clojure, Dart, Lua, R, and more. Provides unified quality assessment across heterogeneous codebases.
| name | run-smoke-test |
| description | Run a quick smoke test to verify the RL framework is working end-to-end. Use when checking basic functionality after changes or before committing. |
Quick end-to-end sanity check for the RL framework.
ANTIGRAVITY_AGENT=1 (required for torch.compile artifact isolation).ANTIGRAVITY_AGENT=1 python -m pytest tests/ -m "not slow" -x -q