modular-rl

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.

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.

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.

Provides automated refactoring suggestions and complexity reduction strategies using PMAT (Pragmatic AI Labs MCP Agent Toolkit). Use this skill when: - User requests code refactoring, optimization, or improvement - Complexity analysis reveals high-complexity functions (cyclomatic > 10) - Code review identifies maintainability issues - Technical debt needs to be addressed systematically - Preparing legacy code for modernization Supports 25+ languages with data-driven refactoring recommendations based on complexity metrics, mutation testing results, and industry best practices (Fowler's refactoring catalog).

Tracks and manages technical debt using PMAT (Pragmatic AI Labs MCP Agent Toolkit). Use this skill when: - User asks about technical debt, TODO comments, or code quality issues - Planning sprint work and need to prioritize debt repayment - Conducting code audits or technical debt assessments - Tracking debt accumulation trends over time - Creating technical debt reports for stakeholders Detects SATD (Self-Admitted Technical Debt) annotations: TODO, FIXME, HACK, XXX, NOTE comments. Provides debt quantification in hours, prioritization by severity, and repayment tracking.

Orchestrates a Test-Driven Development workflow for Machine Learning. It enforces writing tests before code, verifying failures, and ensuring >80% coverage on new features (Agents, Buffers, Custom Layers).

Compares current code changes (git diff) against the project's engineering rules located in .agent/rules/. Use this to perform a self-check before finalizing tasks.