yt-dlp-for-ableton

Clean up AI-generated or over-engineered code while preserving behavior. Use when: refactoring noisy code, removing dead code, simplifying abstractions, or making code more idiomatic without changing the public API.

Review AI-generated code changes for unintended consequences, logic errors, UI inconsistencies, and context-specific issues. Use when: code has just been modified, flag potential bugs, verify UI consistency, check implementation correctness against project patterns.

Turn a feature request, issue, or rough spec into a safe implementation plan for an existing codebase. Use when: planning a new feature, scoping impacted files, evaluating trade-offs, or deciding rollout steps before coding.

Turn small ideas into fully-fledged projects through structured discovery, research, and specification. Use when: starting a new project from a rough idea, need to validate feasibility, want to create comprehensive project documentation, exploring feature requirements and design patterns. Conversational, adaptive workflow.

Audit the quality and robustness of an implementation by inventorying the code, understanding how it works, then researching reference examples and community standards online to identify gaps. Use when: checking if a DSP routine is correct, validating a JUCE component follows best practices, reviewing an entire module for structural weaknesses, or verifying a feature is not just "working" but properly implemented.

Initialize or bootstrap a Ralph workspace. Use when starting a repo, preparing the workspace for first use, creating or seeding the `.ralph` folder, filling the first project understanding / journal entries, and documenting setup blockers before substantive work begins.

Find the actual cause of bugs, failures, flaky tests, build errors, and regressions before proposing a fix. Use when: debugging runtime errors, CI failures, broken features, inconsistent behavior, JUCE/CMake build errors, or production incidents.

Review code and architecture for security risks, then harden the implementation without unnecessary complexity. Use when: checking auth flows, input validation, secrets, dependency risk, unsafe file or network access, or preparing a project for release.

Design the smallest effective set of tests for a change, then generate them in the project's style. Use when: fixing bugs, adding features, preventing regressions, or improving weak test coverage across web, JVM, Python, and native codebases.

Apply algorithmic UI design principles, build production-grade frontend interfaces, conduct JUCE UI audits, and detect AI-generated UI patterns. Use when: generating web UI code, designing or reviewing interfaces, building distinctive frontend components, auditing JUCE plugin UIs for premium aesthetics, ensuring design system consistency, detecting vibe-coded or AI-generated design, fixing generic-looking websites, de-slopping a UI, removing AI tropes, identifying cookie-cutter patterns, auditing for AI aesthetics, or making a site look less AI-generated.