pedalboard-pluginary — Cursor Rules

The pedalboard-pluginary project has successfully implemented a robust, failsafe scanning architecture with complete process isolation, journaling capabilities, and SQLite-based storage. The system reliably scans audio plugins (VST3, AU) with crash recovery, resume functionality, and efficient data storage.

• If we need a new Python project, run curl -LsSf https://astral.sh/uv/install.sh | sh; uv venv --python 3.12; uv init; uv add fire rich; uv sync • Use tree CLI app if available to verify file locations • Check existing code with .venv folder to scan and consult dependency source code • Run DIR="."; uvx codetoprompt --compress --output "$DIR/llms.txt" --respect-gitignore --cxml --exclude ".svg,.specstory,.md,.txt,ref,testdata,.lock,*.svg" "$DIR" to get a condensed snapshot of the codebase into llms.txt

When I say "/plan [requirement]", you must: • DECONSTRUCT the requirement: • Extract core intent, key features, and objectives • Identify technical requirements and constraints • Map what's explicitly stated vs. what's implied • Determine success criteria • DIAGNOSE the project needs: • Audit for missing specifications • Check technical feasibility • Assess complexity and dependencies • Identify potential challenges • RESEARCH additional material: • Repeatedly call the perplexity_ask and request up-to-date information or additional remote context • Repeatedly call the context7 tool and request up-to-date software package documentation • Repeatedly call the codex tool and request additional reasoning, summarization of files and second opinion • DEVELOP the plan structure: • Break down into logical phases/milestones • Create hierarchical task decomposition • Assign priorities and dependencies • Add implementation details and technical specs • Include edge cases and error handling • Define testing and validation steps • DELIVER to PLAN.md: • Write a comprehensive, detailed plan with: • Project overview and objectives • Technical architecture decisions • Phase-by-phase breakdown • Specific implementation steps • Testing and validation criteria • Future considerations • Simultaneously create/update TODO.md with the flat itemized - [ ] representation Plan Optimization Techniques: • Task Decomposition: Break complex requirements into atomic, actionable tasks • Dependency Mapping: Identify and document task dependencies • Risk Assessment: Include potential blockers and mitigation strategies • Progressive Enhancement: Start with MVP, then layer improvements • Technical Specifications: Include specific technologies, patterns, and approaches

• Process Isolation: Each plugin scanned in separate subprocess via scan_single.py • Journaling: SQLite-based journal tracks scan progress and enables resume after crashes • Primary Storage: SQLite database with full-text search and indexing • Atomic Commits: Two-phase commit ensures cache consistency • Parallel Processing: ThreadPoolExecutor for optimal performance • Progress Tracking: Rich console UI with real-time scan progress