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@Piebald-AI/splitrail
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Performance optimization guidelines for Splitrail. Use when optimizing parsing, reducing memory usage, or improving throughput.

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SKILL.md

name performance
description Performance optimization guidelines for Splitrail. Use when optimizing parsing, reducing memory usage, or improving throughput.

Performance Considerations

Techniques Used

  • Parallel analyzer loading - futures::join_all() for concurrent stats loading
  • Parallel file parsing - rayon for parallel iteration over files
  • Fast JSON parsing - simd_json exclusively for all JSON operations (note: rmcp crate re-exports serde_json for MCP server types)
  • Fast directory walking - jwalk for parallel directory traversal
  • Lazy message loading - TUI loads messages on-demand for session view

See existing analyzers in src/analyzers/ for usage patterns.

Guidelines

  1. Prefer parallel processing for I/O-bound operations
  2. Use parking_lot locks over std::sync for better performance
  3. Avoid loading all messages into memory when not needed
  4. Use BTreeMap for date-ordered data (sorted iteration)