Agent skill
performance
Performance optimization guidelines for Splitrail. Use when optimizing parsing, reducing memory usage, or improving throughput.
Install this agent skill to your Project
npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/other/other/performance-piebald-ai-splitrail
SKILL.md
Performance Considerations
Techniques Used
- Parallel analyzer loading -
futures::join_all()for concurrent stats loading - Parallel file parsing -
rayonfor parallel iteration over files - Fast JSON parsing -
simd_jsonexclusively for all JSON operations (note:rmcpcrate re-exportsserde_jsonfor MCP server types) - Fast directory walking -
jwalkfor parallel directory traversal - Lazy message loading - TUI loads messages on-demand for session view
See existing analyzers in src/analyzers/ for usage patterns.
Guidelines
- Prefer parallel processing for I/O-bound operations
- Use
parking_lotlocks overstd::syncfor better performance - Avoid loading all messages into memory when not needed
- Use
BTreeMapfor date-ordered data (sorted iteration)
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