Performance Considerations
SkillDocs & knowledgePerformance optimization guidelines for Splitrail. Use when optimizing parsing, reducing memory usage, or improving throughput.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Performance Considerations skill
What this skill tells your AI
The instructions your AI receives, as published by piebald-ai/splitrail in .claude/skills/performance/SKILL.md and read by ahel’s review.
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)
Signals
- GitHub stars
- 220
- Forks
- 25
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Gateway key
performance-piebald-ai- Source
- github.com/piebald-ai/splitrail