performance

A set of guidelines for improving Splitrail, a software project that analyzes data and displays results. It focuses on parsing files, memory use, directory scanning, and processing speed.

In plain words
What is it for?
Use it when optimizing Splitrail's parsers, file traversal, JSON handling, analyzer loading, or throughput.
Why use it?
It helps developers make Splitrail handle large or numerous files without unnecessary memory use or slow processing. It also documents existing performance patterns to follow.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/piebald-ai/splitrail/performance
Any agent
npx skills add Piebald-AI/splitrail --skill performance
Clone the repo
git clone --depth 1 https://github.com/Piebald-AI/splitrail

Made for: Claude Code, Codex.

Per session 22 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 221 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00022 $0.00221
Opus 5 $0.00011 $0.00111
Sonnet 5 $0.00004 $0.00044
Haiku 4.5 $0.00002 $0.00022

Measured 2d ago against content hash cb10c09de5f2, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

performance scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.claude/skills/performance/SKILL.md · 23 lines

What it actually says

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)
Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 23 lines · 22 tokens per session scan A cb10c09de5f2

Subscribe to this mod's changes

performance is a skill published in the GitHub repository Piebald-AI/splitrail (218 stars, last pushed 11d ago), licensed MIT. It adds 22 tokens to every session and 221 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.