Borrowing it
Nothing to install: this file belongs to Piebald-AI/splitrail. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Piebald-AI/splitrail/main/.claude/skills/performance/SKILL.mdgit clone --depth 1 https://github.com/Piebald-AI/splitrailWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/piebald-ai/splitrail/performance)<a href="https://agentmods.dev/skills/piebald-ai/splitrail/performance"><img src="https://agentmods.dev/badge/skills/piebald-ai/splitrail/performance/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/piebald-ai/splitrail/performance"><img src="https://agentmods.dev/badge/skills/piebald-ai/splitrail/performance.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00022 | $0.00221 |
| Opus 5.5 | $0.00009 | $0.00088 |
| Sonnet 5.5 | $0.00004 | $0.00044 |
| Haiku 4.5 | $0.00002 | $0.00022 |
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 1mo 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.
What it actually says
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)
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.
- 1mo ago First seen · 23 lines · 22 tokens per session scan A cb10c09de5f2
performance is a skill published in the GitHub repository Piebald-AI/splitrail (227 stars, last pushed today), licensed MIT. It adds 22 tokens to every session and 221 once invoked, about $0.0001 per session on Opus 5.5. 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.
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