Borrowing it
Nothing to install: this file belongs to Jm-Paunlagui/CATHERINE. 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/Jm-Paunlagui/CATHERINE/main/.claude/skills/senior-performance-engineer/SKILL.mdgit clone --depth 1 https://github.com/Jm-Paunlagui/CATHERINEWrote 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/jm-paunlagui/catherine/senior-performance-engineer)<a href="https://agentmods.dev/skills/jm-paunlagui/catherine/senior-performance-engineer"><img src="https://agentmods.dev/badge/skills/jm-paunlagui/catherine/senior-performance-engineer/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/jm-paunlagui/catherine/senior-performance-engineer"><img src="https://agentmods.dev/badge/skills/jm-paunlagui/catherine/senior-performance-engineer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00114 | $0.01254 |
| Opus 5 | $0.00057 | $0.00627 |
| Sonnet 5 | $0.00023 | $0.00251 |
| Haiku 4.5 | $0.00011 | $0.00125 |
Grade A, and why
senior-performance-engineer 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 4d 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.
How it starts
The opening of the file, as written. The whole thing — 62 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Senior Performance Engineer (time + space complexity)
Fast and reliable, but proportional to the workload. Optimise the inner loop, not the cold path. Profile before optimising. Big-O dominates at scale; constants dominate at small N. Know which regime the code lives in.
Complexity discipline
- For every non-trivial algorithm, state time and space in Big-O in the JSDoc or a comment:
// O(n log n) time, O(n) space — n = row count. - Prefer
O(n)overO(n²)only when n can grow. For n ≤ 100 with no growth path, a clearO(n²)beats a cleverO(n log n). - Hidden quadratics: nested
.find()/.includes()inside a.map()isO(n·m). Convert one side to aMap/Set→O(n + m). - Avoid premature
.flat()/.flatMap()chains that allocate intermediate arrays; a singleforloop with manual push is often the right Big-O and the right constants.
Time-vs-space tradeoff table
| Situation | Prefer | Why |
|---|---|---|
| Read-heavy lookup, small key set | Map/object cache (space) |
O(1) lookup, memory is cheap |
| Write-heavy, small read set | Recompute (time) | Avoid cache-invalidation complexity |
| Hot path called per-request | Memoise at module load | One-time space, zero per-request time |
| Cold path called once/day | Recompute (time) | Memory pressure not worth it |
| Large dataset, single pass | Streaming/generator | O(1) space vs O(n) materialised |
| Repeated aggregation, same dataset | Materialised view (space) | Trade storage for read latency |
Frontend performance
- Lazy-load route-level views only. Memoise only with measured re-render cost.
- Virtualise lists above ~200 rows. Stable
key— never index in a reorderable list. useRequeststaleTimeper feature: high-volatility short, dashboards longer.- Images:
loading="lazy", responsivesrcset, modern formats. Batch DOM reads then writes to avoid layout thrash.
Backend performance
- Oracle:
EXPLAIN PLANfor every query touching > 10k rows. Index range scan > full table scan for selective predicates. - Aggregation: filters before joins, joins before aggregation, aggregation before sort.
- N+1: any loop calling a per-iteration query is a defect. Use
$in/IN (...)batching or a single pipeline. - Pagination: keyset (
WHERE id > :last_id) over deep offset. - Caching: cache-aside for read-heavy stable data; explicit invalidation on write. Never cache mutating responses.
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.
- 4d ago First seen · 62 lines · 114 tokens per session scan A 7d32be164160
senior-performance-engineer is a skill published in the GitHub repository Jm-Paunlagui/CATHERINE (2 stars, last pushed 4d ago), licensed Apache-2.0. It adds 114 tokens to every session and 1,254 once invoked, about $0.0006 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 2026-09-05.
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