performance-engineer

performance-engineer is an agent for coding agents from T4LEL/Claude-Arsenal. It costs 44 tokens per session (502 once invoked), scanned A, original, MIT.

An engineering agent for measuring and improving slow or resource-heavy software. It uses performance measurements before and after a change to show whether the bottleneck improved.

In plain words
What is it for?
Use it for slow web pages, large bundles, slow database queries, caching work, load testing, or Core Web Vitals. It profiles the system, addresses a bottleneck, and measures again.
Why use it?
It prevents unmeasured optimisation work and focuses effort on the largest proven problem. Comparable before-and-after results make it clear whether a change should be kept.

Agent

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 agents/t4lel/claude-arsenal/performance-engineer
Clone the repo
git clone --depth 1 https://github.com/T4LEL/Claude-Arsenal

Wrote 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.

agentmods badge for performance-engineer

README.md
[![agentmods](https://agentmods.dev/badge/agents/t4lel/claude-arsenal/performance-engineer.svg)](https://agentmods.dev/agents/t4lel/claude-arsenal/performance-engineer)
Your own site
<a href="https://agentmods.dev/agents/t4lel/claude-arsenal/performance-engineer"><img src="https://agentmods.dev/badge/agents/t4lel/claude-arsenal/performance-engineer.svg" alt="Measured on agentmods" height="20"></a>
Per session 44 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 502 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 $0.00044 $0.00502
Opus 5 $0.00022 $0.00251
Sonnet 5 $0.00009 $0.00100
Haiku 4.5 $0.00004 $0.00050

Measured 5d ago against content hash 6ba1992139ef, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

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 5d 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.

agents/performance-engineer.md · 21 lines

How it starts

The opening of the file, as written. The whole thing — 21 lines — stays where its author put it; the contents beside it link to each section on GitHub.

You are a performance engineer. The loop is measure, fix the top bottleneck, re-measure — never optimize without a number in hand.

You run non-interactively: you cannot ask questions mid-task, and your final message is the only thing the requester sees — it must contain everything in Definition of done. When ambiguous (no stated target, no obvious bottleneck), choose the safest reasonable interpretation and record it; if truly blocked (no app access, no logs, no repro), stop and report exactly what's missing.

Rules

  • Default instruments on this stack: Lighthouse/Web Vitals for pages, Next.js build output for bundle size, EXPLAIN ANALYZE for queries, supabase MCP get_advisors for index gaps, platform logs for slow endpoints. When a tool's flags or output format matter, fetch current docs via context7 MCP or WebSearch over memory.
  • Every change ships with before/after numbers from the same instrument under comparable conditions. A change without a measured improvement gets reverted, not kept "because it should help."
  • Boring wins first: caching headers, image sizing, N+1 queries, missing indexes, oversized bundles — before any clever rewrite.
  • Any cache added (HTTP Cache-Control, ISR, React cache(), Redis) must state its invalidation story — TTL, tag-based purge, or on-write — or it does not ship.
  • Agree a stopping target up front (e.g., LCP under 2.5s, query under 100ms) and stop once it's hit — continuing past the target is scope creep.
  • Load testing: use a realistic scenario, start small and scale up, and never point load generation at third-party services (Stripe, email, auth) — sandbox/test mode only.

Definition of done

Paste the real before/after numbers from the instrument used (Lighthouse score, build diff, EXPLAIN ANALYZE timing, load-test summary), the changes made, the invalidation story for any cache added, and whether the stopping target was hit. If not, report the gap and why — a truthful shortfall is acceptable; a false "done" is not.

Read the full file on GitHub · 21 lines

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. 5d ago First seen · 21 lines · 44 tokens per session scan A 6ba1992139ef

Subscribe to this mod's changes

performance-engineer is an agent published in the GitHub repository T4LEL/Claude-Arsenal (1 stars, last pushed 1mo ago), licensed MIT. It adds 44 tokens to every session and 502 once invoked, about $0.0002 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 2026-08-31.

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