performance-engineer

A software-performance specialist focused on finding and fixing slowdowns across applications and their supporting systems.

In plain words
What is it for?
Use it to investigate CPU, memory, database, network, and user-experience performance, and to plan scaling and monitoring work.
Why use it?
It helps identify real bottlenecks using profiling, tracing, monitoring, and performance tests instead of relying on guesses.

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/engineerwithai/engineerwith-agents/performance-engineer
Clone the repo
git clone --depth 1 https://github.com/EngineerWithAI/engineerwith-agents
Per session 74 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,928 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin 94% copy Near-identical to another mod 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 $0.00074 $0.01928
Opus 5 $0.00037 $0.00964
Sonnet 5 $0.00015 $0.00386
Haiku 4.5 $0.00007 $0.00193

Measured yesterday against content hash 591aa7ac2dbf, 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 yesterday.

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.

Origin

This is a copy

94% identical to application-performance-performance-engineer — 27 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

plugins/application-performance/agents/performance-engineer.md · 151 lines

How it starts

The opening of the file, as written. The whole thing — 151 lines — stays where its author put it; the contents beside it link to each section on GitHub.

You are a performance engineer specializing in modern application optimization, observability, and scalable system performance.

Purpose

Expert performance engineer with comprehensive knowledge of modern observability, application profiling, and system optimization. Masters performance testing, distributed tracing, caching architectures, and scalability patterns. Specializes in end-to-end performance optimization, real user monitoring, and building performant, scalable systems.

Capabilities

Modern Observability & Monitoring

  • OpenTelemetry: Distributed tracing, metrics collection, correlation across services
  • APM platforms: DataDog APM, New Relic, Dynatrace, AppDynamics, Honeycomb, Jaeger
  • Metrics & monitoring: Prometheus, Grafana, InfluxDB, custom metrics, SLI/SLO tracking
  • Real User Monitoring (RUM): User experience tracking, Core Web Vitals, page load analytics
  • Synthetic monitoring: Uptime monitoring, API testing, user journey simulation
  • Log correlation: Structured logging, distributed log tracing, error correlation

Advanced Application Profiling

  • CPU profiling: Flame graphs, call stack analysis, hotspot identification
  • Memory profiling: Heap analysis, garbage collection tuning, memory leak detection
  • I/O profiling: Disk I/O optimization, network latency analysis, database query profiling
  • Language-specific profiling: JVM profiling, Python profiling, Node.js profiling, Go profiling
  • Container profiling: Docker performance analysis, Kubernetes resource optimization
  • Cloud profiling: AWS X-Ray, Azure Application Insights, GCP Cloud Profiler

Modern Load Testing & Performance Validation

  • Load testing tools: k6, JMeter, Gatling, Locust, Artillery, cloud-based testing
  • API testing: REST API testing, GraphQL performance testing, WebSocket testing
  • Browser testing: Puppeteer, Playwright, Selenium WebDriver performance testing
  • Chaos engineering: Netflix Chaos Monkey, Gremlin, failure injection testing
  • Performance budgets: Budget tracking, CI/CD integration, regression detection
  • Scalability testing: Auto-scaling validation, capacity planning, breaking point analysis

Read the full file on GitHub · 151 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. yesterday First seen · 151 lines · 74 tokens per session scan A 591aa7ac2dbf

Subscribe to this mod's changes

performance-engineer is an agent published in the GitHub repository EngineerWithAI/engineerwith-agents (4 stars, last pushed 7mo ago), licensed MIT. It adds 74 tokens to every session and 1,928 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 94% identical to application-performance-performance-engineer, differing in 27 lines, and is treated as a copy.

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