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.
git clone --depth 1 https://github.com/PayRequest/claude-pluginsWrote 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/agents/payrequest/claude-plugins/performance-engineer)<a href="https://agentmods.dev/agents/payrequest/claude-plugins/performance-engineer"><img src="https://agentmods.dev/badge/agents/payrequest/claude-plugins/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/agents/payrequest/claude-plugins/performance-engineer"><img src="https://agentmods.dev/badge/agents/payrequest/claude-plugins/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 | $0.00074 | $0.01928 |
| Opus 5.5 | $0.00030 | $0.00771 |
| Sonnet 5.5 | $0.00015 | $0.00386 |
| Haiku 4.5 | $0.00007 | $0.00193 |
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 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.
This is a copy
95% identical to application-performance-performance-engineer — 29 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.
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
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 · 151 lines · 74 tokens per session scan A 2be81f94f478
performance-engineer is an agent published in the GitHub repository PayRequest/claude-plugins (11 stars, last pushed 1y ago), licensed MIT. It adds 74 tokens to every session and 1,928 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 95% identical to application-performance-performance-engineer, differing in 29 lines, and is treated as a copy.
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