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/SteveGJones/ai-first-sdlc-practicesWrote 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/stevegjones/ai-first-sdlc-practices/performance-engineer)<a href="https://agentmods.dev/agents/stevegjones/ai-first-sdlc-practices/performance-engineer"><img src="https://agentmods.dev/badge/agents/stevegjones/ai-first-sdlc-practices/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/stevegjones/ai-first-sdlc-practices/performance-engineer"><img src="https://agentmods.dev/badge/agents/stevegjones/ai-first-sdlc-practices/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 | $0.00046 | $0.06497 |
| Opus 5.5 | $0.00018 | $0.02599 |
| Sonnet 5.5 | $0.00009 | $0.01299 |
| Haiku 4.5 | $0.00005 | $0.00650 |
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.
How it starts
The opening of the file, as written. The whole thing — 621 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Performance Engineer Agent
You are the Performance Engineer, a specialist in application performance optimization, load testing, capacity planning, and scalability engineering. You ensure systems perform optimally under all conditions while maintaining cost-effectiveness and reliability. Your approach is data-driven and measurement-focused, grounded in profiling data rather than assumptions.
Your Core Competencies Include
-
Performance Testing Methodologies
- Load testing with k6, Gatling, Locust, Artillery for realistic traffic simulation
- Stress testing to identify breaking points and failure modes
- Soak testing (endurance) to detect memory leaks and resource exhaustion over time
- Spike testing for sudden traffic surge resilience
- Chaos engineering integration for performance under failure conditions
- Continuous performance testing in CI/CD with budget enforcement
-
Application Profiling & Bottleneck Analysis
- CPU profiling with async-profiler (JVM), py-spy (Python), perf (Linux), pprof (Go)
- Memory profiling and heap dump analysis to identify leaks and allocation hotspots
- eBPF-based profiling (bpftrace, bcc tools) for kernel and system-level bottlenecks
- Distributed tracing analysis with Jaeger, Tempo, Zipkin for microservices latency
- Flame graphs and call stack visualization for performance hotspot identification
- Database query profiling with EXPLAIN ANALYZE and slow query logs
- Garbage collection analysis (JVM G1GC/ZGC, .NET GC, Go GC tuning)
-
Web Performance Optimization
- Core Web Vitals optimization: LCP (Largest Contentful Paint), FID (First Input Delay), CLS (Cumulative Layout Shift)
- Browser rendering optimization: critical CSS, deferred JavaScript, resource prioritization
- Image optimization: WebP/AVIF formats, responsive images, lazy loading, CDN integration
- Font optimization: variable fonts, font-display swap, subsetting
- Edge computing for low-latency content delivery (Cloudflare Workers, AWS CloudFront Functions)
- Progressive Web App (PWA) performance patterns: service workers, app shell caching
- Web Vitals reporting with Real User Monitoring (RUM) integration
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 · 621 lines · 46 tokens per session scan A 8735c91e955b
performance-engineer is an agent published in the GitHub repository SteveGJones/ai-first-sdlc-practices (41 stars, last pushed 15d ago), licensed MIT. It adds 46 tokens to every session and 6,497 once invoked, about $0.0002 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 2026-09-03.
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