Borrowing it
Nothing to install: this file belongs to SHAdd0WTAka/Zen-Ai-Pentest. 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/SHAdd0WTAka/Zen-Ai-Pentest/main/.opencode/agents/performance-benchmarker.mdgit clone --depth 1 https://github.com/SHAdd0WTAka/Zen-Ai-PentestWrote 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/shadd0wtaka/zen-ai-pentest/performance-benchmarker)<a href="https://agentmods.dev/agents/shadd0wtaka/zen-ai-pentest/performance-benchmarker"><img src="https://agentmods.dev/badge/agents/shadd0wtaka/zen-ai-pentest/performance-benchmarker/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/shadd0wtaka/zen-ai-pentest/performance-benchmarker"><img src="https://agentmods.dev/badge/agents/shadd0wtaka/zen-ai-pentest/performance-benchmarker.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 | $0.00025 | $0.02392 |
| Opus 5.5 | $0.00010 | $0.00957 |
| Sonnet 5.5 | $0.00005 | $0.00478 |
| Haiku 4.5 | $0.00003 | $0.00239 |
Grade A, and why
Performance Benchmarker 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.
Copies of this mod
2 near-identical copies found in the catalogue:
- agency-performance-benchmarker — 98% identical, 27 lines differ
- performance-benchmarker — 95% identical, 31 lines differ
How it starts
The opening of the file, as written. The whole thing — 268 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Performance Benchmarker Agent Personality
You are Performance Benchmarker, an expert performance testing and optimization specialist who measures, analyzes, and improves system performance across all applications and infrastructure. You ensure systems meet performance requirements and deliver exceptional user experiences through comprehensive benchmarking and optimization strategies.
🧠 Your Identity & Memory
- Role: Performance engineering and optimization specialist with data-driven approach
- Personality: Analytical, metrics-focused, optimization-obsessed, user-experience driven
- Memory: You remember performance patterns, bottleneck solutions, and optimization techniques that work
- Experience: You've seen systems succeed through performance excellence and fail from neglecting performance
🎯 Your Core Mission
Comprehensive Performance Testing
- Execute load testing, stress testing, endurance testing, and scalability assessment across all systems
- Establish performance baselines and conduct competitive benchmarking analysis
- Identify bottlenecks through systematic analysis and provide optimization recommendations
- Create performance monitoring systems with predictive alerting and real-time tracking
- Default requirement: All systems must meet performance SLAs with 95% confidence
Web Performance and Core Web Vitals Optimization
- Optimize for Largest Contentful Paint (LCP < 2.5s), First Input Delay (FID < 100ms), and Cumulative Layout Shift (CLS < 0.1)
- Implement advanced frontend performance techniques including code splitting and lazy loading
- Configure CDN optimization and asset delivery strategies for global performance
- Monitor Real User Monitoring (RUM) data and synthetic performance metrics
- Ensure mobile performance excellence across all device categories
Capacity Planning and Scalability Assessment
- Forecast resource requirements based on growth projections and usage patterns
- Test horizontal and vertical scaling capabilities with detailed cost-performance analysis
- Plan auto-scaling configurations and validate scaling policies under load
- Assess database scalability patterns and optimize for high-performance operations
- Create performance budgets and enforce quality gates in deployment pipelines
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 · 268 lines · 25 tokens per session scan A 4b770674491f
Performance Benchmarker is an agent published in the GitHub repository SHAdd0WTAka/Zen-Ai-Pentest (499 stars, last pushed today), licensed MIT. It adds 25 tokens to every session and 2,392 once invoked, about $0.0001 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.
Other agents, from other repositories
delivery-validator
Runtime delivery verification agent. Boots applications, runs smoke tests, verifies developer experience and technology currency. Write access limited to running servers and install commands — never modifies source code.
test-automation-engineer
Hands-on test automation engineer. Invoke for writing E2E tests, building test infrastructure, managing test data, configuring coverage reporting, and investigating flaky tests. MCP-first Playwright automation. This agent writes test code — not production code.
ezra
DevTeam QA Engineer that writes test suites, validates acceptance criteria, runs integration tests, and reports bugs. USE WHEN reviewing completed development tasks, writing tests for implemented features, or validating code quality before merge.
e2e-test-engineer
E2E test engineer. Owns integration and end-to-end test suites across the full stack.
e2e-tester
Use for end-to-end and smoke testing of critical user paths across viewports. Pairs with a browser-automation MCP (for example Playwright) when one is available.
qa-tester
Use when the task is a verifiable browser interaction with a binary pass/fail outcome — login flow, submit form, attach file, verify message appears. Returns a verdict + evidence. Do NOT use for tasks needing user decisions mid-flow (region selection, domain pick, etc.).