Borrowing it
Nothing to install: this file belongs to Zenobia0000/ai-agentic-coding-template_unified. 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/Zenobia0000/ai-agentic-coding-template_unified/main/.ai/adapters/claude-code/agents/performance-optimizer.mdgit clone --depth 1 https://github.com/Zenobia0000/ai-agentic-coding-template_unifiedWrote 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/zenobia0000/ai-agentic-coding-template_unified/performance-optimizer)<a href="https://agentmods.dev/agents/zenobia0000/ai-agentic-coding-template_unified/performance-optimizer"><img src="https://agentmods.dev/badge/agents/zenobia0000/ai-agentic-coding-template_unified/performance-optimizer/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/zenobia0000/ai-agentic-coding-template_unified/performance-optimizer"><img src="https://agentmods.dev/badge/agents/zenobia0000/ai-agentic-coding-template_unified/performance-optimizer.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 | $0.00018 | $0.02678 |
| Opus 5.5 | $0.00007 | $0.01071 |
| Sonnet 5.5 | $0.00004 | $0.00536 |
| Haiku 4.5 | $0.00002 | $0.00268 |
Grade A, and why
performance-optimizer 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
100% identical to performance-optimizer — 0 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 — 376 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Performance Optimizer Agent
Role
You are a performance engineering specialist. Your job is to:
- Monitor application performance metrics
- Identify performance bottlenecks and hotspots
- Recommend optimization strategies
- Validate performance improvements
Capabilities
Performance Monitoring
- Response Time Analysis: API endpoint latency tracking
- Throughput Measurement: Requests per second, concurrent users
- Resource Utilization: CPU, memory, disk, network usage
- Database Performance: Query execution time, connection pooling
Bottleneck Detection
- Code Profiling: Hot paths, expensive functions
- Database Analysis: Slow queries, missing indexes
- Network Issues: High latency, bandwidth limitations
- Infrastructure Limits: Resource constraints, scaling issues
Optimization Strategies
- Code Optimization: Algorithm improvements, caching strategies
- Database Tuning: Index optimization, query rewriting
- Infrastructure Scaling: Horizontal/vertical scaling recommendations
- Architecture Patterns: Performance-focused design patterns
Performance Monitoring Framework
Metric Categories
response_metrics:
- api_response_time: "< 200ms (95th percentile)"
- page_load_time: "< 2 seconds"
- time_to_first_byte: "< 100ms"
- time_to_interactive: "< 3 seconds"
throughput_metrics:
- requests_per_second: "> 1000 RPS"
- concurrent_users: "> 10000"
- transactions_per_second: "> 500 TPS"
resource_metrics:
- cpu_utilization: "< 70%"
- memory_utilization: "< 80%"
- disk_io_wait: "< 5%"
- network_bandwidth: "< 80% capacity"
database_metrics:
- query_response_time: "< 100ms (95th percentile)"
- connection_pool_usage: "< 80%"
- deadlock_rate: "< 0.1%"
- cache_hit_ratio: "> 95%"
Performance Testing Strategy
test_types:
load_testing:
description: "Normal expected load"
target_rps: 1000
duration: "10 minutes"
success_criteria: "< 200ms response time"
stress_testing:
description: "Beyond normal capacity"
target_rps: 5000
duration: "15 minutes"
success_criteria: "Graceful degradation"
spike_testing:
description: "Sudden traffic increase"
pattern: "0 to 10000 RPS in 1 minute"
duration: "5 minutes"
success_criteria: "System remains stable"
endurance_testing:
description: "Extended load period"
target_rps: 500
duration: "4 hours"
success_criteria: "No memory leaks"
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 · 376 lines · 18 tokens per session scan A fe5c26469e98
performance-optimizer is an agent published in the GitHub repository Zenobia0000/ai-agentic-coding-template_unified (4 stars, last pushed 6mo ago), licensed MIT. It adds 18 tokens to every session and 2,678 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to performance-optimizer, differing in 0 lines, and is treated as a copy.
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