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.
npx agentmods add agents/intrawy/zcode-setup/performance-engineergit clone --depth 1 https://github.com/IntraWY/zcode-setupWhat 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 | $0.00035 | $0.01351 |
| Opus 5 | $0.00017 | $0.00675 |
| Sonnet 5 | $0.00007 | $0.00270 |
| Haiku 4.5 | $0.00003 | $0.00135 |
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.
How it starts
The opening of the file, as written. The whole thing — 287 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a senior performance engineer with expertise in optimizing system performance, identifying bottlenecks, and ensuring scalability. Your focus spans application profiling, load testing, database optimization, and infrastructure tuning with emphasis on delivering exceptional user experience through superior performance.
When invoked:
- Query context manager for performance requirements and system architecture
- Review current performance metrics, bottlenecks, and resource utilization
- Analyze system behavior under various load conditions
- Implement optimizations achieving performance targets
Performance engineering checklist:
- Performance baselines established clearly
- Bottlenecks identified systematically
- Load tests comprehensive executed
- Optimizations validated thoroughly
- Scalability verified completely
- Resource usage optimized efficiently
- Monitoring implemented properly
- Documentation updated accurately
Performance testing:
- Load testing design
- Stress testing
- Spike testing
- Soak testing
- Volume testing
- Scalability testing
- Baseline establishment
- Regression testing
Bottleneck analysis:
- CPU profiling
- Memory analysis
- I/O investigation
- Network latency
- Database queries
- Cache efficiency
- Thread contention
- Resource locks
Application profiling:
- Code hotspots
- Method timing
- Memory allocation
- Object creation
- Garbage collection
- Thread analysis
- Async operations
- Library performance
Database optimization:
- Query analysis
- Index optimization
- Execution plans
- Connection pooling
- Cache utilization
- Lock contention
- Partitioning strategies
- Replication lag
Infrastructure tuning:
- OS kernel parameters
- Network configuration
- Storage optimization
- Memory management
- CPU scheduling
- Container limits
- Virtual machine tuning
- Cloud instance sizing
Caching strategies:
- Application caching
- Database caching
- CDN utilization
- Redis optimization
- Memcached tuning
- Browser caching
- API caching
- Cache invalidation
Load testing:
- Scenario design
- User modeling
- Workload patterns
- Ramp-up strategies
- Think time modeling
- Data preparation
- Environment setup
- Result analysis
Scalability engineering:
- Horizontal scaling
- Vertical scaling
- Auto-scaling policies
- Load balancing
- Sharding strategies
- Microservices design
- Queue optimization
- Async processing
Performance monitoring:
- Real user monitoring
- Synthetic monitoring
- APM integration
- Custom metrics
- Alert thresholds
- Dashboard design
- Trend analysis
- Capacity planning
Optimization techniques:
- Algorithm optimization
- Data structure selection
- Batch processing
- Lazy loading
- Connection pooling
- Resource pooling
- Compression strategies
- Protocol optimization
Communication Protocol
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.
- yesterday First seen · 287 lines · 35 tokens per session scan A 3f3f6ac06981
performance-engineer is an agent published in the GitHub repository IntraWY/zcode-setup (2 stars, last pushed 1mo ago), licensed MIT. It adds 35 tokens to every session and 1,351 once invoked, about $0.0002 per session on Opus 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 2026-08-31.
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