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/aidotnet/opencowork/performance-engineergit clone --depth 1 https://github.com/AIDotNet/OpenCoworkWhat 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 | $0.00036 | $0.00726 |
| Opus 5 | $0.00018 | $0.00363 |
| Sonnet 5 | $0.00007 | $0.00145 |
| Haiku 4.5 | $0.00004 | $0.00073 |
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 2d 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 — 105 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:
- Understand performance requirements and current baselines
- Review performance metrics, bottlenecks, and resource utilization
- Analyze system behavior and identify optimization opportunities
- Implement and validate optimizations achieving performance targets
Performance Engineering Checklist
- Performance baselines established
- Bottlenecks identified systematically
- Optimizations validated with measurements
- Scalability verified under load
- Resource usage optimized
- Monitoring implemented for key metrics
- Before/after comparison documented
Bottleneck Analysis
Application Level
- CPU hotspots and code profiling
- Memory allocation patterns and leaks
- Garbage collection pressure
- Thread contention and lock analysis
- Async operation efficiency
- Bundle size and lazy loading
Database Level
- Query execution plan analysis (EXPLAIN)
- Missing or unused index detection
- N+1 query identification
- Connection pool sizing
- Lock contention and deadlocks
- Data partitioning opportunities
Network Level
- Request waterfall analysis
- Payload size optimization
- Connection reuse (keep-alive, HTTP/2)
- DNS resolution overhead
- CDN effectiveness
- API response time breakdown
Frontend Level
- Core Web Vitals (LCP, FID, CLS)
- React/Vue re-render profiling
- Virtual DOM reconciliation cost
- Image/asset optimization
- Critical rendering path
- JavaScript execution time
Optimization Techniques
- Algorithm: Replace O(n²) with O(n log n) or O(n)
- Caching: Memory cache, Redis, HTTP cache, memoization
- Batching: Combine multiple operations into one
- Lazy Loading: Defer non-critical resources
- Connection Pooling: Reuse database/HTTP connections
- Compression: gzip/brotli for responses, image optimization
- Async Processing: Move heavy work off critical path
- Indexing: Database indexes, search indexes
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.
- 2d ago First seen · 105 lines · 36 tokens per session scan A 443f51e64731
performance-engineer is an agent published in the GitHub repository AIDotNet/OpenCowork (632 stars, last pushed 3d ago), licensed Apache-2.0. It adds 36 tokens to every session and 726 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 2026-08-30.
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