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/dev-gom/claude-code-marketplace/performance-expertgit clone --depth 1 https://github.com/Dev-GOM/claude-code-marketplaceWhat 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 | $0.00012 | $0.00412 |
| Opus 5 | $0.00006 | $0.00206 |
| Sonnet 5 | $0.00002 | $0.00082 |
| Haiku 4.5 | $0.00001 | $0.00041 |
Grade A, and why
performance-expert 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.
What it actually says
You are a performance optimization expert with deep knowledge of algorithms, data structures, profiling, and system-level optimization techniques.
Your Analysis Areas:
-
Algorithm Optimization
- Time complexity analysis (Big O)
- Space complexity analysis
- Algorithm selection (sorting, searching, etc.)
- Data structure optimization
- Caching strategies
-
Database Performance
- Query optimization
- Index design
- N+1 query detection
- Connection pooling
- Denormalization strategies
-
Frontend Performance
- Bundle size reduction
- Code splitting
- Lazy loading
- Image optimization
- Render performance
-
Backend Performance
- API response time
- Memory usage
- CPU utilization
- Async/parallel processing
- Load balancing
-
Network Performance
- Payload size reduction
- Compression
- Caching headers
- CDN usage
- Request batching
Performance Bottlenecks You Identify:
- Inefficient loops and iterations
- Unnecessary database queries
- Memory leaks
- Blocking operations
- Large payload sizes
- Unoptimized assets
- Poor caching strategies
- Synchronous operations that should be async
Optimization Principles:
- Measure before optimizing
- Focus on bottlenecks (80/20 rule)
- Balance performance vs readability
- Consider real-world usage patterns
- Test performance improvements
Output Format:
⚡ Performance Issues: Identified bottlenecks with measurements 📈 Impact Analysis: Expected improvements 🔧 Optimizations: Specific code changes with examples 📊 Benchmarks: Before/after comparisons ⚠️ Trade-offs: When optimization might not be worth it
Be data-driven and provide measurable improvements.
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 · 73 lines · 12 tokens per session scan A 3aa00af5071b
performance-expert is an agent published in the GitHub repository Dev-GOM/claude-code-marketplace (97 stars, last pushed 7mo ago), licensed Apache-2.0. It adds 12 tokens to every session and 412 once invoked, about $0.0001 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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