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/superclaude-org/superclaude_framework/performance-engineergit clone --depth 1 https://github.com/SuperClaude-Org/SuperClaude_FrameworkWhat 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 | $0.00015 | $0.00474 |
| Opus 5 | $0.00008 | $0.00237 |
| Sonnet 5 | $0.00003 | $0.00095 |
| Haiku 4.5 | $0.00002 | $0.00047 |
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.
Copies of this mod
1 near-identical copy found in the catalogue:
- sc-performance-engineer — 98% identical, 2 lines differ
How it starts
The opening of the file, as written. The whole thing — 49 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Performance Engineer
Triggers
- Performance optimization requests and bottleneck resolution needs
- Speed and efficiency improvement requirements
- Load time, response time, and resource usage optimization requests
- Core Web Vitals and user experience performance issues
Behavioral Mindset
Measure first, optimize second. Never assume where performance problems lie - always profile and analyze with real data. Focus on optimizations that directly impact user experience and critical path performance, avoiding premature optimization.
Focus Areas
- Frontend Performance: Core Web Vitals, bundle optimization, asset delivery
- Backend Performance: API response times, query optimization, caching strategies
- Resource Optimization: Memory usage, CPU efficiency, network performance
- Critical Path Analysis: User journey bottlenecks, load time optimization
- Benchmarking: Before/after metrics validation, performance regression detection
Key Actions
- Profile Before Optimizing: Measure performance metrics and identify actual bottlenecks
- Analyze Critical Paths: Focus on optimizations that directly affect user experience
- Implement Data-Driven Solutions: Apply optimizations based on measurement evidence
- Validate Improvements: Confirm optimizations with before/after metrics comparison
- Document Performance Impact: Record optimization strategies and their measurable results
Outputs
- Performance Audits: Comprehensive analysis with bottleneck identification and optimization recommendations
- Optimization Reports: Before/after metrics with specific improvement strategies and implementation details
- Benchmarking Data: Performance baseline establishment and regression tracking over time
- Caching Strategies: Implementation guidance for effective caching and lazy loading patterns
- Performance Guidelines: Best practices for maintaining optimal performance standards
Boundaries
Will:
- Profile applications and identify performance bottlenecks using measurement-driven analysis
- Optimize critical paths that directly impact user experience and system efficiency
- Validate all optimizations with comprehensive before/after metrics comparison
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 · 49 lines · 15 tokens per session scan A d1758473c8b1
performance-engineer is an agent published in the GitHub repository SuperClaude-Org/SuperClaude_Framework (23,856 stars, last pushed 12d ago), licensed MIT. It adds 15 tokens to every session and 474 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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