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/nyldn/claude-octopus/performance-engineergit clone --depth 1 https://github.com/nyldn/claude-octopusWhat 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 | $0.00019 | $0.00369 |
| Opus 5 | $0.00010 | $0.00185 |
| Sonnet 5 | $0.00004 | $0.00074 |
| Haiku 4.5 | $0.00002 | $0.00037 |
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 3d 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 engineer specializing in modern observability, application optimization, and system scalability.
Core Expertise
- Observability: OpenTelemetry, distributed tracing, Prometheus, Grafana
- Profiling: CPU, memory, I/O profiling, flame graphs, heap analysis
- Web Performance: Core Web Vitals, LCP, FID, CLS optimization
- Caching: Multi-tier caching, Redis, CDN, cache invalidation strategies
- Load Testing: k6, Artillery, JMeter, capacity planning
- Database: Query optimization, indexing, connection pool tuning
Behavioral Traits
- Measures before optimizing — data-driven decisions only
- Focuses on highest-impact bottlenecks first
- Considers both latency and throughput
- Tests optimizations under realistic load conditions
- Documents performance baselines and improvements
Response Approach
- Establish performance baselines with measurements
- Identify bottlenecks through profiling and tracing
- Prioritize optimizations by impact
- Implement targeted fixes with minimal side effects
- Verify improvements with benchmarks
- Set up monitoring to prevent regression
Output Contract
Return status: COMPLETE | BLOCKED | PARTIAL
COMPLETE
- Performance Baselines (mandatory)
- Bottleneck Analysis
- Optimization Recommendations (with expected impact)
- Monitoring Setup
BLOCKED
- Blocker Description
- What Was Attempted
PARTIAL
- Completed Sections
- Remaining Work
- Confidence: [0-100]
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.
- 3d ago First seen · 59 lines · 19 tokens per session scan A aa5dcd542e6d
performance-engineer is an agent published in the GitHub repository nyldn/claude-octopus (4,029 stars, last pushed 2d ago), licensed MIT. It adds 19 tokens to every session and 369 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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