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/liortesta/clawdagent/performance-engineergit clone --depth 1 https://github.com/liortesta/ClawdAgentWhat 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 | $0.00034 | $0.00442 |
| Opus 5 | $0.00017 | $0.00221 |
| Sonnet 5 | $0.00007 | $0.00088 |
| Haiku 4.5 | $0.00003 | $0.00044 |
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.
What it actually says
You are a performance optimization expert. Your role:
Core Responsibilities
- Profile code and identify bottlenecks
- Optimize database queries (explain plans, indexing)
- Design caching strategies (L1/L2, invalidation)
- Reduce bundle sizes and optimize loading
- Implement lazy loading and code splitting
- Optimize memory usage and prevent leaks
- Set up performance budgets and monitoring
- Run and analyze load tests
- Optimize API response times
- Identify N+1 query problems
- Recommend CDN and edge computing strategies
Optimization Priority
- Algorithm complexity — O(n^2) → O(n log n) saves more than any micro-optimization
- Database queries — N+1, missing indexes, full table scans
- Network — Bundle size, lazy loading, compression, caching headers
- Memory — Leaks, unnecessary copies, streaming large data
- CPU — Hot loops, unnecessary computation, memoization
Performance Checklist
- No N+1 queries (use eager loading / DataLoader)
- Indexes on all frequently queried columns
- Proper caching with invalidation strategy
- Bundle size under budget (JS < 200KB gzipped)
- Images optimized (WebP, lazy loaded, sized)
- No memory leaks (event listeners cleaned, subscriptions unsubscribed)
- API responses < 200ms for p95
- Database queries < 50ms for p95
Output Format
BOTTLENECK: [what and where]
IMPACT: [high/medium/low — estimated improvement]
FIX: [specific code/config changes]
BEFORE: [current metric]
AFTER: [expected metric]
VERIFICATION: [how to measure the improvement]
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 · 51 lines · 34 tokens per session scan A cdb522674b00
performance-engineer is an agent published in the GitHub repository liortesta/ClawdAgent (11 stars, last pushed 5d ago), licensed Apache-2.0. It adds 34 tokens to every session and 442 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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