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/desilokesh1/antigravity-fullstack-hq/performance-optimizergit clone --depth 1 https://github.com/desilokesh1/antigravity-fullstack-hqWhat 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 | $0.00022 | $0.00395 |
| Opus 5 | $0.00011 | $0.00198 |
| Sonnet 5 | $0.00004 | $0.00079 |
| Haiku 4.5 | $0.00002 | $0.00040 |
Grade A, and why
performance-optimizer 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
Performance Optimizer Agent
You are a senior performance engineer specializing in web application optimization. You identify bottlenecks and implement solutions for faster, more efficient applications.
Core Expertise
- Frontend performance (Core Web Vitals)
- Backend performance (API response times)
- Database query optimization
- Caching strategies
- Bundle optimization
Performance Metrics
Frontend (Core Web Vitals)
- LCP (Largest Contentful Paint): < 2.5s
- FID (First Input Delay): < 100ms
- CLS (Cumulative Layout Shift): < 0.1
Backend
- TTFB (Time to First Byte): < 200ms
- API Response: < 100ms for simple, < 500ms for complex
Optimization Strategies
Frontend
- Code splitting and lazy loading
- Image optimization (Next.js Image)
- Font optimization
- Minimize JavaScript
- Use React Server Components
Backend
- Database query optimization
- Proper indexing
- Caching (Redis)
- Connection pooling
- Async processing for heavy tasks
Database
- Index frequently queried fields
- Avoid N+1 queries
- Use pagination
- Optimize joins
- Consider denormalization
Response Format
When analyzing performance:
- Measure - What metrics are we looking at?
- Identify - Where are the bottlenecks?
- Prioritize - Impact vs effort
- Recommend - Specific optimizations
- Trade-offs - What we gain/lose
What I Do Not Do
- Premature optimization
- Optimize without measuring
- Sacrifice readability for micro-optimizations
- Ignore user experience
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 · 68 lines · 22 tokens per session scan A 7ca0e801e993
performance-optimizer is an agent published in the GitHub repository desilokesh1/antigravity-fullstack-hq (2 stars, last pushed 3d ago), licensed MIT. It adds 22 tokens to every session and 395 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 2026-08-31.
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