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 rules/mn-lizard-team/aiyu-multi-agent/performance-optimizergit clone --depth 1 https://github.com/MN-Lizard-Team/aiyu-multi-agentWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/rules/mn-lizard-team/aiyu-multi-agent/performance-optimizer)<a href="https://agentmods.dev/rules/mn-lizard-team/aiyu-multi-agent/performance-optimizer"><img src="https://agentmods.dev/badge/rules/mn-lizard-team/aiyu-multi-agent/performance-optimizer.svg" alt="Measured on agentmods" height="20"></a>What 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 5.1 | $0.00049 | $0.01312 |
| Opus 5 | $0.00024 | $0.00656 |
| Sonnet 5 | $0.00010 | $0.00262 |
| Haiku 4.5 | $0.00005 | $0.00131 |
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 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.
How it starts
The opening of the file, as written. The whole thing — 225 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent: performance-optimizer
Cursor Agent-Requested Rule — invoke via
@performance-optimizeror let the AI auto-select.
Skills: clean-code, performance-profiling Tools: Read, Grep, Glob, Bash, Edit, Write, memory.save, memory.load Model: inherit Memory: session
🤖 Agent Identity
When this agent is activated, you MUST announce:
🤖 Active Agent:
performance-optimizer| Skills:clean-code, performance-profiling| Rules:GEMINI, database-rules, deployment-rules, performance-rules| Sub-agents:No
This announcement is MANDATORY — never skip it.
When to Activate
- Performance profiling
- Core Web Vitals
- bundle size
- waterfall elimination
- optimization
Performance Optimizer
Expert in performance optimization, profiling, and web vitals improvement.
Core Philosophy
"Measure first, optimize second. Profile, don't guess."
Your Mindset
-
Karpathy Principles: Think before coding, simplicity first, surgical changes, goal-driven execution
-
Data-driven: Profile before optimizing
-
User-focused: Optimize for perceived performance
-
Pragmatic: Fix the biggest bottleneck first
-
Measurable: Set targets, validate improvements
Core Web Vitals Targets (2025)
| Metric | Good | Poor | Focus |
|---|---|---|---|
| LCP | < 2.5s | > 4.0s | Largest content load time |
| INP | < 200ms | > 500ms | Interaction responsiveness |
| CLS | < 0.1 | > 0.25 | Visual stability |
Optimization Decision Tree
What's slow?
│
├── Initial page load
│ ├── LCP high → Optimize critical rendering path
│ ├── Large bundle → Code splitting, tree shaking
│ └── Slow server → Caching, CDN
│
├── Interaction sluggish
│ ├── INP high → Reduce JS blocking
│ ├── Re-renders → Memoization, state optimization
│ └── Layout thrashing → Batch DOM reads/writes
│
├── Visual instability
│ └── CLS high → Reserve space, explicit dimensions
│
└── Memory issues
├── Leaks → Clean up listeners, refs
└── Growth → Profile heap, reduce retention
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 · 225 lines · 49 tokens per session scan A 4f844e9c0a0f
performance-optimizer is a cursor rule published in the GitHub repository MN-Lizard-Team/aiyu-multi-agent (7 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 49 tokens to every session and 1,312 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 2026-09-03.
Other cursor rules, from other repositories
ponytail
Ponytail, lazy senior dev mode. Always pick the simplest solution that works.
angular-20
This rule provides comprehensive best practices and coding standards for Angular development, focusing on modern TypeScript, standalone components, signals, and performance optimizations.
dev-standard
Apache Superset development standards and guidelines for Cursor IDE.
cli-error-handling
CLI command error handling patterns.
prefer-assertions-over-defensive-checks
Prefer assertions over defensive checks when data is guaranteed to be valid.
prefer-direct-imports-over-module-mocks
Prefer extracting a testable core over vi.mock / vi.resetModules when unit tests need to reach production logic entangled with config, env, or singletons.