A comprehensive optimization toolkit for boosting web application speed and minimizing LLM API expenditures through intelligent resource management.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install performance-ecc
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install performance-ecc using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Performance Optimization (Web & LLM Cost)?
This skill provides a dual-focus framework designed to maximize application efficiency. It covers deep web performance optimization, including Core Web Vitals, resource budgets, and runtime execution, alongside sophisticated LLM cost management strategies. By utilizing these Openclaw Skills, developers can build blazing-fast user interfaces while maintaining sustainable API budgets through techniques like model routing and prompt caching.
Whether you are reducing JavaScript execution time or managing token usage for Claude or GPT models, this skill bridges the gap between high-end frontend engineering and cost-aware AI integration. It focuses on actionable patterns that ensure high performance is achieved without escalating infrastructure or API costs.
Performance Optimization (Web & LLM Cost) Use Cases
- Auditing and improving Core Web Vitals metrics like LCP, TBT, and CLS for better SEO.
- Reducing web page weight and JavaScript parse times through code splitting and tree shaking.
- Implementing intelligent model routing to switch between expensive and budget-friendly LLMs based on task complexity.
- Managing high-volume batch processing with immutable cost tracking and budget-safe guardrails.
- Optimizing asset delivery using modern formats like AVIF, WebP, and variable fonts.
How Performance Optimization (Web & LLM Cost) Works
- Identify performance bottlenecks across frontend code, static assets, and external LLM API dependencies.
- Prioritize optimizations based on their specific impact on user-centric metrics and financial budgets.
- Apply technical enhancements such as critical path rendering, service worker caching, and prompt ephemeral storage.
- Measure improvements using before/after metrics to validate that the Openclaw Skills implementation meets defined performance and cost targets.
Performance Optimization (Web & LLM Cost) Setup
To start optimizing your project, benchmark your current site performance using the Lighthouse CLI:
npx lighthouse https://example.com --output html --output-path report.html
For LLM cost control, integrate the cost-tracking pipeline into your TypeScript environment to monitor token consumption and set budget limits. Ensure your API calls utilize exponential backoff for transient errors to prevent wasted spend during outages.
Performance Optimization (Web & LLM Cost) Data Schema & Taxonomy
The skill organizes optimization data using clear budget frameworks and immutable records:
| Resource Category | Budget Target | Tracking Method |
|---|---|---|
| Total Page Weight | < 1.5 MB | Bundle Analysis |
| Critical JS | < 300 KB (Compressed) | Build Metrics |
| LLM Input/Output | Per-Model Token Rates | Immutable CostRecord |
| API Spend | Defined USD Budget | CostTracker Interface |
All cost data is stored in frozen records, ensuring that every API interaction returns a fresh state for reliable auditing within the Openclaw Skills ecosystem.
Performance Optimization (Web & LLM Cost) Advanced Features
- Intelligent Task Routing: Select models like Claude Sonnet for complex reasoning and Haiku for simple extraction based on character thresholds.
- Prompt Caching: Leverage cache_control metadata to reduce latency and costs for repetitive system prompts over 1024 tokens.
- Facade Patterns: Delay third-party script execution (e.g., video embeds) until user interaction to preserve the main thread.
- Layout Thrashing Prevention: Batch DOM reads and writes using requestAnimationFrame to maintain 60fps runtime performance.
- Content Visibility: Use CSS containment and virtualization to render large lists with minimal initial CPU overhead.
SKILL.md
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