optimize▌

Optimize content for AI search engines through citation architecture, E-E-A-T signals, and entity building. \n \n Guides content structuring for AI citation across ChatGPT, Perplexity, Claude, Gemini, and Google AI Overviews — focusing on extractability, quotability, authority, freshness, and entity clarity \n Provides AI Citation Scoring framework (5 dimensions, 20+ checklist items) and before/after examples showing how to transform weak content into citable statements \n Covers per-engine opti

Optimize tool descriptions in ToolUniverse JSON configuration files to ensure they are clear, complete, and user-friendly.

Best practices for high-quality research skills with evidence grading and source attribution.

Largest Contentful Paint (LCP) measures how quickly a page's main content becomes visible. It's the time from navigation start until the largest image or text block renders in the viewport.

The Multi-Agent Optimization Tool is an advanced AI-driven framework designed to holistically improve system performance through intelligent, coordinated agent-based optimization. Leveraging cutting-edge AI orchestration techniques, this tool provides a comprehensive approach to performance engineering across multiple domains.

Backtesting strategy parameter optimization with VectorBT, generating performance heatmaps and benchmark comparisons. \n \n Accepts strategy name, symbol, exchange, and interval; creates optimization script in backtesting/{strategy}/ directory \n Loads market data from OpenAlgo via .env configuration or directly from DuckDB; uses TA-Lib for all indicators with OpenAlgo ta for specialty indicators like Supertrend and Donchian \n Tests parameter combinations across sensible ranges (e.g., EMA 5-50

Streamline Claude Code memory files through 4 interactive optimization steps. \n \n Removes duplicate entries already present in CLAUDE.md or rules files, then migrates remaining entries to appropriate persistent config locations \n Compresses remaining memory entries using LLM-efficient formatting (prose to tables, verbose descriptions to imperatives) \n Validates final state by checking for broken references, contradictions, and orphaned files \n Typical reduction of 30–50% token count in memo

You are a cloud cost optimization expert specializing in reducing infrastructure expenses while maintaining performance and reliability. Analyze cloud spending, identify savings opportunities, and implement cost-effective architectures across AWS, Azure, and GCP.

Systematically identify and fix performance bottlenecks across loading, rendering, animations, and bundle size. \n \n Covers five optimization areas: image optimization, JavaScript/CSS reduction, font loading, rendering performance, and animation efficiency \n Includes Core Web Vitals guidance (LCP, FID/INP, CLS) with specific thresholds and remediation strategies \n Provides code examples for common patterns: lazy loading, code splitting, layout thrashing prevention, GPU-accelerated animations,

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