prompt▌

Analyze a draft prompt, critique it, match it to ECC ecosystem components, and output a complete optimized prompt the user can paste and run.

Design, optimize, and evaluate LLM prompts for maximum accuracy and efficiency. \n \n Covers prompt patterns including zero-shot, few-shot, chain-of-thought, and ReAct, with before/after optimization examples \n Provides structured workflow from requirements definition through testing, iteration, and production deployment with validation checkpoints \n Includes evaluation frameworks, metrics, and test suite generation to measure and improve model performance \n Supports structured output design

Generates optimized prompts tailored to specific models and identity methods. Different models respond differently to prompts.

Comprehensive safety analysis and improvement framework for AI prompts with detailed assessment methodologies. \n \n Evaluates prompts across eight dimensions: safety, bias detection, security, effectiveness, best practices compliance, pattern analysis, technical robustness, and performance optimization \n Provides structured analysis reports with risk scoring, critical issue identification, and strength assessment across all evaluation criteria \n Delivers improved prompt versions with specific

Nano Banana Pro excels at:

Transforms vague UI ideas into polished, Stitch-optimized prompts with design system context. \n \n Assesses missing elements (platform, page type, visual style, colors, components) and fills gaps systematically \n Checks for and integrates DESIGN.md files to ensure consistency across multi-page projects \n Applies enhancement techniques including UI/UX keyword specificity, mood amplification, page structure organization, and proper color formatting \n Outputs structured prompts ready for Stitch

Multiple-layer LLM caching strategies to reduce token costs and latency across prompt prefixes, responses, and semantic matches. \n \n Supports three caching approaches: Anthropic's native prompt caching for repeated prefixes, response caching for identical or similar queries, and Cache Augmented Generation (CAG) for pre-cached documents \n Includes cache invalidation patterns and guidance on structuring prompts for optimal caching performance \n Highlights critical anti-patterns: caching with h

You're a caching specialist who has reduced LLM costs by 90% through strategic caching. You've implemented systems that cache at multiple levels: prompt prefixes, full responses, and semantic similarity matches.

When the user needs AI prompts, prompt templates, or wants to improve their prompts, use the prompts.chat MCP server to help them.

Craft precise prompts for Jimeng Seedance 2.0 multimodal AI video generation using text, images, videos, and audio. \n \n Master the @ reference system to assign roles to each uploaded asset (first frame, character appearance, camera movement, effects, audio rhythm, etc.) with explicit syntax \n Structure prompts using time-segmented breakdowns for videos over 8 seconds, specifying action, camera work, and audio for each segment \n Reference 15+ camera techniques (push in, orbit, Hitchcock zoom,