productivity▌

Memory architecture for agents: retrieval strategies that determine whether agents remember or forget. \n \n Covers five memory types: short-term (context window), long-term (vector stores), working memory, episodic memory, and semantic memory, each suited to different information patterns \n Emphasizes retrieval as the core challenge; provides chunking strategies, embedding quality guidance, and metadata filtering to surface the right memories at decision time \n Includes anti-patterns like sto

Comprehensive performance optimization guide for Postgres, maintained by Supabase. Contains rules across 8 categories, prioritized by impact to guide automated query optimization and schema design.

yeet

Guides GEO/AEO strategy for AI search visibility. GEO optimizes content for ChatGPT, Claude, Perplexity, and AI search summaries (Google AI Overviews, Bing Copilot, Yandex Search with AI)—getting cited in AI-generated answers rather than ranking in traditional SERPs. See serp-features for AI search as SERP features; featured-snippet for snippet optimization that overlaps with AI Overviews.

This skill enables real-time research information lookup using Perplexity's Sonar models through OpenRouter. It intelligently selects between Sonar Pro Search (fast, efficient lookup) and Sonar Reasoning Pro (deep analytical reasoning) based on query complexity. The skill provides access to current academic literature, recent studies, technical documentation, and general research information with proper citations and source attribution.

Expert patterns for Neon serverless Postgres, branching, connection pooling, and ORM integration. \n \n Covers Prisma and Drizzle ORM setup with Neon, including dual connection string configuration (pooled vs. direct) for migrations and application queries \n Explains PgBouncer connection pooling limits (10K concurrent connections) and reserved superuser connections \n Provides guidance on choosing between Neon's neon-http driver (single queries) and neon-serverless driver (transactions and WebS

Before dispatching, analyze the task systematically. Think through step by step:

Build and scale partner ecosystems that drive revenue and platform adoption. These aren't theory — they're patterns from building partner programs that drove 8-figure ARR and observing partnerships with real economic commitment.

Technical rigor in code review: receiving feedback critically, requesting systematic reviews, and verifying before any completion claims. \n \n Three core practices: receiving feedback with technical evaluation over performative agreement, requesting reviews via code-reviewer subagent, and verification gates requiring evidence before status claims \n Includes decision tree and protocols for handling feedback from human partners versus external reviewers, with explicit rules against performative

This skill provides a set of best practices and patterns for creating ViewModels, Wizards, and managing navigation in Avalonia applications, leveraging the power of ReactiveUI and the Zafiro toolkit.