configuring-connection-pools
Optimizes Prisma Client connection pool settings for production databases, serverless environments, and high-concurrency architectures to prevent connection exhaustion and performance bottlenecks.
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320 skills found
Optimizes Prisma Client connection pool settings for production databases, serverless environments, and high-concurrency architectures to prevent connection exhaustion and performance bottlenecks.
Audit and optimize SwiftUI runtime performance. Diagnose slow rendering, janky scrolling, high resource usage, and layout thrash with code review, architecture analysis, and Instruments profiling guidance.
A high-performance Liquid template engine that compiles templates into optimized Ruby and machine code via an intermediate language (IL).
Pragmatic AI-assisted coding standards focused on clean code, simplicity, and maintainability. Enforces best practices like SRP, DRY, and KISS to prevent over-engineering.
Guidelines for testing HashQL code using compiletest (UI tests), unit tests, and insta snapshots. Includes commands for --bless, annotation syntax, and strategies for compiler components.
Streamline your codebase by automatically removing redundant or obvious comments while preserving essential architectural and logic-focused documentation.
Capture snapshots, video clips, and monitor motion events from RTSP and ONVIF compatible security cameras.
AI-assisted version control for code agents. Track prompts, context, and diffs automatically with MemoV to ensure full traceability without polluting your git history.
Brainstorm differentiated product positioning strategies, identify key competitors, and craft compelling positioning statements with strategic rationale.
Transform technical documentation into a growth engine. Learn to write docs that improve SEO, reduce time-to-value, and convert developers by mastering information architecture and developer-focused writing.
Multi-LLM code review pipeline using consensus-based analysis to detect security, architectural, and quality issues.
Guidelines for curating high-quality datasets for LLM post-training (SFT/DPO/RLHF), covering data formats, quality filtering, and collection strategies.