Discover reusable agent skills, browse implementation details, and find the right skill for your workflow.
135 skills found
Classical machine learning with scikit-learn. Use for classification, regression, clustering, dimensionality reduction, preprocessing, model evaluation, and building robust ML pipelines in Python.
Analyze local system hardware (RAM, CPU, GPU/VRAM) to receive expert recommendations for optimized local LLM models, quantization settings, and performance estimates.
Optimizes Prisma Client connection pool settings for production databases, serverless environments, and high-concurrency architectures to prevent connection exhaustion and performance bottlenecks.
Autonomous improvement loop for codebase optimization. Automatically modifies, measures, and iterates on code based on a specific goal and mechanical metric.
A framework to transform experimental ML prototypes into robust, production-ready Python packages using src layout, hybrid architecture, and strict configuration management.
Advanced context engineering system for orchestrating AI agents, memory management, and token optimization to improve long-term persistence and project intelligence.
Optimize your App Store and Google Play metadata with expert ASO frameworks. Craft high-ranking titles, descriptions, and keyword fields to maximize search visibility and conversion rates for iOS and Android.
Optimize Node.js performance via Redis caching, clustering, profiling, and monitoring to build fast, scalable, and efficient backend services.
Foundational guidelines for context engineering: optimizing token budgets, attention mechanics, and system architecture for AI agents.
Expert guidance and configuration standards for creating specialized OpenCode AI agents, including YAML frontmatter, tool permissions, and operational modes.
Comprehensive reference for GrepAI configuration, detailing the .grepai/config.yaml schema, embedder settings, storage backends, and optimization parameters.
PyTorch Lightning skill for scalable deep learning: automates model training, multi-GPU orchestration, data pipelines, and distributed training strategies like DDP, FSDP, and DeepSpeed.