Universal data lake and lakehouse patterns covering ingestion (dlt, Airbyte), transformation (SQLMesh, dbt), storage formats (Iceberg, Delta, Hudi, Parquet), query engines (ClickHouse, DuckDB, Doris, StarRocks), streaming (Kafka, Flink), orchestration (Dagster, Airflow, Prefect), and visualization (Metabase, Superset, Grafana). Self-hosted and cloud options.
Expert data engineer specializing in building scalable data pipelines, ETL/ELT processes, and data infrastructure. Masters big data technologies and cloud platforms with focus on reliable, efficient, and cost-optimized data platforms.
End-to-end data science patterns (modern best practices): problem framing -> data -> EDA -> feature engineering (with feature stores) -> modelling -> evaluation -> reporting, plus SQL transformation (SQLMesh). Emphasizes MLOps integration, drift monitoring, and production-ready workflows.
Master orchestration for routing technical problems through 32 engineering skills - AI/ML, software development, data, APIs, and Claude Code framework
Expert LLM architect specializing in large language model architecture, deployment, and optimization. Masters LLM system design, fine-tuning strategies, and production serving with focus on building scalable, efficient, and safe LLM applications.
Expert error coordinator specializing in distributed error handling, failure recovery, and system resilience. Masters error correlation, cascade prevention, and automated recovery strategies across multi-agent systems with focus on minimizing impact and learning from failures.
Configure and build Model Context Protocol (MCP) servers for Claude Code integration. Set up database, filesystem, git, and API connections. Build custom MCP servers with TypeScript/Python SDK, implement tools and resources, configure transports (stdio, HTTP), and deploy for production.
Expert prompt engineer specializing in designing, optimizing, and managing prompts for large language models. Masters prompt architecture, evaluation frameworks, and production prompt systems with focus on reliability, efficiency, and measurable outcomes.
Expert MCP developer specializing in Model Context Protocol server and client development. Masters protocol specification, SDK implementation, and building production-ready integrations between AI systems and external tools/data sources.
Create and configure Claude Code agents with YAML frontmatter, tool selection, model specification, and naming conventions. Reference for building specialized AI subagents that handle complex, multi-step tasks.
Design AI-first help centers, knowledge bases, FAQs, and learning materials using 2025-2026 best practices
Create event-driven hooks for Claude Code automation. Configure PreToolUse, PostToolUse, Stop, and other hook events with bash scripts, environment variables, matchers, and exit codes.
Implement comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and benchmarking. Use when testing LLM performance, measuring AI application quality, or establishing evaluation frameworks.
Operational patterns for LLM inference: latency budgeting, tail-latency control, caching, batching/scheduling, quantization/compression, parallelism, and reliable serving at scale. Emphasizes production-grade performance, cost control, and observability.
Write PRDs, specs, and project context optimized for coding assistants (Claude Code, Cursor, Copilot, Custom GPTs). Includes CLAUDE.md generation, session planning, and templates for creating documentation that tools can execute effectively.
Expert task distributor specializing in intelligent work allocation, load balancing, and queue management. Masters priority scheduling, capacity tracking, and fair distribution with focus on maximizing throughput while maintaining quality and meeting deadlines.
Expert ML engineer specializing in production model deployment, serving infrastructure, and scalable ML systems. Masters model optimization, real-time inference, and edge deployment with focus on reliability and performance at scale.
Work with state-of-the-art machine learning models for NLP, computer vision, audio, and multimodal tasks using HuggingFace Transformers. This skill should be used when fine-tuning pre-trained models, performing inference with pipelines, generating text, training sequence models, or working with BERT, GPT, T5, ViT, and other transformer architectures. Covers model loading, tokenization, training with Trainer API, text generation strategies, and task-specific patterns for classification, NER, QA, summarization, translation, and image tasks. (plugin:scientific-packages@claude-scientific-skills)
