Skill UI

Automates adapting pretrained models via transfer learning by analyzing requirements, generating PyTorch or TensorFlow fine-tuning code, validating data, tracking KPIs, and saving artifacts so you can quickly reuse existing models for new datasets and tasks.

Guides you through building Airflow operators for data pipelines with automated best practices, production-ready code/config generation, and validation for ETL, transformation, orchestration, and streaming scenarios.

Expert in developing complete, high-performance firmware and peripheral drivers for ARM Cortex-M platforms (STM32, Teensy, nRF52). Specializes in low-level hardware interaction, including implementing drivers for I²C, SPI, UART, and managing complex concurrency patterns using FreeRTOS/Zephyr. Provides critical guidance on optimizing for determinism, cache coherency, DMA transfers, and memory barriers to ensure reliable embedded system operation.

Azure Data Lake Storage Gen2 SDK for Python enables hierarchical file system management, ACL control, metadata handling, and async transfers for big data analytics workflows hosted in Azure Storage.

This comprehensive workflow automates the creation of C4 architectural documentation for any codebase. It utilizes a bottom-up analysis approach, starting by analyzing code details (Code Level), synthesizing them into logical components, mapping containers, and finally establishing the high-level system context. Ideal for documenting complex, existing systems and ensuring comprehensive technical knowledge transfer.

This meta-skill is designed to guide and execute structured developmental workflows, enabling continuous skill improvement and knowledge transfer. It is applicable when a task requires following a defined developmental process. Users must ensure all necessary inputs, permissions, and success criteria are provided, and must treat the output as a guide, supplementing professional validation.

Transforms git commits into polished, customer-ready changelog entries by analyzing history, categorizing features/bug fixes, filtering noise, and applying consistent brand voice so releases are ready in minutes.

Automates beam pipeline builder tasks by guiding best practices, generating production-ready code, and validating ETL, transformation, orchestration, and streaming workflows.

Use before creative or constructive work (features, architecture, behavior). Transforms vague ideas into validated designs through disciplined reasoning and collaboration.

Design and deploy an API gateway that unifies routing, load balancing, auth enforcement, rate limiting, transformations, and aggregation across multiple services, providing centralized observability and resilience patterns for microservice architectures.

Enables Claude to configure neural network architectures through the neural-network-builder plugin, creating or modifying models and defining layers, activation functions, and training parameters whenever network design or tuning is requested.

This guide details setting up robust CI/CD pipelines for applications utilizing Clay. It covers automated unit and integration testing for webhook handlers, validating data schema compliance, and ensuring the reliability of data enrichment processes within your build workflow. Ideal for teams deploying webhooks or complex data transformation logic.