Skill UI

A comprehensive guide and expert resource for mastering durable workflow orchestration using the Temporal Python SDK. Covers advanced patterns including distributed transactions, saga implementation, signal handling, and query patterns. Essential for building highly reliable, scalable, long-running backend systems, and mastering async/await execution models for production deployment.

Automates TensorFlow SavedModel deployment support with step-by-step guidance, best-practice patterns, production-ready code/config generation, and validation for serving, monitoring, and optimization scenarios.

Automates terraform module creation within the DevOps Advanced workflow, guiding through best practices, patterns, and production-ready code plus validations.

Automates Terraform provider configuration guidance for advanced DevOps teams, offering best-practice code, IaC patterns, and validation support when working with Kubernetes and CI/CD stacks.

Test Data Builder Guidance auto-activates when you ask about test data strategies, best practices, or configuration for automation frameworks, delivering step-by-step patterns, production-ready samples, and validation against common standards.

Jest testing patterns, factory functions, mocking strategies, and TDD workflow. Use when writing unit tests, creating test factories, or following TDD red-green-refactor cycle.

Automation workflow for The Odds API using Rube MCP via Composio, covering tool discovery, connection checks, execution patterns, pagination handling, and schema compliance so workflows stay reliable.

Automates time series decomposition work in data analytics by guiding through patterns and best practices, generating production-ready code/configuration, and validating outputs for SQL, visualization, and BI workflows.

Analyzes token implementations and integrations with ERC20/ERC721 standards, checks 20+ weird token patterns, assesses owner privileges, contract composition, and on-chain scarcity to highlight risks before deployment.

Automates guidance on train/test split workflows inside ML training, surfacing best-practice patterns, code snippets, and validation tips for PyTorch, TensorFlow, and sklearn projects.

TransformerLens is the standard library for performing mechanistic interpretability research on large language models. It provides clean interfaces to inspect and manipulate transformer internals—such as attention patterns, residual streams, and MLP outputs—via activation caching and HookPoints. This tool is essential for reverse-engineering model algorithms, performing causal tracing, and studying the internal circuits responsible for model behavior.

Automates the creation of technical troubleshooting guides by following documentation best practices, generating templates, and validating outputs for API docs, user guides, and architecture references whenever you mention troubleshooting patterns.