Skill UI

Automates guidance on assessing feature importance for machine learning training, covering data prep, model training, tuning, and experiment tracking while generating production-ready code, configurations, and validation instructions for best-practice compliance.

Guides use of the Figma MCP server to pull design context, screenshots, variables, and assets, then convert nodes into production-ready code following project conventions and validation steps.

Implements user-provided Figma nodes into production-ready code via the MCP workflow, ensuring design-token compliance, asset handling, and 1:1 visual fidelity for agreed UI components.

Automates file format conversion guidance within data pipelines, covering ETL, workflow orchestration, data transformation, and streaming standards with production-ready examples.

Automates guidance for API filtering query builder tasks, offering industry best practices, production-ready code, and validation across REST, GraphQL, OpenAPI, and authentication design patterns.

This guide provides production-ready patterns for integrating the Firecrawl SDK in TypeScript and Python. It covers essential architectural best practices, such as implementing singleton clients, typed service wrappers, automatic retries with exponential backoff, concurrency management, and robust response validation using Zod. Ideal for building scalable, reliable web scraping services.

This resource provides production-ready, type-safe GraphQL client patterns for integrating with the Fireflies.ai API in both TypeScript and Python. It addresses critical engineering concerns such as robust error handling, managing singletons, implementing multi-tenant support, and ensuring structured data validation using best practices.

This guide outlines essential security protocols for integrating Fireflies.ai. It covers best practices for managing sensitive API keys (e.g., environment variables, pre-commit hooks), implementing robust HMAC-SHA256 webhook signature verification in multiple languages (TypeScript, Python), and correctly configuring transcript privacy levels to ensure data confidentiality. Ideal for developers setting up production-ready integrations.

Automated Firestore index guidance for GCP Skills, triggering on index creator requests to deliver best-practice configs, validation, and production-ready code for Firebase/BigQuery/Vertex AI workloads.

Automates firewall rule generation within GCP Skills by responding when you mention firewall rule generator, delivering best-practice guidance, production-ready configs, and validation for compute, storage, BigQuery, Vertex AI, and related services.

Automates Flask blueprint creation and related backend guidance, triggering whenever users request blueprint creator help or backend best practices; generates production-ready code, validations, and covers node.js/python/go microservices patterns.

Automated assistance for Flink job creation in data pipelines, triggered by Flink job creator requests. Guides ETL, streaming, and workflow orchestration, generates production-ready code/configurations, and validates outputs against industry standards.