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Feature Store Connector

feature-store-connector

jeremylongshore/claude-code-plugins-plus-skills

Automates guidance for setting up and validating feature store connectors within ML deployment pipelines, covering best practices, code generation, and production monitoring for inference.

Figma MCP Integration

tech-leads-club/agent-skills

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.

Figma Design Implementation

figma-implement-design

tech-leads-club/agent-skills

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.

File Format Converter

file-format-converter

jeremylongshore/claude-code-plugins-plus-skills

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

Filtering Query Builder

filtering-query-builder

jeremylongshore/claude-code-plugins-plus-skills

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.

Firebase Vertex AI Fullstack Integration

firebase-vertex-ai

jeremylongshore/claude-code-plugins-plus-skills

This skill guides the end-to-end development, deployment, and secure integration of complex AI features into Firebase applications. It covers setting up Cloud Functions, Firestore/Storage rules, Authentication, and connecting robustly with Vertex AI/Gemini for advanced AI capabilities. Focuses on building production-grade, highly secure, data-intensive AI systems.

Firecrawl Architecture Variants Guide

firecrawl-architecture-variants

jeremylongshore/claude-code-plugins-plus-skills

A comprehensive guide to selecting and implementing the optimal Firecrawl architecture pattern for various data ingestion needs. Learn to choose between On-Demand, Scheduled Crawl Pipelines, and Real-Time Ingestion Pipelines based on data volume, required latency, and complexity. This is essential for designing scalable, production-grade scraping infrastructure, including RAG and knowledge base applications.

Deploying Firecrawl Web Scraping Integrations

firecrawl-deploy-integration

jeremylongshore/claude-code-plugins-plus-skills

A comprehensive guide for deploying web scraping applications powered by Firecrawl to various production environments. Covers Vercel serverless functions, Google Cloud Run, and self-hosted Docker setups. Learn how to configure platform-specific secrets, implement API routes, and handle asynchronous crawl results via webhooks for scalable, reliable production service.

Configuring Multi-Environment Firecrawl Setup

firecrawl-multi-env-setup

jeremylongshore/claude-code-plugins-plus-skills

This guide details how to configure Firecrawl for reliable multi-environment operations (Development, Staging, Production). It addresses critical operational concerns, such as managing credit budgets and ensuring separation between environments. Users learn how to implement self-hosted Docker setups for development, manage environment-specific API keys, and integrate safe scraping wrappers into CI/CD pipelines.

Firecrawl Production Deployment Checklist

firecrawl-prod-checklist

jeremylongshore/claude-code-plugins-plus-skills

A comprehensive checklist for securely deploying and validating applications using Firecrawl's core APIs (scrape, crawl, map, extract) in a production environment. It covers critical areas including secure credential management, implementing crawl safety limits, robust error handling (rate limits, authentication failures), establishing monitoring alerts, and executing necessary rollback procedures.

Building Reliable Web Scraping Patterns

firecrawl-reliability-patterns

jeremylongshore/claude-code-plugins-plus-skills

A comprehensive guide to implementing production-grade reliability patterns for web scraping pipelines using Firecrawl. Learn techniques such as circuit breakers, crawl fallbacks, content quality validation, and robust backoff strategies to handle timeouts, API failures, and inconsistent data quality, ensuring maximum uptime and data integrity.

Firecrawl SDK Patterns for Production Use

firecrawl-sdk-patterns

jeremylongshore/claude-code-plugins-plus-skills

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.

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