Skill UI

Automates fastify plugin creation within backend development workflows by guiding through best practices, generating production-ready code, and validating configurations for microservices, caching, and database integrations.

Automatically activates when you mention feature engineering helper, offering guidance on data preparation, model training, hyperparameter tuning, and experiment tracking for ML projects.

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.

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

This skill provides structured guidance and patterns for performing comprehensive web fuzzing using the powerful command-line tool, ffuf. Web fuzzing is an essential security testing technique used by penetration testers to discover hidden directories, unlinked endpoints, or sensitive files on a web server by systematically brute-forcing potential paths and parameters. Use this skill when you need to audit the security posture of a web application or map out unknown parts of a website.

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.

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

Provides expert guidance on secure and performant file upload mechanisms for cloud environments. Covers best practices for using services like AWS S3 and Cloudflare R2, including implementing presigned URLs, handling large files via streaming, and optimizing images. Crucially emphasizes critical security measures such as validating file types using magic bytes, enforcing size limits, and preventing path traversal attacks.

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.

A comprehensive pipeline for processing, validating, and optimizing scraped web content from Firecrawl. This tool handles markdown cleaning, structured data extraction using Zod validation, content deduplication, and chunking tailored for Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems. It ensures crawled data is standardized, clean, and ready for downstream consumption or knowledge base indexing.

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.

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.