Skill UI

This comprehensive guide details essential security practices for integrating the Exa API. It covers key areas such as secure API key management using environment variables, implementing content moderation to filter unsafe results, restricting results via domain allow/block lists, and sanitizing user queries to prevent injection attacks. Use this when hardening or auditing an Exa-powered application.

This advanced workflow executes sophisticated web data gathering tasks. It supports three primary functions: LLM-powered structured data extraction using custom JSON schemas or prompts; efficient batch scraping of multiple known URLs; and rapid site mapping to discover all reachable URLs on a domain. Use this when you require reliable, typed data structures rather than raw markdown content.

This guide outlines how to implement robust Role-Based Access Control (RBAC) for Firecrawl scraping resources. It allows enterprises to manage multiple API keys, enforce per-team credit consumption limits, and restrict scraping to specific allowed domains (allowlisting). Use this when scaling scraping operations across various departments or services that require granular resource governance and budget tracking.

This skill provides robust, automated guardrails for web scraping pipelines using Firecrawl. It enforces critical policies to ensure compliance and stability, including domain blocklists (preventing scraping prohibited sites), managing API credit budgets, filtering low-quality content (e.g., error pages, thin content), and implementing granular per-domain rate limiting. Use this when building highly reliable, legal, and cost-efficient scraping solutions.

This guide details integrating FoundationModels for running Large Language Models (LLMs) directly on the device (iOS 26). It covers advanced patterns including text generation, extracting structured data using @Generable, implementing custom tool calling for domain logic, and streaming responses for real-time UI updates. Key features emphasize strong privacy and offline capability, ensuring no data leaves the device.

This skill acts as a comprehensive security scanner specializing in detecting Cross-Site Scripting (XSS) vulnerabilities in client-side code. It performs static analysis across React, Vue, Angular, and vanilla JavaScript, specifically identifying unsafe practices like direct HTML manipulation (innerHTML), raw rendering (v-html), and insecure URL handling. Use it when auditing frontend code for security flaws and needing best practices for secure coding.

An expert skill dedicated to building secure, robust frontend applications. It specializes in preventing client-side vulnerabilities such as XSS and clickjacking. Core capabilities include safe DOM manipulation, implementing Content Security Policy (CSP), advanced input sanitization (allowlisting), and securing complex interactions like secure redirects and session management.

Automated fuzz testing of APIs via the /fuzz-api command to inject malformed, boundary, and random payloads, spotting crashes, SQL injection, XSS, and validation gaps during vulnerability scanning or security reviews.

Fuzzing Dictionary supplies domain-specific tokens to guide fuzzers when targeting parsers, protocols, configs, or file formats so they reach deeper logic; include magic bytes, headers, keywords, and combine generation from LLMs, headers, or binaries before passing via the fuzzer flag.

Automates guidance for Github Actions starters within the DevOps Basics domain, offering step-by-step best practices, production-ready configurations, and validation so teams can launch CI/CD, Git, Docker, and infrastructure workflows with confidence.

A production-grade framework for executing high-performance, robust browser automation and web scraping using Playwright with the Go language. This skill specializes in handling complex Single Page Applications (SPAs), emulating human behavior (e.g., random typing delays, Bezier mouse movements), and implementing advanced stealth techniques to bypass anti-bot systems like Cloudflare. It emphasizes architectural best practices, such as using contexts for isolation and structured logging.

Guides developers in crafting and refining fuzzing harnesses that parse random fuzzer input, validate data, and exercise targeted API paths to improve coverage and find reproducible bugs.