Skill UI

Walks through broken authentication assessments by analyzing login flows, password policies, brute-force/credential-stuffing, and session/token security to document vulnerabilities and mitigation steps for web applications.

Guides analysts through assessing web authentication, credential stuffing, password policies, session tokens, and fixation flows to uncover broken authentication risks before attackers do.

A comprehensive guide to utilizing a core, token-driven component library and design system patterns. It details best practices for managing UI styles—including spacing, color, and typography—by using semantic design tokens instead of hard-coded values. Ideal for building consistent, scalable, and maintainable user interfaces across different platforms.

Implements robust mechanisms for handling API rate limits (e.g., 429 errors) when interacting with services like Customer.io. It utilizes the Token Bucket algorithm for precise throttling and combines it with exponential backoff, jitter, and queue processing (p-queue) to ensure high reliability and prevent API overloads.

A comprehensive guide detailing the setup process for Databricks CLI v2 and Python SDK. It covers essential steps for connecting to various Databricks workspaces, including configuring Personal Access Tokens (PAT), interactive OAuth U2M flows, and secure OAuth M2M service principals for CI/CD pipelines. Essential for developers integrating Databricks into their projects.

A comprehensive guide and implementation pattern for managing rate limits, throttling, and ensuring idempotency when interacting with the Databricks API. This skill demonstrates techniques like exponential backoff with jitter, token-bucket rate limiting, and using idempotency tokens to make bulk and concurrent API calls reliable and efficient, minimizing HTTP 429 errors.

Defuddle CLI extracts clutter-free markdown from web pages, stripping navigation and ads to save tokens when analyzing documentation, articles, or blogs; prefer it over WebFetch for non-.md URLs.

A comprehensive toolkit designed to debug and troubleshoot Evernote API integrations. It provides deep logging of requests, responses, and timings. Features include an instrumented client wrapper for zero-config tracing, ENML content validation with auto-fix capabilities, and token inspection utilities to diagnose connectivity issues and quota limits.

This guide provides a comprehensive setup for an efficient local development workflow when integrating with the Evernote API. It covers establishing a proper project structure, configuring sandbox credentials, and implementing both Developer Token and full OAuth flows. Use this when setting up testing environments and ensuring reliable API connectivity.

This guide provides comprehensive best practices for securing Evernote API integrations. It covers essential topics like secure credential management using environment variables or vaults, implementing CSRF-protected OAuth flows, encrypting access tokens at rest using AES-256-GCM, rigorous input validation, and ensuring secure logging practices by redacting sensitive data. Use this when hardening your application against data breaches.

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.

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.