Skill UI

This utility provides programmatic access to Granola meeting data, enabling developers to build automated post-meeting workflows. Users can read meeting notes from the local cache, integrate with MCP servers for AI context, automatically extract action items and assignees, and sync outcomes directly into coding tasks, project documentation, or issue trackers like GitHub. Ideal for automating the development lifecycle from meeting insights.

Automates hardcoded credential discovery by guiding you through secure coding patterns, validation checks, and remediation strategies tailored to security fundamentals engagements.

Assists with security fundamentals by auto-activating when asked about https certificate checker tasks, guiding through detection patterns, secure coding, and validation workflows for reliable certificate handling.

Automates input validation checker guidance within the Security Fundamentals category, offering best practices, secure coding patterns, production-ready snippets, and output validation for common standards.

Automates guidance and code for identifying insecure deserialization flaws, following Security Fundamentals best practices to help with authentication, input validation, and secure coding checks when that phrase is mentioned.

Defines Java 17+ coding standards for Spring Boot services, covering naming, immutability, Optional usage, streams, exceptions, generics, project layout, and testing practices to ensure readable, maintainable code.

This skill specializes in writing, debugging, and refactoring modern JavaScript code (ES2023+). It covers advanced patterns like async/await, Promise handling, ESM modules, and optimal performance for both browser and Node.js environments. Use it for vanilla applications, complex async flows, and performance optimization.

Guides deliberate skill development during AI-assisted coding by offering optional 10–15 minute exercises after architecture work, schema changes, or “why” questions so developers stay engaged and understand decisions.

Automated license compliance scanner guidance for Security Fundamentals, delivering step-by-step practices, best-practice patterns, and production-ready code plus validation to keep authentication, validation, and secure coding efforts aligned.

This guide provides production-grade coding patterns for integrating Lokalise into TypeScript and Node.js applications. It covers crucial best practices such as client singleton implementation, robust cursor-based pagination for large datasets, structured error handling with retry logic, and efficient batch operations. Implementing these patterns ensures reliable, scalable, and maintainable API usage.

Extends transformer context windows using RoPE, YaRN, ALiBi, and position interpolation so long documents (32k-128k+ tokens) can be processed, pre-trained models lengthened, and efficient positional encodings or extrapolation strategies deployed.

Memory provides persistent context across multiple coding sessions. It stores crucial project decisions, user preferences (like preferred package managers or specific libraries), and architectural conventions in a local memory file. This ensures that the AI maintains consistency, recalls past choices automatically, and applies project-specific rules without needing constant reminders, significantly improving workflow continuity and reliability.