Skill UI

This skill provides comprehensive CI/CD pipeline setup for Fly.io edge deployments. It automates unit testing on pull requests, deploys to staging environments, and promotes changes to production upon merging to the main branch. It handles Docker builds, uses flyctl for deployment, and includes health checks to ensure service reliability and seamless automated delivery.

This comprehensive bundle collects all necessary diagnostic evidence for Fly.io applications, including machine status, application logs, volume metadata, and network diagnostics. It is crucial for generating detailed support tickets and troubleshooting complex infrastructure issues such as stuck deployments, boot failures, or edge networking problems.

This skill provides advanced integration for deploying applications on Fly.io, focusing on edge computing architectures. It covers building production-ready Docker images, configuring `fly.toml` for services and health checks, and executing sophisticated deployment strategies like blue-green and canary releases. It ensures reliable, scalable deployment across multiple regions, including automated rollbacks and micro-VM optimization.

This guide demonstrates how to deploy a minimal "Hello World" application to the Fly.io platform. It covers two primary deployment methods: using the simple `flyctl` Command Line Interface (CLI) for quick setup, and utilizing the advanced Machines API for programmatic deployment. This skill is ideal for beginners starting a new Fly.io project, or for implementing robust, globally distributed edge compute infrastructure.

This comprehensive checklist guides developers through hardening their application for production deployment on Fly.io. It covers critical aspects such as multi-region redundancy, secure secret management, robust health checks, graceful shutdown procedures, and automated rollback mechanisms. Use it to ensure your edge-deployed application is highly available, reliable, and resilient against common operational failures.

A comprehensive TypeScript SDK providing production-ready patterns for interacting with the Fly.io Machines REST API. It includes a typed client for managing the entire machine lifecycle—creation, state polling, and deletion—across multiple global regions. Features like robust error handling, rate-limit retries, and deployment builders ensure reliable multi-region orchestration for edge computing applications.

This workflow demonstrates the core capabilities of the Glean Client API, allowing users to build comprehensive knowledge assistants. It covers advanced full-text search with filters and facets, implements AI-powered chat for contextual Q&A, and provides autocomplete suggestions. Ideal for integrating deep search and conversational AI into enterprise applications.

This document outlines the reference architecture for integrating diverse enterprise knowledge sources (such as Confluence, Jira, Notion, and Slack) into the Glean search index. It details the end-to-end data flow, covering connector frameworks, message queuing, permission synchronization (ACLs), and client-side search APIs. Designed to ensure continuous, reliable, and low-latency unified search across all corporate data.

A comprehensive guide to securing HubSpot API integrations. Details best practices for implementing least-privilege scopes, securely managing access tokens in environment variables, validating webhook signatures (v3), and executing proper token rotation procedures. Essential knowledge for building robust and secure applications connected to HubSpot.

This skill helps users create specialized 'steering documents' or project memory for highly specific domains (e.g., API standards, testing strategies, security patterns). By analyzing the existing codebase and following predefined architectural principles, it synthesizes concise, actionable guides that capture technical patterns, best practices, and domain-specific rules, ensuring project consistency and knowledge retention.

This skill provides a comprehensive guide to building robust LangGraph 1.0 StateGraphs in Python. It covers defining typed TypedDict states with reducers, writing deterministic nodes, and defensively wiring conditional edges. It addresses common pitfalls such as silent graph termination, incorrect state updates using reducers, proper management of recursion limits, and handling evolving checkpoint schemas for reliable LLM workflow orchestration.

This skill transforms an LLM into a disciplined wiki maintainer for Obsidian. It moves beyond simple RAG by incrementally ingesting sources (papers, notes, articles) to build a persistent, interlinked "Second Brain." The LLM manages cross-references, updates core concepts, and maintains an evolving knowledge graph, ensuring that knowledge compounds over time rather than being re-derived on every query. Ideal for deep research, personal knowledge management (PKM), and building comprehensive internal wikis.