Skill UI

An autonomous research agent that plans complex investigations using Directed Acyclic Graphs (DAGs). It executes sub-questions in parallel, performs gap analysis, and iteratively refines findings before synthesizing a comprehensive, structured report. Ideal for deep market analysis, academic literature reviews, or complex problem-solving requiring multiple data sources.

This skill provides the core mechanism for integrating Hex into external data pipelines. It enables triggering, monitoring, and managing Hex projects (e.g., via Airflow or Dagster) programmatically. Users can implement parameterized runs, execute multi-step ETL workflows sequentially, and handle status polling and error management, making it ideal for production data orchestration.

This skill provides blueprints for deploying Clari data export pipelines into robust, production-grade cloud environments. It guides users on setting up reliable, scheduled, and serverless data syncs using major platforms like Airflow DAGs, AWS Lambda, and Google Cloud Functions. Ideal for automating revenue forecasting and business intelligence data extraction.

This guide details the recommended reference architecture and project structure for integrating with the Hex platform. It covers best practices for designing robust data pipelines, including components like orchestration layers (Airflow, Dagster), API clients, and standardized project layouts. Use this when starting new Hex integrations or reviewing complex data workflows.

Master the creation of robust ELT (Extract, Load, Transform) data pipelines within Snowflake. This guide demonstrates advanced data orchestration using Snowflake Streams for Change Data Capture (CDC), Tasks for scheduled transformations and DAG management, and Dynamic Tables for declarative, auto-refreshing data models. Ideal for building sophisticated, scalable data warehousing solutions.

A lightweight utility designed for fetching content from a single URL via HTTP or HTTPS. It supports various methods (GET, POST, PUT, DELETE) and allows configuration of timeouts and maximum response body sizes. This tool is ideal for quick, simple data extraction steps within a workflow DAG, offering significant performance gains over complex LLM agent loops. Note that complex tasks like JS rendering or multi-page crawling require dedicated sub-agents.

This meta-skill is designed to orchestrate the creation of complex, multi-step workflows (meta-skills). It should only be used when a user explicitly requests combining, synthesizing, or proposing a new skill that coordinates multiple existing functions. The process includes intent clarification, trigger collision checking, validation, and generating a detailed proposal containing the workflow DAG.

This skill provides a generic entry point for delegating self-contained, complex tasks to various LLM-powered sub-agents (Codex, Claude Code, Pi). It is designed to be used in meta-skill DAG steps for tasks like feature creation, PR reviews, code refactoring, or document generation, providing full tool surface access. It favors non-interactive CLI modes for reliable background execution.

This skill provides explicit, pedagogical instruction on complex CrossFrame concepts. It is designed to move beyond simple definitions by structuring the output into plain language explanations, conceptual mapping, common misreading boundaries, real-world signals, and practical exercises. It is ideal for users who need deep, structured, and comprehensive understanding of complex theoretical frameworks.

A comprehensive guide covering patterns and architectures for automating code execution using LLMs. It spans simple sequential pipelines (e.g., daily dev scripts) to advanced, RFC-driven multi-agent Directed Acyclic Graph (DAG) orchestration. Ideal for building CI/CD style continuous development pipelines, managing complex state, and automating quality gates without human intervention.

A robust framework for orchestrating complex, multi-step coding tasks using multiple AI agents. It functions by first planning the entire project into a mission dependency graph (DAG), then dispatching specialized agents to isolated Git worktrees for parallel development. The system automatically monitors progress, handles merges, and generates structured, comprehensive reports detailing changes, completed tasks, and next steps.

This advanced pipeline orchestrates complex feature development based on Request for Comments (RFCs). It breaks down monolithic changes into small, independently verifiable work units, managing dependencies through a Directed Acyclic Graph (DAG). It includes multiple quality gates, dedicated unit testing, and a safe merge queue to ensure robust integration and controlled deployment.