Skill UI

Automates the creation of Airflow DAGs for ETL and streaming data workflows, offering best-practice guidance, production-ready code samples, and validation against common pipeline standards.

A comprehensive guide to building production-ready Apache Airflow Directed Acyclic Graphs (DAGs). This skill covers best practices for operators, sensors, testing, deployment, and designing robust data pipelines. Use this when orchestrating complex workflows, managing ETL processes, or ensuring reliable batch job execution in a production environment.

Guides you through building Dagster data pipelines with best-practice advice, ready-to-use configurations, and validation checks for ETL, transformation, orchestration, and streaming workflows whenever the Dagster pipeline creator skill is activated.

This skill acts as a specialized tutorial engineering specialist, transforming raw, complex technical concepts or codebases into structured, progressive, and engaging educational content. It applies pedagogical design principles, such as progressive disclosure, spaced repetition, and hands-on exercises, to create documentation, courses, and onboarding materials that teach users how to build, debug, and optimize, rather than just referencing information.

Documents how to build autonomous Claude Code loops, covering sequential pipelines, NanoClaw REPL, infinite multi-agent deployments, RFC-based DAG orchestration, and PR-driven CI cycles so teams can run persistent, quality-gated development workflows without babysitting.

Coordinates complex features via a humanplane-inspired RFC pipeline, breaking work into DAG-based agents with quality gates, merge queues, and unit orchestration to keep large jobs verifiable.

Shows the AgentHub session status, including agent branches, commit counts, DAG frontier state, and board updates, helping operators monitor progress or check a specific session via `/hub:status`.

Simulates Andrej Karpathy, a leading AI researcher and educator (ex-OpenAI, Tesla). Use this skill to learn deep learning concepts from scratch, gain profound insights into LLMs, understand Software 2.0, or discuss advanced topics like autonomous driving and neural network implementation. Provides technical, pedagogical, and highly detailed explanations.

This module adopts the persona, vocabulary, and critical pedagogical style of Yann LeCun. It provides deep insights into open-source technology, the concept of technological sovereignty, and the geopolitical incentives driving major AI companies (Meta, OpenAI, Google). It structures explanations using Socratic methods, grounding complex AI concepts in physical analogies.

Transforms existing code files into highly effective learning resources by adding context-aware educational comments. Acting as an expert educator, this tool tailors explanations—from foundational concepts for beginners to deep architectural insights for advanced practitioners—while strictly maintaining the original file's structure, encoding, and syntax. Ideal for generating self-documenting, pedagogically rich codebases.

A comprehensive, adaptive mathematics tutor covering K-12 topics from arithmetic through calculus. This skill utilizes proven pedagogical strategies—such as Socratic questioning, CPA approach, and error analysis—to ensure students build deep mathematical understanding rather than just procedural fluency. It adapts to multiple global curricula (SAT, A-Level, Gaokao) and tracks progress to target weak spots.

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.