AI & ML Skills

Guide AI agents through a systematic, user-choice-driven installation process for dbt-core on local machines. The agent runs diagnostic scripts, presents options to users, and executes non-interactive installation scripts based on user preferences.

Use when creating or updating AGENTS.md files, .github/copilot-instructions.md, or other AI agent rule files, onboarding AI agents to a project, standardizing agent documentation, or when anyone mentions AGENTS.md, agent rules, project onboarding, or codebase documentation for AI agents.

This skill enables AI agents to help users monitor dbt execution using the brooklyn-data/dbt_artifacts package. The package captures detailed execution metadata during dbt runs and stores it in queryable tables for analysis and monitoring.

Expert guidance for creating Claude Code slash commands. Use when working with slash commands, creating custom commands, understanding command structure, or learning YAML configuration.

Expert guidance for creating, building, and using Claude Code subagents and the Task tool. Use when working with subagents, setting up agent configurations, understanding how agents work, or using the Task tool to launch specialized agents.

This file coordinates multi-agent swarm mode for ProjectStats. When swarm mode is active, multiple Claude Code agents can work in parallel on different parts of the codebase.

Transform AI agents into experts on dbt materializations, providing guidance on choosing the right materialization strategy based on model purpose, size, update frequency, and query patterns, plus implementation details for each type including advanced features like snapshots and Python models.

Transform AI agents into experts on dbt and Snowflake performance optimization, providing guidance on choosing optimal materializations, leveraging Snowflake-specific features, and implementing query optimization patterns for production-grade performance.

Transform AI agents into experts on dbt testing strategies, providing guidance on implementing comprehensive data quality checks with database-enforced constraints, generic tests, and custom singular tests to ensure data integrity across all layers.

Transform AI agents into experts on writing production-quality dbt models, providing guidance on CTE patterns, SQL structure, and best practices for creating maintainable and performant data models across all medallion architecture layers.

Agent execution engine that composes prompts, routes models, and writes run artifacts. Use when launching subagent runs.

Transform AI agents into experts on dbt project architecture and medallion layer patterns, providing guidance on structuring production-grade dbt projects with proper layer separation, naming conventions, and configuration strategies.

Transform AI agents into experts on dbt command-line operations, model selection patterns, Jinja templating, and troubleshooting techniques for efficient dbt development and debugging.

Cost-effective task delegation strategy using Haiku model for straightforward work. Use when planning how to approach simple, pattern-following tasks to minimize costs.

Multi-model supervisor that discovers skills, picks models, and composes runs. Use when executing multi-step plans across multiple models.

Look up examples and patterns from the official Anthropic Claude Cookbooks repository. Use when you need reference implementations, code examples, or best practices for Claude integrations, tool use, agents, prompting techniques, or API usage.

Create new Claude Code skills with proper structure and best practices. Use when the user wants to create a new skill, extend Claude's capabilities, or needs help structuring a SKILL.md file.

This skill should be used when the user asks to generate study plans for 考研 (Chinese graduate entrance exam), parse course schedules, create daily/weekly study schedules, or optimize study time allocation.

An AI-powered task management system that integrates seamlessly with AI Agents to manage specification-driven development workflows.

Apply Chain-of-Verification (CoVe) prompting to improve response accuracy through self-verification. Use for complex questions requiring fact-checking, technical accuracy, or multi-step reasoning.

Synchronizes AI agent skills from local directories and Git repositories, generating .cursor/rules/skills.mdc for Cursor IDE integration.

Task-master is an AI-powered task management system for breaking down complex projects into manageable tasks and subtasks. It supports PRD parsing, complexity analysis, and specification-driven development workflows.

Advanced subagent troubleshooting. Use when subagent calls fail, return bad output, or need voting for critical decisions.

Take an interactive tour of agentic-loop - the system for going from idea to shipped code with AI.

Initialize ERNE — the AI agent harness for React Native & Expo projects. Sets up 13 specialized agents, hook-based code quality enforcement, MCP server integrations, and a visual dashboard.

Sync the yaah marketing website (website/index.html) with the current project state — features, CLI commands, skills, agents, hooks, and installation instructions.

Audit code changes on the current branch and update docs/ + .claude/rules/ to stay in sync. Run before /pr.

Amazon SageMaker for building, training, and deploying machine learning models. Use for SageMaker AI endpoints, model training, inference, MLOps, and AWS machine learning services.

NVIDIA NeMo framework for building and training conversational AI models. Use for NeMo Retriever models, RAG (Retrieval-Augmented Generation), embedding models, enterprise search, and multilingual retrieval systems.

Add a new LangChain tool to the Medox ReAct agent. Use when creating a new tool file, registering it in the graph, and writing its test.