Skill UI

This skill enables implementing data quality validation using Great Expectations, dbt tests, and data contracts. It is used for building reliable data pipelines, setting up validation rules, and establishing data contracts between teams. It includes automating validation in CI/CD and monitoring data quality metrics to ensure data reliability in production environments.

Expert data scientist for advanced analytics, machine learning, and statistical modeling. Guides the full workflow from exploratory data analysis to production deployment, covering experimentation, forecasting, NLP, and business intelligence for actionable insights.

This comprehensive workflow guides the entire database lifecycle, from initial architecture design (covering both SQL and NoSQL) to advanced implementation, optimization, and maintenance. It covers crucial stages like schema design, implementing migrations, optimizing query performance, developing robust data pipelines (using tools like Airflow and dbt), and managing data quality and operations. Ideal for engineers building complex, production-grade data systems.

This skill provides expert guidance on SQL database migrations for PostgreSQL, MySQL, and SQL Server. It focuses on zero-downtime deployment strategies, ensuring data integrity and providing comprehensive rollback plans. You will receive migration scripts, validation checks, and performance optimization recommendations suitable for production environments.

A guide to production-ready patterns for dbt (data build tool). It covers essential practices for robust data pipeline development, including model organization (staging, intermediate, marts), implementing advanced testing strategies, ensuring data quality, and optimizing incremental processing for large datasets.

This skill provides a structured methodology for tackling complex bugs, performance bottlenecks, and production incidents. It guides users through reproducing issues, forming hypotheses, performing controlled experiments, and narrowing down the scope using proven strategies and tools. Ideal for deep dive troubleshooting and root cause analysis in complex software systems.

This skill offers expert guidance for AI-assisted debugging workflows and observability analysis. It covers root cause analysis, hypothesis generation, and production-safe debugging strategies using tools like Sentry and DataDog. Use it for troubleshooting errors, analyzing logs, and implementing validated fixes in development or production environments.

Production-ready Solidity templates for building DeFi protocols, including staking, AMMs, governance, lending, and flash loans. Use for smart contract development and decentralized finance applications.

Generates Dockerfiles, CI/CD pipelines, environment configs, infrastructure as code, observability setup, and deployment verification checklists to turn tested code into production-ready releases.

This skill teaches the critical principles and decision-making processes for safe production releases. It covers platform selection (e.g., Kubernetes, Vercel), mandatory pre-deployment verification (Code Quality, Environment), structured workflows (Prepare, Backup, Deploy, Verify), and advanced strategies like Blue-Green and Canary deployments. Focuses on thinking through risk mitigation, not just executing scripts.

Establishes and updates the foundational context for your target developer audience. Analyzes product materials, personas, pain points, and alternatives to create a comprehensive .agents/developer-audience-context.md file. Essential for starting other developer marketing skills to ensure consistent messaging and strategy.

Understand, reduce, and recover from developer churn. Identify at-risk users, analyze reasons for leaving, and run win-back campaigns with honest value, not discounts. Ideal for developer relations, product management, and marketing teams.