Skill UI

This guide provides a comprehensive, multi-language walkthrough for installing and initializing the Sentry SDK in various environments (Node.js, Python, Browser). It covers essential concepts like DSN setup, using secure environment variables, configuring release versions, and implementing basic error and performance tracking for robust observability in production and CI/CD pipelines.

A comprehensive guide to optimizing Sentry's performance monitoring pipeline. Learn how to minimize SDK overhead and event costs while maximizing signal quality. Techniques covered include replacing static sampling rates with dynamic `tracesSampler`, configuring profiling rates, preventing cardinality explosion through proper transaction naming, and implementing custom measurements for deeper business logic visibility. Essential for high-traffic, production applications.

This guide details how to use the Sentry CLI to manage the full application release lifecycle. Users can create versioned releases (using Semver or SHA), upload source maps to deminify stack traces, and associate specific git commits. This ensures that every error is accurately linked to the exact code version and deployment that caused it, significantly improving debugging and observability in CI/CD pipelines.

A comprehensive guide to configuring and utilizing ShellCheck for static analysis of shell scripts (Bash, sh, POSIX). This tool detects common programming pitfalls, enforces best practices, and helps ensure script reliability and portability. Ideal for setting up linting in CI/CD pipelines, pre-commit hooks, or maintaining code quality standards across a development team.

A comprehensive meta-skill for validating, testing, and scoring the quality of technical skills within an ecosystem. It performs deep structural checks (SKILL.md compliance), executes Python scripts for syntax, runtime, and output validation, and generates multi-dimensional quality scores using letter grades and tier recommendations. Ideal for new skill authorship, auditing existing components, or integrating rigorous QA gates into CI/CD pipelines.

Automates sklearn pipeline builder tasks within ML training, activating on relevant triggers to guide data prep, model training, hyperparameter tuning, and experiment tracking with production-ready code and standards validation.

Automates Snowflake data warehouse workflows via the Composio MCP, letting you list databases, schemas, and tables, run SQL (SELECT, DDL, DML), and integrate those operations into cross-app pipelines for faster data ops.

Guides building, tuning, and validating production Apache Spark jobs, covering DataFrame/RDD choices, partitioning, broadcast joins, skew handling, caching, and cluster configuration for big data pipelines.

Automates spark job creation within data pipelines by guiding etl, transformation, orchestration, and streaming workflows; includes industry best practices, code templates, and validation to deliver production-ready jobs.

This guide provides advanced patterns and best practices for optimizing Apache Spark jobs. It covers critical areas like effective partitioning, memory tuning, minimizing data skew, and optimizing different types of joins (broadcast, bucket). Use this when troubleshooting slow, large-scale data processing workflows or needing to scale Spark pipelines efficiently and reliably.

Automates Spark SQL optimizer tasks in data pipelines by guiding best practices, producing production-ready code and configurations, and validating ETL, transformation, orchestration, and streaming workflows.

This guide details setting up a robust CI integration pipeline for Speak language learning platform integrations. It utilizes GitHub Actions with mocked API testing and audio validation to ensure code reliability and functional correctness before deployment. Ideal for developers managing complex language learning products and APIs.