Skill UI

Automates CodeQL database builds, suite selection, and taint-aware scans to surface security and quality issues across supported languages, handling output directories, SARIF results, and extensions in one workflow.

Guides CodeRabbit production deployments and rollbacks with prerequisites, code quality, infrastructure, documentation, and gradual rollout steps plus monitoring checks.

Orchestrates an exhaustive, multi-dimensional code review process by coordinating specialized agents (e.g., security, performance, quality, architecture). This workflow covers code quality, architectural integrity, security vulnerabilities (OWASP Top 10), performance bottlenecks, and test coverage, providing consolidated, actionable feedback and remediation guidance.

Combines model routing, immutable budget tracking, retry policies, and prompt caching so LLM API calls stay on-budget without sacrificing quality, ideal for batch workloads with mixed complexity.

Analyzes code coverage during fuzzing campaigns to assess harness quality, spot blockers like magic values, and guide instrumentation or corpus updates.

Orchestrates advanced Cursor Composer workflows for complex multi-file edits, guiding pattern reuse, architecture migrations, and quality checks while documenting prerequisites and incremental application steps for consistent feature generation.

Guides teams in building effective custom prompts for Cursor AI, covering context-task structure, constraints, templates, and iteration practices to maintain reusable, high-quality responses across projects.

Identifies Customer.io anti-patterns across authentication, tracking, data quality, delivery, and performance so you can audit integrations, fix common mistakes, and optimize implementations with actionable checks.

Daily News Report orchestrates agents to scrape a curated URL list, filter high-quality technical findings, and compile them into daily Markdown summaries while monitoring batch progress and leveraging smart caching for reliability.

Guides data engineering patterns for ETL pipelines, Spark analytics, and quality checks, showcasing Python and SQL snippets to extract, transform, load, validate, and model sales data in modern warehouses.

An expert in designing, implementing, and optimizing scalable, reliable, and cost-effective data pipelines for both batch and streaming data. Covers advanced topics including ETL/ELT patterns, Kappa/Lambda architectures, workflow orchestration (Airflow/Prefect), transformation using dbt/Spark, and managing modern storage formats like Delta Lake and Iceberg. Focuses heavily on data quality, monitoring, and cost optimization strategies.

Automated Data Quality Checker offers guidance, best practices, and production-ready code for validating pipelines whenever you work with ETL, streaming, or data transformation scenarios.