Skill UI

Automates configuring and aggregating infrastructure metrics across compute, storage, network, containers, load balancers, and databases with Prometheus, Datadog, or CloudWatch for dashboards and capacity tracking.

This comprehensive guide covers the entire MLOps lifecycle, specializing in building robust, scalable, and production-grade Machine Learning systems. Expertise includes orchestrating complex ML pipelines using Kubeflow, Airflow, Prefect, and cloud-native services (AWS SageMaker, Azure ML, GCP Vertex AI). It covers experiment tracking (MLflow, W&B), model versioning, model registries, infrastructure as code (Terraform), and container deployment using Kubernetes. Ideal for engineers tasked with moving ML models from research to reliable, managed production environments.

Upstash QStash expert handles serverless message queues, scheduled HTTP jobs, retries, deduplication, and callback tracking so you can deliver asynchronous tasks without managing infrastructure.

A comprehensive development tool for building and managing enterprise-grade transactional email systems. It supports modern React Email and MJML templates, generating production-ready code for major providers like Resend, Postmark, and SendGrid. Key features include multi-provider integration, internationalization (i18n), dark mode, spam score optimization, and advanced analytics tracking. Ideal for setting up new products or migrating legacy email infrastructure.

This system builds an automated framework to track adversary Command and Control (C2) infrastructure. By leveraging passive DNS records, Certificate Transparency logs, and WHOIS data, it maps complex relationships between domains, IPs, and registrars. Use it to pivot from known indicators to discover entirely new, related threat actor infrastructure or maintain a continuous map of a threat group's network activity.

This skill provides advanced techniques and code examples for monitoring, mapping, and pivoting across adversary-controlled infrastructure. It leverages Passive DNS, Certificate Transparency logs, Shodan/Censys scanning, and network fingerprinting (JARM/JA3S) to discover C2 servers, phishing domains, and exploitable assets over time, which is crucial for advanced security operations and threat intelligence gathering.

LaminDB is an open-source, lineage-native lakehouse designed for managing complex biological datasets and computational workflows. It makes diverse scientific data (like scRNA-seq, AnnData) queryable, traceable, and compliant with FAIR principles. Core capabilities include robust data lineage tracking, schema validation, ontology-backed annotation (Bionty), and unified querying across multiple cloud/local storage systems. It is essential for bioinformatics research and building reproducible data infrastructure.

This workflow systematically analyzes deployed AWS resources and Infrastructure-as-Code (IaC) definitions. It collects usage metrics from CloudWatch and Cost Explorer to identify over-provisioning, unused resources, and inefficient configurations (e.g., right-sizing EC2, S3 lifecycle policies). It generates actionable cost optimization recommendations and creates structured GitHub issues for tracking and implementing savings.

This skill designs and audits the core engineering harness required for reliable AI agent collaboration within a codebase. It establishes critical infrastructure elements such as change tracking, linting rules, CI/CD gates, and standardized documentation, ensuring the repository remains the single source of truth for all intelligent agents. Ideal for setting up complex development workflows.