Skill UI

Guide for implementing Jaeger/Tempo distributed tracing across microservices, covering Kubernetes/Docker deployments plus OpenTelemetry instrumentation for Python, Node.js, and Go to visualize request flows and diagnose latency.

This skill provides patterns for building highly resilient applications that interact with external APIs. It covers crucial concepts like Exponential Backoff with Jitter, request queuing for bulk operations, and the Circuit Breaker pattern. Use this when your application needs to handle rate limit errors (429), transient network failures, or temporary service outages, ensuring reliable throughput and graceful degradation.

Mastery of advanced system design patterns, including Event Sourcing, CQRS, and Event-Driven Architecture (EDA). This skill covers designing event stores, building read/write projections, orchestrating sagas, and managing eventual consistency. It is ideal for building complex microservices that require guaranteed audit trails, historical state querying (temporal queries), and robust handling of business workflows with compensating actions.

A comprehensive guide and expert resource for mastering durable workflow orchestration using the Temporal Python SDK. Covers advanced patterns including distributed transactions, saga implementation, signal handling, and query patterns. Essential for building highly reliable, scalable, long-running backend systems, and mastering async/await execution models for production deployment.

This skill provides comprehensive, structured guidance for testing Temporal workflows written in Python. It covers best practices for unit testing (using time-skipping), integration testing (mocking dependencies), and determinism validation through replay testing. Ideal for ensuring high code coverage and robust CI/CD pipelines for complex asynchronous workflows.

Provides durable workflow automation guidance for AI agents, covering platforms like n8n, Temporal, Inngest and patterns such as sequential, parallel, and orchestrator-worker flows to turn brittle scripts into production-grade orchestration.

Aeon is a scikit-learn compatible toolkit for time series machine learning, covering classification, regression, clustering, forecasting, anomaly detection, segmentation, similarity search, and feature extraction on temporal data.

Expert guide for building durable distributed systems with the Temporal Go SDK, covering deterministic workflow rules, mTLS worker configuration, advanced patterns, and testing/observability strategies for production workloads.

Provides instant, temporary Postgres databases for local development, prototyping, and testing environments. Supports connecting via REST API, CLI, SDK, and even Vite plugins. No account or credit card is required, making it ideal for setting up quick, throwaway database instances for development purposes.

Use PyMISP to query MISP events, IOC distributions, galaxy clusters, and tag trends so analysts can generate threat landscape reports with top actors, malware families, MITRE techniques, and temporal insights.

Detects NTFS timestomping (MITRE T1070.006) by comparing $STANDARD_INFORMATION and $FILE_NAME timestamps from analyzeMFT/MFTECmd output, enabling threat hunters to flag anomalous temporal edits in forensic timelines.

Implement Just-In-Time access provisioning to remove standing privileges by granting temporary, scoped permissions with approval workflows, PAM/IGA integration, automated revocation, monitoring, and zero-trust controls.