wshobson/agents

Patterns for combining vector similarity and keyword-based search.

Production-ready templates for incident response runbooks covering detection, triage, mitigation, resolution, and communication.

Comprehensive guide to Istio traffic management for production service mesh deployments.

Step-by-step guidance for creating production-ready Kubernetes manifests including Deployments, Services, ConfigMaps, Secrets, and PersistentVolumeClaims.

Production patterns for Linkerd service mesh - the lightweight, security-first service mesh for Kubernetes.

Comprehensive techniques for acquiring, analyzing, and extracting artifacts from memory dumps for incident response and malware analysis.

Complete end-to-end MLOps pipeline orchestration from data preparation through model deployment.

Comprehensive guide to implementing mutual TLS for zero-trust service mesh communication.

Decision framework and patterns for architecting applications across AWS, Azure, and GCP.

Master ERC-721 and ERC-1155 NFT standards, metadata best practices, and advanced NFT features.

Production patterns for Nx monorepo management.

Effective patterns for on-call shift transitions, ensuring continuity, context transfer, and reliable incident response across shifts.

Framework for debugging complex issues using the Analysis of Competing Hypotheses (ACH) methodology with parallel agent investigation.

Master PayPal payment integration including Express Checkout, IPN handling, recurring billing, and refund workflows.

Master PCI DSS (Payment Card Industry Data Security Standard) compliance for secure payment processing and handling of cardholder data.

Comprehensive guide to writing effective, blameless postmortems that drive organizational learning and prevent incident recurrence.

Comprehensive guide to building projections and read models for event-sourced systems.

Complete guide to Prometheus setup, metric collection, scrape configuration, and recording rules.

A reference checklist of common mistakes and anti-patterns in Python code. Review this before finalizing implementations to catch issues early.

Decouple long-running or unreliable work from request/response cycles. Return immediately to the user while background workers handle the heavy lifting asynchronously.

Externalize configuration from code using environment variables and typed settings. Well-managed configuration enables the same code to run in any environment without modification.

Build robust Python applications with proper input validation, meaningful exceptions, and graceful failure handling. Good error handling makes debugging easier and systems more reliable.

Instrument Python applications with structured logs, metrics, and traces. When something breaks in production, you need to answer "what, where, and why" without deploying new code.

Build fault-tolerant Python applications that gracefully handle transient failures, network issues, and service outages. Resilience patterns keep systems running when dependencies are unreliable.