MongoDB driver and SDK best practices for Azure DocumentDB — singleton MongoClient, connection reuse, connection-pool fundamentals. Use when writing code that instantiates a MongoDB client, reviewing driver initialization, or diagnosing connection-related bugs. For full connection-pool tuning (serverless vs OLTP vs…
High availability, business-continuity, and disaster-recovery best practices for Azure DocumentDB — enabling in-region HA with availability zones (99.99% SLA), adding active-passive cross-region replica clusters (99.995% SLA), and understanding automatic backup retention. Use when designing production topology…
Index-type selection and shape guidance for Azure DocumentDB — when to use single-field, compound (ESR), multikey, wildcard, hashed, 2dsphere, TTL, and vector indexes; query-pattern → index-shape cookbook; per-collection index budget; DocumentDB-specific preference for textSearch over community $text. Use when…
Best practices for running Azure DocumentDB locally for development — choosing between the Gateway Docker image and the psql-only image, docker-compose setup, connection config (port 10260, TLS, SCRAM-SHA-256), env-driven configuration, sample-data management (SKIPINITDATA / INITDATAPATH), port bindings, and dev/prod…
Guide users through installing and configuring the DocumentDB MCP server for Azure DocumentDB. Use this skill when a user wants to wire the DocumentDB MCP server into an agentic client (Claude Code, Claude Desktop, Cursor, Copilot CLI, Gemini CLI, VS Code) and define a CONNECTIONPROFILES entry, or when they hit MCP…
Monitoring and diagnostics best practices for Azure DocumentDB — enabling diagnostic settings and slow-query logs, analyzing them in Log Analytics, and alerting on CPU / memory / IOPS / storage / connection saturation per cluster tier. Use when setting up observability on a new cluster, investigating production…
Generate read-only DocumentDB/MongoDB queries (find) or aggregation pipelines using natural language, with collection schema context and sample documents. Use this skill whenever the user asks to write, create, or generate queries for Azure DocumentDB, wants to filter/query/aggregate data, asks "how do I query..."…
Query and aggregation-pipeline optimization rules for Azure DocumentDB — using explain("executionStats") to verify index usage and avoid COLLSCAN. Use when reviewing a specific query, diagnosing a slow query, or validating that an index is actually being used. For full index-design workflow, see the…
Help with DocumentDB/MongoDB query optimization and indexing for Azure DocumentDB. Use only when the user asks for optimization or performance: "How do I optimize this query?", "How do I index this?", "Why is this query slow?", "Can you fix my slow queries?", etc. Do not invoke for general query writing unless user…
Security best practices for Azure DocumentDB — TLS enforcement, Private Endpoint / firewall configuration, Microsoft Entra ID + RBAC for authentication, and customer-managed keys (CMK) for encryption at rest. Use when reviewing production security posture, configuring networking, setting up authentication /…
Horizontal sharding (partitioning) for Azure DocumentDB collections — when to shard vs stay single-shard, how to pick a shard key for read-heavy vs write-heavy workloads, the logical/physical shard mental model, scaling out vs scaling up, hot-partition diagnosis, and the sh.shardCollection / sh.reshardCollection…
Storage configuration guidance for Azure DocumentDB — when and how to use Premium SSD v2 high-performance storage, IOPS/bandwidth caps that are gated by compute tier (not disk size), Premium SSD v2 limitations (no CMK, migration paths, disk-hydration sequencing), and storage capacity change limits. Use when picking a…
Vector search best practices for Azure DocumentDB using cosmosSearch — choosing between DiskANN / HNSW / IVF, creating indexes, tuning lBuild / lSearch / maxDegree, Product Quantization (up to 16,000 dims), half-precision (fp16) indexing, and normalizing embeddings for cosine similarity. Use when building RAG /…
Azure Kubernetes Service (AKS) agent skills and MCP integration: troubleshoot live clusters, optimize cost, assess AKS Automatic readiness, operate GPU and model-inference workloads, capture packet-level evidence, and design clusters. Deep Day-2 AKS operations that complement the broader Azure Skills plugin.
Run HolmesGPT evals (tests/llm/fixtures/testaskholmes) against the current agent itself (agent-native). The agent must answer the prompts itself (no external wrapper) using its own tools, run beforetest/aftertest around each case, compare the answer to expectedoutput using the scoring rules here, and generate JSON +…
Assess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic…
Make the AKS-specific design decisions for a new production Azure Kubernetes Service (AKS) cluster — SKU (Automatic vs Standard), pod IP model (Azure CNI Overlay vs kubenet), API-server access, egress, identity, upgrades, node pools, and reliability — then delegate the actual provisioning to the Azure Skills…
Match exact AKS operation-failure signatures to documented causes and fixes. WHEN: an AKS create, scale, upgrade, node-image, or image-pull failure names VMCannotFitEphemeralOSDisk, LinkedAuthorizationFailed, NodePoolMcVersionIncompatible, 'NodeImageVersion is not accepted', SkuNotAvailable, ZonalAllocationFailed…
Packet-level network evidence for AKS: run a bounded, distributed packet capture across nodes (filtered by IP, port, or tcpdump/BPF expression), and collect Azure network resources (NSG rules, route tables, firewall, VNET peering) when you need pcap-level proof of where traffic drops. Escalation tool for when logs and…