ワンクリックでManusで任意のスキルを実行

documentdb-monitoring

スター5

フォーク7

更新日2026年5月4日 19:04

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 incidents, or tuning alert thresholds.

インストール

Codex または Claude でインストールこの Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。

Manusで実行

ソース

Azure

Azure/documentdb-agent-kit

GitHub リポジトリを開く Creator のリポジトリを見る

ダウンロード

Manusで実行

Monitoring & Diagnostics — Azure DocumentDB

Rules

monitoring-slow-query-log — Enable diagnostic settings and analyze slow-query logs in Log Analytics.
monitoring-metrics-alerts — Alert on CPU, memory, IOPS, storage, and connection saturation per cluster tier.

このリポジトリの他の Skills

同じリポジトリ

documentdb-mcp-setup

Azure/documentdb-agent-kit

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 `CONNECTION_PROFILES` entry, or when they hit MCP connection / auth / profile errors.

2026-05-275

documentdb-connection

Azure/documentdb-agent-kit

Optimize MongoDB client connection configuration (pools, timeouts, patterns) for Azure DocumentDB. Use this skill when working on functions that instantiate or configure a MongoDB client (e.g., calling `connect()`), configuring connection pools, troubleshooting connection errors (ECONNREFUSED, timeouts, pool exhaustion), optimizing connection-related performance issues. Includes scenarios like building serverless functions, creating API endpoints, optimizing high-traffic applications, or debugging connection failures.

2026-05-265

documentdb-full-text-search

Azure/documentdb-agent-kit

Full-text search best practices for Azure DocumentDB using the `createSearchIndexes` command and `$search` aggregation stage — BM25 keyword scoring, fuzzy search (`maxEdits`), phrase search with `slop`, custom analyzers (keyword + lowerCase + asciiFolding + edgeGram) for prefix / ID matching, `pathHierarchy` tokenizer for hierarchical IDs, multi-field search indexes, and hybrid (BM25 + vector) retrieval via Reciprocal Rank Fusion. Use when building search experiences, adding typo tolerance, matching phrases or prefixes on IDs / SKUs / part numbers, or combining lexical and semantic retrieval on the same collection.

2026-05-265

documentdb-vector-search

Azure/documentdb-agent-kit

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 / semantic-search applications, creating a vector index, tuning recall/latency, or reducing vector-index memory footprint.

2026-05-265

documentdb-indexing

Azure/documentdb-agent-kit

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 designing or reviewing indexes, choosing an index type for a query pattern, or deciding whether an additional index is worth the write cost.

2026-05-185

documentdb-natural-language-querying

Azure/documentdb-agent-kit

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...", needs help with query syntax, or discusses finding/filtering/grouping documents. Also use for translating SQL-like requests to MongoDB syntax. Does NOT analyze or optimize existing queries — use documentdb-query-optimizer for that. Requires DocumentDB MCP server.

2026-05-185

name	documentdb-monitoring
description	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 incidents, or tuning alert thresholds.
license	MIT

name	documentdb-monitoring
description	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 incidents, or tuning alert thresholds.
license	MIT