Browse skills

Expert knowledge for Azure Cosmos DB development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when using NoSQL/Mongo/Cassandra APIs, change feed, multi-region HA, vector search, or Cosmos DB for PostgreSQL, and other Azure Cosmos DB related development tasks. Not for Azure Table Storage (use azure-table-storage), Azure SQL Database (use azure-sql-database), Azure SQL Managed Instance (use azure-sql-managed-instance), Azure Data Explorer (use azure-data-explorer).

The English summary is being prepared. Showing the original description.

SQL and NoSQL query optimization techniques, indexing strategies, execution plan analysis, JOIN algorithms, cardinality estimation, and database-specific query patterns

The English summary is being prepared. Showing the original description.

TLA+ and PlusCal for specifying distributed system invariants. Decision heuristics for when formal verification adds value, key patterns, state explosion management, and alternatives comparison.

The English summary is being prepared. Showing the original description.

Database comparison catalogs, RDBMS vs NoSQL selection criteria, CAP/ACID/BASE theory, OLTP vs OLAP, and technology-specific characteristics

The English summary is being prepared. Showing the original description.

Acceptance test creation methodology for the DISTILL wave. Domain knowledge for the acceptance designer agent: port-to-port principle, prior wave reading, wave-decision reconciliation, graceful degradation, and document back-propagation.

The English summary is being prepared. Showing the original description.

Domain-specific authoritative source databases, search strategies by topic category, and source freshness rules

The English summary is being prepared. Showing the original description.

编写Python脚本，实现每天定时从MongoDB获取数据，进行Gzip压缩，并通过HTTP POST请求同步到指定URL，同时处理BytesIO二进制传输错误。

The English summary is being prepared. Showing the original description.

Apply MongoDB data-modeling, indexing, and query rules from access patterns. Use when designing schemas, choosing embed vs reference, or tuning MongoDB query behavior.

The English summary is being prepared. Showing the original description.

30+ service-specific secret detection regex patterns, entropy-based detection, PEM/JWT/Base64 identification, and false positive filtering.

The English summary is being prepared. Showing the original description.

Static Application Security Testing patterns, OWASP Top 10 checklist, language-specific vulnerability patterns, Semgrep rule writing guide, and CI/CD integration. Use when scanning code for security vulnerabilities or writing custom SAST rules.

The English summary is being prepared. Showing the original description.

Document modeling, aggregation pipeline, indexing strategy, change streams, and multi-document transactions.

The English summary is being prepared. Showing the original description.

Design and implement disaster recovery strategies with RTO/RPO planning, database backups, Kubernetes DR, cross-region replication, and chaos engineering testing. Use when implementing backup systems, configuring point-in-time recovery, setting up multi-region failover, or validating DR procedures.

The English summary is being prepared. Showing the original description.

Document database implementation for flexible schema applications. Use when building content management, user profiles, catalogs, or event logging. Covers MongoDB (primary), DynamoDB, Firestore, schema design patterns, indexing strategies, and aggregation pipelines.

The English summary is being prepared. Showing the original description.

Implement applications using Google Cloud Platform (GCP) services. Use when building on GCP infrastructure, selecting compute/storage/database services, designing data analytics pipelines, implementing ML workflows, or architecting cloud-native applications with BigQuery, Cloud Run, GKE, Vertex AI, and other GCP services.

The English summary is being prepared. Showing the original description.

When designing distributed systems for scalability, reliability, and consistency. Covers CAP/PACELC theorems, consistency models (strong, eventual, causal), replication patterns (leader-follower, multi-leader, leaderless), partitioning strategies (hash, range, geographic), transaction patterns (saga, event sourcing, CQRS), resilience patterns (circuit breaker, bulkhead), service discovery, and caching strategies for building fault-tolerant distributed architectures.

The English summary is being prepared. Showing the original description.

Strategic guidance for designing modern data platforms, covering storage paradigms (data lake, warehouse, lakehouse), modeling approaches (dimensional, normalized, data vault, wide tables), data mesh principles, and medallion architecture patterns. Use when architecting data platforms, choosing between centralized vs decentralized patterns, selecting table formats (Iceberg, Delta Lake), or designing data governance frameworks.

The English summary is being prepared. Showing the original description.

用于火山引擎（Volcengine）数据库（MySQL、veDB-MySQL、PostgreSQL、SQL Server、MongoDB、Redis）和公网自建数据库（MySQL和PostgreSQL系列）的元数据管理、数据分析、开发变更、运维诊断、巡检。覆盖实例列表查询、实例下数据库列表查询、表列表查询、表结构查询、数据查询、数据分析与可视化报告（含跨数据源/文件联合分析）、数据治理（资产盘点/数据画像/数据质量/敏感数据识别）、慢查询诊断、死锁与锁等待分析、事务与活跃会话排查、错误日志查询、表空间分析、健康巡检、监控指标查询、变更工单申请等场景。不支持字节云（ByteCloud）数据库，如 ByteRDS / ByteDoc / ByteRedis。

The English summary is being prepared. Showing the original description.

使用火山引擎 MongoDB Skill，帮助用户完成 MongoDB 相关的实例管理、备份恢复、参数等运维任务，可直接调用 uv run ./scripts/call_mongodb.py 脚本获取实时结果。当需要访问管理在火山引擎 MongoDB 实例详细信息时，此 Skill 可以提供方便的接口。

The English summary is being prepared. Showing the original description.

Grafana 监控大盘设计评审技能。基于"如何定义好的监控大盘"最佳实践，对一个 Grafana 大盘的设计或现状进行评审，输出问题清单、改进建议与有效性评分（满分 10 分）。触发词："/grafana-dashboard-review" 或用户要求"评审/审视/打分一个 Grafana 监控大盘"。

The English summary is being prepared. Showing the original description.

Use when you need framework-agnostic MongoDB and non-relational database query guidance — document schema design, collection modeling, JSON Schema validation, indexes, aggregation pipelines, query performance, consistency trade-offs, transactions, and operational safety — without choosing Spring Boot, Quarkus, or Micronaut. This should trigger for requests such as Design MongoDB document schemas; Review MongoDB queries and indexes; Improve aggregation pipeline performance; Model non-relational data access patterns; Review NoSQL consistency and transaction trade-offs. Part of Plinth Toolkit

The English summary is being prepared. Showing the original description.

Use when you need to add or review Mongock MongoDB data migrations in a Micronaut application — including Mongock runner/driver selection, Micronaut bean wiring, migration scan packages, @ChangeUnit classes, lock/transaction settings, and Testcontainers verification. This should trigger for requests such as Add Mongock migrations in Micronaut; Review Micronaut MongoDB data migrations; Configure Mongock change units with Micronaut Data MongoDB; Create Mongock change units for Micronaut MongoDB; Review Mongock migration ordering in a Micronaut service. Part of Plinth Toolkit

The English summary is being prepared. Showing the original description.

Use when you need MongoDB persistence in Quarkus — including Panache Mongo entities/repositories, document design, indexes, transactions where applicable, and error handling. This should trigger for requests such as Add MongoDB in Quarkus; Review Quarkus Mongo Panache design; Improve Mongo error handling in Quarkus services; Model MongoDB documents for a Quarkus service; Configure Quarkus MongoDB clients codecs or transactions. Part of Plinth Toolkit

The English summary is being prepared. Showing the original description.

Use when you need MongoDB persistence in Micronaut — including @MongoRepository design, document modeling, indexes, query patterns, and error handling. This should trigger for requests such as Add MongoDB in Micronaut; Review Micronaut Data Mongo design; Improve error handling for Micronaut Mongo operations; Model MongoDB documents for a Micronaut service; Configure Micronaut MongoDB codecs indexes or transactions. Part of Plinth Toolkit

The English summary is being prepared. Showing the original description.

Use when you need to add or review Mongock MongoDB data migrations in a Quarkus application — including the Quarkiverse Mongock extension, Quarkus MongoDB client configuration, migrate-at-start, @ChangeUnit classes, lock/transaction settings, and Quarkus test verification. This should trigger for requests such as Add Mongock migrations in Quarkus; Configure quarkus-mongock; Review Quarkus MongoDB data migrations; Create Mongock change units for Quarkus MongoDB; Review Mongock migration ordering in a Quarkus service. Part of Plinth Toolkit

The English summary is being prepared. Showing the original description.