370건 · 관련도순
Use Phoenix to record execution traces, detailed records of an AI application's operations, and investigate problems. Evaluate answers on datasets, compare prompts and models, and set up monitoring through a self-hosted server.
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Investigate slow SQL, write complex joins and summaries, and design database structures. Uses execution plans, which show how queries run, to guide index choices and query rewrites, with before-and-after performance measurements.
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Audit database migration changes before a Bifrost release for lock contention, deadlocks, and delayed application startup. Receive pass, warning, or failure results, a release recommendation, and concrete remediation steps without modifying files.
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Assess web applications and APIs for SQL injection, where input changes database queries. The skill covers finding vulnerable inputs, testing different database systems, automating checks, and documenting findings with remediation suggestions.
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Guides deployment of Open Harness on Vercel, from collecting credentials and setting environment variables to checking the first session. Separates a basic setup from a fuller setup with GitHub repository access.
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Design ClickHouse tables, write analytical queries, and plan data loading using practical SQL and code patterns. Use it to review slow queries, build recurring summaries, and analyze user activity across large datasets.
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Administer Observal users, settings, submission reviews, identity configuration, and security investigations. Also manage local server upgrades, rollback, and database schema migrations, checking the resulting state and protecting sensitive values throughout privileged operations.
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Understand how Continuous Claude stores session memories, runs automatic actions at key events, and passes work between sessions. The skill explains the setup, relevant files, recall commands, and available database options.
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Audit Claude Code skills, agents, hooks, and a database-backed memory system. It reports configuration problems and broken references by severity, and offers an optional fix mode for permissions, hook builds, and missing cache directories.
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Use this skill to analyze an existing PostgreSQL database and identify which tables should be converted to Timescale/TimescaleDB hypertables. **Trigger when user asks to:** - Analyze database tables for hypertable conversion potential - Identify time-series or event tables in an existing schema - Evaluate if a table would benefit from Timescale/TimescaleDB - Audit PostgreSQL tables for migration to Timescale/TimescaleDB/TigerData - Score or rank tables for hypertable candidacy **Keywords:** hypertable candidate, table analysis, migration assessment, Timescale, TimescaleDB, time-series detection, insert-heavy tables, event logs, audit tables Provides SQL queries to analyze table statistics, index patterns, and query patterns. Includes scoring criteria (8+ points = good candidate) and pattern recognition for IoT, events, transactions, and sequential data.
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Use this skill to migrate identified PostgreSQL tables to Timescale/TimescaleDB hypertables with optimal configuration and validation. **Trigger when user asks to:** - Migrate or convert PostgreSQL tables to hypertables - Execute hypertable migration with minimal downtime - Plan blue-green migration for large tables - Validate hypertable migration success - Configure compression after migration **Prerequisites:** Tables already identified as candidates (use find-hypertable-candidates first if needed) **Keywords:** migrate to hypertable, convert table, Timescale, TimescaleDB, blue-green migration, in-place conversion, create_hypertable, migration validation, compression setup Step-by-step migration planning including: partition column selection, chunk interval calculation, PK/constraint handling, migration execution (in-place vs blue-green), and performance validation queries.
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Use this skill for setting up vector similarity search with pgvector for AI/ML embeddings, RAG applications, or semantic search. **Trigger when user asks to:** - Store or search vector embeddings in PostgreSQL - Set up semantic search, similarity search, or nearest neighbor search - Create HNSW or IVFFlat indexes for vectors - Implement RAG (Retrieval Augmented Generation) with PostgreSQL - Optimize pgvector performance, recall, or memory usage - Use binary quantization for large vector datasets **Keywords:** pgvector, embeddings, semantic search, vector similarity, HNSW, IVFFlat, halfvec, cosine distance, nearest neighbor, RAG, LLM, AI search Covers: halfvec storage, HNSW index configuration (m, ef_construction, ef_search), quantization strategies, filtered search, bulk loading, and performance tuning.
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Design a PostgreSQL-specific schema. Covers best-practices, data types, indexing, constraints, performance patterns, and advanced features
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SQL database migrations with zero-downtime strategies for PostgreSQL, MySQL, SQL Server
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[Deprecated] This is the required documentation for agents operating on the CloudBase Relational Database through MCP. It defines the canonical SQL management flow with `queryMysqlDatabase`, `manageMysqlDatabase`, `queryPermissions`, and `managePermissions`, including destroy flow, async status checks, safe query execution, schema initialization, and permission updates. MySQL provisioning is no longer available through MCP; new environments should use PostgreSQL — see postgresql-development skill instead.
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Use when building, debugging, or evaluating CloudBase PostgreSQL / CloudBase PG / PG mode apps, including Postgres schema setup, queryPgDatabase/managePgDatabase, JS SDK v3 app.rdb() CRUD/RPC, PG HTTP API fallback, RLS-style permissions, username-password auth, and Web CMS/admin CRUD flows backed by CloudBase PG.
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Use CloudBase document database Web SDK only for confirmed NoSQL collection work. Query, create, update, and delete document data; if the task mentions PostgreSQL / CloudBase PG / app.rdb(), route to postgresql-development instead.
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CloudBase PostgreSQL access-pattern and slow-query quality guidance. Use when designing how tables are read and written, eliminating per-row database calls, adding indexes for filters or joins, reviewing high-traffic data access, or explaining a slow SQL query. Not for first-time PG SDK setup, login UI, or NoSQL collections.
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[Deprecated] Use when building frontend Web apps that talk to CloudBase Relational Database via @cloudbase/js-sdk – provides the canonical init pattern so you can then use Supabase-style queries from the browser. New environments should use PostgreSQL with app.rdb() — see postgresql-development skill instead.
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Expert knowledge for Azure Database for MySQL development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when planning MySQL Flexible Server tiers, HA/DR, automated deployments, backups/replication, or secure app connectivity, and other Azure Database for MySQL related development tasks. Not for Azure SQL Database (use azure-sql-database), Azure SQL Managed Instance (use azure-sql-managed-instance), SQL Server on Azure Virtual Machines (use azure-sql-virtual-machines), Azure Database for PostgreSQL (use azure-database-postgresql).
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Enforce multi-tenant isolation and PostgreSQL Row Level Security in NestJS. Use when enforcing tenant isolation or PostgreSQL RLS in NestJS multi-tenant apps.
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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。
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Execute read-only SQL queries against multiple PostgreSQL databases. Use when: (1) querying PostgreSQL databases, (2) exploring database schemas/tables, (3) running SELECT queries for data analysis, (4) checking database contents. Supports multiple database connections with descriptions for intelligent auto-selection. Blocks all write operations (INSERT, UPDATE, DELETE, DROP, etc.) for safety.
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Consult PostgreSQL's parser and grammar (gram.y) to understand SQL syntax, DDL statement structure, and parsing rules when implementing pgschema features
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