144 件 ・ 関連度順
概要と使いどころ
Auto-activate for pytest_databases, Docker DB fixtures, PostgreSQL/pgvector/ParadeDB, MySQL/MariaDB, Oracle/SQL Server, CockroachDB, MongoDB, Redis/Valkey, BigQuery/Spanner, MinIO, or RustFS. Not for mocked DBs.
日本語の概要は準備中です。原文の説明を表示しています。
Use when running analytical SQL over Parquet/CSV/JSON without a warehouse, replacing pandas for data wrangling, joining S3 data in-place, building local data marts, or embedding OLAP into an app. Triggers: read_parquet/read_csv setup, partitioned dataset queries, hive partitioning, glob patterns for S3, COPY TO export, attach Postgres/MySQL, UDFs in Python/R, MotherDuck cloud sync, columnar performance vs row stores. NOT for OLTP workloads (concurrent writes), distributed analytics at petabyte scale (use Spark/Trino), or vector search (use pgvector/Lance).
日本語の概要は準備中です。原文の説明を表示しています。
Scalingo is a European (French) Platform-as-a-Service for deploying and operating web applications, background workers, and managed databases. Use this skill whenever the user mentions Scalingo, wants to deploy an app to Scalingo, work with the scalingo CLI, provision addons (PostgreSQL, MySQL, MongoDB, Redis, OpenSearch, InfluxDB), configure review apps, manage the scalingo.json manifest or a Procfile, scale containers, set up log drains, migrate from Heroku, use the Scalingo Terraform provider, or target sovereign regions (osc-fr1, osc-secnum-fr1 SecNumCloud). Also trigger for "deploy a French PaaS", "HDS-compliant hosting", "SecNumCloud deployment", or questions about Scalingo's Heroku-compatible buildpack model.
日本語の概要は準備中です。原文の説明を表示しています。
Design relational database schemas (and choose when to go NoSQL) that stay maintainable and fast. Use this skill whenever the user mentions tables, DDL, entities and relationships, normalization (1NF/2NF/3NF), primary and foreign keys, UUID vs bigint IDs, indexes (B-tree, composite, covering, partial), EXPLAIN, constraints (CHECK, UNIQUE, exclusion), transactions and isolation levels, migrations (Alembic, Prisma, Flyway, expand-contract, backfilling), or SQL vs NoSQL (MongoDB, DynamoDB, Cassandra, graph databases). Also trigger for "design the database", "model this domain", "which database should I use", or writing ORM models and migration files for PostgreSQL, MySQL, SQLite, or SQL Server.
日本語の概要は準備中です。原文の説明を表示しています。
Use when writing raw SQL — MariaDB/MySQL syntax, parameterization, raw migrations, seeders with `DB::statement`; fires even on a pasted query asking 'why is this slow'.
日本語の概要は準備中です。原文の説明を表示しています。
Use when working with database architecture, MariaDB/MySQL tuning, indexing strategies, slow queries, or multi-connection patterns — even when the user just says 'this query is slow'.
日本語の概要は準備中です。原文の説明を表示しています。
Use Exasol JDBC-based virtual schemas for federated read-only access to external databases such as PostgreSQL, Oracle, MySQL, SQL Server, and DB2. Covers database-source adapter selection, `CREATE VIRTUAL SCHEMA`, `ALTER VIRTUAL SCHEMA`, `EXPLAIN VIRTUAL`, metadata refresh, and query-side troubleshooting.
日本語の概要は準備中です。原文の説明を表示しています。
Use when 需要按 Alibaba Java Coding Guidelines（阿里巴巴 Java 编码规范）审查、生成或重构 Java、Spring、MyBatis、Maven、MySQL、SQL、日志、异常、安全或数据库相关代码。
日本語の概要は準備中です。原文の説明を表示しています。
Design schemas and write queries for PlanetScale serverless MySQL using branch-based workflows. Ensures index coverage, avoids foreign key anti-patterns, and treats every schema change as a reviewable, reversible deploy request.
日本語の概要は準備中です。原文の説明を表示しています。
PHP 8.0+ development — XAMPP, RESTful APIs, PDO/MySQL/MariaDB, and authentication. Use when building PHP backends, creating API endpoints, configuring XAMPP, or integrating PHP with databases.
日本語の概要は準備中です。原文の説明を表示しています。
Database optimization v3 — query plans, partitioning, indexing, connection pooling, PostgreSQL, MySQL
日本語の概要は準備中です。原文の説明を表示しています。
Expert en optimisation de bases de données (query plans, indexing strategies, partitioning, vacuum)
日本語の概要は準備中です。原文の説明を表示しています。
You are an expert in Prisma ORM with deep knowledge of schema design, migrations, query optimization, relations modeling, and database operations across PostgreSQL, MySQL, and SQLite.
日本語の概要は準備中です。原文の説明を表示しています。
SQL injection — error-based, blind boolean, time-based ve UNION tekniklerini kapsar; CTF'te login bypass, veri çekme ve flag okumak için tam otomatik exploit şablonları içerir
日本語の概要は準備中です。原文の説明を表示しています。
Use when the user asks about revenue, payments, subscriptions, billing, CRM deals, support tickets, production database tables, or other data that PostHog does not collect natively. Also use when a query fails because a table does not exist or returns no results for expected external data. The data warehouse can import from SaaS tools (Stripe, Hubspot, etc.), production databases (Postgres, MySQL, BigQuery, Snowflake), and other arbitrary data sources. Covers checking existing sources, identifying the right source type, and guiding the setup.
日本語の概要は準備中です。原文の説明を表示しています。
Guide the user through connecting a new data warehouse source — Postgres, MySQL, Stripe, Hubspot, MongoDB, Salesforce, BigQuery, Snowflake, and so on. Use when the user wants to "connect Stripe", "import data from Postgres", "add a new data source", "sync my warehouse tables", or wants to pick sync methods for each table. Walks through source-type discovery, credential validation, table discovery, per-table sync_type selection, and the final create call. Also covers picking a good prefix and what to do right after creation.
日本語の概要は準備中です。原文の説明を表示しています。
Expert database administrator specializing in high-availability systems, performance optimization, and disaster recovery. Masters PostgreSQL, MySQL, MongoDB, and Redis with focus on reliability, scalability, and operational excellence.
日本語の概要は準備中です。原文の説明を表示しています。
Provides patterns to configure AWS RDS (Aurora, MySQL, PostgreSQL) with Spring Boot applications. Configures HikariCP connection pools, implements read/write splitting, sets up IAM database authentication, enables SSL connections, and integrates with AWS Secrets Manager. Use when setting up RDS connections in Spring Boot, configuring connection pooling, or managing database credentials securely.
日本語の概要は準備中です。原文の説明を表示しています。
Database migration best practices for schema changes, data migrations, rollbacks, and zero-downtime deployments across PostgreSQL, MySQL, and common ORMs (Prisma, Drizzle, Django, TypeORM, golang-migrate).
日本語の概要は準備中です。原文の説明を表示しています。
SQL database migrations with zero-downtime strategies for PostgreSQL, MySQL, SQL Server
日本語の概要は準備中です。原文の説明を表示しています。
Identifies and exploits SQL injection vulnerabilities in web applications during authorized penetration tests using manual techniques and automated tools like sqlmap. The tester detects injection points through error-based, union-based, blind boolean, and time-based blind techniques across all major database engines (MySQL, PostgreSQL, MSSQL, Oracle) to demonstrate data extraction, authentication bypass, and potential remote code execution. Activates for requests involving SQL injection testing, SQLi exploitation, database security assessment, or injection vulnerability verification.
日本語の概要は準備中です。原文の説明を表示しています。
SQL injection testing skill for offensive security assessments and bug bounty hunting. Covers error-based, UNION-based, boolean/time-based blind, out-of-band, second-order, NoSQL, GraphQL, WebSocket, and JSON-operator SQLi. Includes WAF bypass techniques, database-specific exploitation (MySQL, MSSQL, PostgreSQL, Oracle), cloud-native attack paths, ORM CVE tracking, and SQLmap automation. Use when performing web application SQL injection testing, database enumeration, privilege escalation via SQLi, or assessing injection vectors in APIs and modern stacks.
日本語の概要は準備中です。原文の説明を表示しています。
Use when running analytical SQL over Parquet/CSV/JSON without a warehouse, replacing pandas for data wrangling, joining S3 data in-place, building local data marts, or embedding OLAP into an app. Triggers: read_parquet/read_csv setup, partitioned dataset queries, hive partitioning, glob patterns for S3, COPY TO export, attach Postgres/MySQL, UDFs in Python/R, MotherDuck cloud sync, columnar performance vs row stores. NOT for OLTP workloads (concurrent writes), distributed analytics at petabyte scale (use Spark/Trino), or vector search (use pgvector/Lance).
日本語の概要は準備中です。原文の説明を表示しています。
Designs and audits data retention and file compaction for an embedded SQLite datastore in a long-lived service — a unified retention registry (one declared policy per table), TTL / absolute-age / row-count-cap policies, a fail-loud coverage guard, WAL/checkpoint tuning, and auto_vacuum/incremental-vacuum so pruning actually shrinks the file. Use when a SQLite DB grows unbounded, a table has an index but no DELETE, a pruned DB never shrinks on disk, metrics are stored as raw events forever, or a new table needs bounding. Keywords retention policy, TTL, reaper, prune, VACUUM, auto_vacuum, incremental_vacuum, WAL checkpoint, freelist, DB bloat, row cap, per-tenant quota. NOT for Postgres/MySQL/server-DB partitioning or TTL, log-line rate-limiting/sampling, backup/replication, schema migrations, or query-performance indexing.
日本語の概要は準備中です。原文の説明を表示しています。