🗄️ SQL & Databases AI Skills

This skill should be used when the user wants SQL queries written from business requirements, or has an existing SQL query that needs performance tuning. Trigger phrases include '写个 SQL', '帮我优化这条 SQL', '这个查询很慢', 'SQL 优化', 'WHERE 条件怎么写', 'SQL for this request'. It produces dialect-appropriate SQL plus concrete index and rewrite recommendations.

['基于高人气开源Skill深度优化升级\n- 移除风险代码,增强安全性和稳定性\n\n适用场景:\n- 第三方API集成、平台对接、数据同步\n- 独立开发者与一人公司效率提升\n\', '自动化工作流与智能决策辅助\n\n差异化:经过深度优化,去除原始风险代码,清理外部依赖引用,增强元数据和触发关键词,完全适配SkillHub平台规范']

Connect to Supabase for database operations, vector search, and storage. Use for storing data, running SQL queries, similarity search with pgvector, and mana...

蒲公英数据开发工程师Skill套件 - 专为数据开发工程师设计的完整AI Skill生态系统。 包含7个核心模块：需求分析、架构设计、数据建模、SQL开发、ETL Pipeline、数据质量、数据测试。 当用户需要端到端数据开发解决方案、数据仓库建设、ETL开发、SQL优化、数据质量管理时触发。 触发词：数据开发...

快速轻量本地SQLite数据库，支持中文交互，低RAM低存储，适用于SkillHub Agent的数据管理和SQL操作。

Explains what Airtable is and how data is structured — bases, tables, fields, records, views, automations, and interfaces. Use when you need context about th...

Execute SQL queries, maintain data, perform statistics, and backup SQLite/MySQL databases including local and remote connections.

DBA多AI协作系统。用于处理数据库相关的所有DBA工作，包括架构设计、日常运维、故障排查、性能优化、数据迁移等。支持Oracle、MySQL、PostgreSQL、SQL Server及国产数据库（达梦、崖山、金仓、OceanBase、PolarDB、TiDB、TDSQL），以及MongoDB、Redis、Cl...

数据库助�?- SQL查询构建�?Schema管理/数据导入导出，支持SQLite/MySQL/PostgreSQL，含ORM模板

Classify a database migration and surface production risks — locks, compatibility, backfills, rollback — and prefer expand-contract sequencing.

LLM 驱动的数仓元数据自动治理。解决企业数仓最普遍的痛点：字段无注释、表无描述、命名混乱、NL2SQL 准确率低。 自动探测连接模式：pyhive 可用时读写 Hive；否则通过 MCP 工具只读，结果持久化到 SQLite。 触发时机（以下任一即触发）： - 用户提到：数据治理、元数据、字段注释、表备注、字段...

Baserow API integration with managed API key authentication. Manage database rows, fields, and tables. Use this skill when users want to read, create, update, or delete Baserow database rows, or query data with filters. For other third party apps, use the api-gateway skill (https://clawhub.ai/byungkyu/api-gateway). Calls run through the `maton` CLI with OAuth login, or over raw HTTP with a Maton API key where the CLI cannot be installed. Every call is authenticated as the user's connection and reaches only what that connection's authorization allows, which the provider enforces on every request; the endpoints documented here are the ones this skill uses, and any other endpoint of this app needs the user to ask for it by name. Default to read and list calls, and confirm every write or new connection with the user. This file also documents the three constructs that turn a Baserow connection into automation, in the order they are used: the connection (the first step), a hosted function that runs a Baserow action through the Maton SDK, and a trigger that calls that function on a schedule or on an event. Those sections are the platform's own reference text, shared with the api-gateway skill, with Baserow examples; they add no Baserow capability - Baserow is not an event source, a trigger cannot read Baserow data, and the files under `references/<source>/triggers.md` are the platform's event catalogues for the sources Maton offers (time, Calendly, GitHub, Gmail, HubSpot, Linear, Notion, Slack, Stripe).

Connect to and explore databases (PostgreSQL, MySQL, SQLite, MongoDB, Redis). Run queries, inspect schemas, export data. Use when user wants to query a datab...

Write database queries using tkms AsyncSqlSessionTemplate. Use when implementing dao layer, writing SQL, or when the user asks for database operations with i...

Work with Baserow tables/rows over the REST API for reads, inserts, and updates. Use when user asks to view or modify Baserow CRM/pipeline data.

Use this skill whenever the user needs to operate or troubleshoot a MySQL 8.x or MariaDB 10.6+ server as a DBA — a one-shot server health overview (version + flavor, connection headroom, replica role); server reads (global variables, status counters, databases, storage engines); activity (sessions/processlist, long-running queries, open InnoDB transactions, lock waits); query stats (performance_schema statement-digest top-N, EXPLAIN FORMAT=JSON); index health (unused indexes, redundant/duplicate indexes, cardinality); table health (sizes, data_free fragmentation, engine/row-format status); replication (replica IO/SQL thread state and lag, binlog/GTID status); four flagship analyses — slow-query RCA (worst digest + EXPLAIN → cited cause/action incl. full-scan and lock-time-dominant classification), InnoDB lock-wait & deadlock chain RCA (wait-for tree, root blocker, last deadlock parsed from SHOW ENGINE INNODB STATUS), replication lag RCA (thread state/error fields → cause+action), and table fragmentation analysis (data_free → OPTIMIZE candidates); and guarded writes (kill a session or query, OPTIMIZE/ANALYZE TABLE, create/drop an index, SET GLOBAL a variable, reset digest stats). Always use this skill for "mysql health check", "why is this query slow", "top queries by time", "EXPLAIN this", "table fragmentation", "which indexes are unused", "redundant index", "who is blocking whom", "deadlock", "kill the session holding the lock", "replication lag", "replica stopped", "seconds behind master/source", "OPTIMIZE this table", "create/drop an index", or "SET GLOBAL max_connections" when the context is a MySQL or MariaDB database. Do NOT use for PostgreSQL — use postgres-aiops. Do NOT use when the target is OT / industrial equipment (use industrial-aiops), a hypervisor, a storage appliance, a backup product, or a container/cluster orchestrator (negative routing hints only). Common MySQL/MariaDB DBA operations with a built-in governance harness (audit, policy, token budget, undo, risk-tiers). Behaviour is validated by a mock-based test suite; see docs/VERIFICATION.md for the live-verification checklist.

Use this skill whenever the user needs to operate or troubleshoot a PostgreSQL server/cluster as a DBA — a one-shot cluster health overview; server reads (version/uptime, settings, extensions, databases, roles); activity (sessions, idle-in-transaction, long-running queries, locks); query stats (pg_stat_statements top-N, EXPLAIN a statement); index health (unused indexes, missing-index hints, bloat, invalid/duplicate); table health (sizes, dead-tuple bloat, autovacuum status); replication (standby lag, replication slots, WAL); three flagship analyses — slow-query RCA (worst pg_stat_statements entry + EXPLAIN → cause/action), bloat & vacuum analysis (dead tuples + autovacuum lag → recommendation), and blocking lock-chain RCA (build the wait-for tree, name the root blocker); and guarded writes (terminate a backend, cancel a query, VACUUM/ANALYZE, create/drop an index, REINDEX, ALTER SYSTEM SET a parameter, reset query stats). Always use this skill for "postgres health check", "why is this query slow", "pg_stat_statements top queries", "EXPLAIN this", "table/index bloat", "which indexes are unused", "missing index", "autovacuum status", "who is blocking whom", "kill the backend holding the lock", "replication lag", "replication slots", "VACUUM this table", "create/drop an index", or "ALTER SYSTEM SET work_mem" when the context is a PostgreSQL database. Do NOT use when the target is OT / industrial equipment (Modbus, OPC-UA, PLCs — use industrial-aiops), a hypervisor, a storage appliance, a backup product, a container/cluster orchestrator, or a non-PostgreSQL database (negative routing hints only). Covers common PostgreSQL DBA operations with a built-in governance harness (audit, token budget, undo, risk-tiers). Beyond the mock suite, the reads plus a governed write and its undo have been exercised against a live PostgreSQL 16.14 instance (see docs/VERIFICATION.md).

Generate semantic YAML files from databases or Excel; use when users need to quickly build semantic models, generate topic configs or export table structure...

Data engineering skill for building scalable data pipelines, ETL/ELT systems, and data infrastructure. Expertise in Python, SQL, Spark, Airflow, dbt, Kafka,...

专门处理日常办公场景下的高频、复杂数据分析与处理的助手。使用本地代码执行模式（SQL 或 Python + SQLite）来处理数据导入、清洗、查询、提取、合并拆分及报告生成，支持大数据量且保障数据隐私安全。当用户需要处理 Excel/CSV 文件、跨表查询、生成图表或输出数据分析报告时使用此 Skill。

Google BigQuery API integration with managed OAuth. Run SQL queries, manage datasets and tables, and analyze data at scale. Use this skill when users want to query BigQuery data, create or manage datasets/tables, run analytics jobs, or work with BigQuery resources. For other third party apps, use the api-gateway skill (https://clawhub.ai/byungkyu/api-gateway). Calls run through the `maton` CLI with OAuth login, or over raw HTTP with a Maton API key where the CLI cannot be installed. Every call is authenticated as the user's connection and reaches only what that connection's authorization allows, which the provider enforces on every request; the endpoints documented here are the ones this skill uses, and any other endpoint of this app needs the user to ask for it by name. Default to read and list calls, and confirm every write or new connection with the user. This file also documents the three constructs that turn a Google BigQuery connection into automation, in the order they are used: the connection (the first step), a hosted function that runs a Google BigQuery action through the Maton SDK, and a trigger that calls that function on a schedule or on an event. Those sections are the platform's own reference text, shared with the api-gateway skill, with Google BigQuery examples; they add no Google BigQuery capability - Google BigQuery is not an event source, a trigger cannot read Google BigQuery data, and the files under `references/<source>/triggers.md` are the platform's event catalogues for the sources Maton offers (time, Calendly, GitHub, Gmail, HubSpot, Linear, Notion, Slack, Stripe).

A spatial protocol engine for formatting, validating, and registering Smart Space Standard Units (SSSU) and managing entity spawning via local database tools.

Relative binding free-energy and activity-label prediction workflows using PBCNet 2.0 on SciMiner, with Gnina docking and PDB/database retrieval to complete...

Create and troubleshoot AWS Glue connections to JDBC databases (Oracle, SQL Server, PostgreSQL, MySQL, RDS), Redshift, Snowflake, and BigQuery. Gathers connection hints from user, discovers existing connections and RDS/Redshift candidates, registers credentials in Secrets Manager or IAM DB auth, configures VPC, and tests. Triggers on: connect to database, set up Glue connection, register data source, connect to Snowflake/BigQuery/RDS, connection timeout, test connection, troubleshoot connection. Do NOT use for moving data (use ingesting-into-data-lake), creating tables (use creating-data-lake-table), queries (use querying-data-lake), catalog exploration (use exploring-data-catalog), or SaaS (Salesforce, ServiceNow, SAP, MongoDB, Kafka).