1 633 database skills from 232 authors. They work on schemas, queries and moving data between them. Half of them fit into 2 236 tokens or less — that is what one costs your context window when the agent loads it. 285 ship runnable scripts rather than instructions alone. 7 of them cannot work without an MCP server, most often rube. We also found 383 copies of these same skills sitting in other people's repositories — counted once here, not 383 times.
1 633 unique 232 authors 712 updated this month 156 from vendors
Writes data from MATLAB to relational databases and performs database operations. Use when writing data with sqlwrite, updating rows with sqlupdate, executing SQL with execute, running stored procedures, managing transactions with commit/rollback, creating tables, or using SQL prepared statements.
Use when importing or exporting vehicle data from/to log files (MDF/MF4/DAT, BLF, ASC/TXT), decoding CAN/CAN FD/LIN messages to signals via DBC, ARXML, or LDF databases, writing timetable data to MDF or BLF files, or calling blfread, blfinfo, blfwrite, mdfRead, mdfWrite, mdfCreate, mdfInfo, canSignalImport, canMessageImport, canMessageTimetable, canFDMessageTimetable, canSignalTimetable, or linMessageTimetable.
Build with Aurora DSQL — manage schemas, execute queries, handle migrations, diagnose query plans, diagnose cluster performance, load data, and develop applications with a serverless, distributed SQL database. Covers IAM auth, multi-tenant patterns, MySQL-to-DSQL and PostgreSQL-to-DSQL schema conversion, FK replacement code generation, OCC retry patterns, ORM migration (Django/EF Core/Hibernate/Rails), DDL operations, query plan explainability, system diagnostics via CloudWatch AAS, SQL compatibility validation, and bulk data loading. Triggers on phrases like: DSQL, Aurora DSQL, distributed SQL database, serverless PostgreSQL-compatible database, migrate to DSQL, DSQL query plan, DSQL EXPLAIN ANALYZE, DSQL ENUM, DSQL foreign key, DSQL OCC retry, DSQL multi-region, DSQL JSONB, DSQL GIN index, load into DSQL, load CSV into DSQL, bulk load DSQL, aurora-dsql-loader, DSQL slow, DSQL performance, DSQL wait events, DSQL AAS.
> EU AI Act (Regulation (EU) 2024/1689) compliance advisor — risk classification across all four tiers, all 9 prohibited practices (Art. 5, including the nudification/CSAM prohibition from Dec 2, 2026), all 8 Annex III high-risk use case areas, provider and deployer obligations (Arts. 9–17, 26), GPAI model obligations including the July 2025 Code of Practice (Arts. 51–55), conformity assessment and CE marking (Arts. 43–48), EU AI database registration, Art. 50 transparency (chatbots, synthetic media, AI-generated content), governance (AI Office, AI Board), penalties (Art. 99), confirmed phase-in timeline (Digital Aug 2, 2028), and cross-framework mapping to ISO 42001, NIST AI RMF, and GDPR. Use for any EU AI regulation, AI system classification, or AI compliance question. Current as of July 2026. GPAI enforcement powers activate August 2, 2026.
Comprehensive guide for building OpenAPI endpoints with chanfana - schema definition, request validation, CRUD operations, D1 database integration, and exception handling
数据库迁移助手 - Schema 对比、迁移脚本生成
Strix SQL 注入测试手册,覆盖 union、blind、error-based 与 ORM 绕过技巧;触发名:strix-sql-injection
Detect potential code-level performance smells in Java - streams, collections, boxing, regex, object creation. Provides awareness, not absolutes - always measure before optimizing. For JPA/database performance, use jpa-patterns instead.
Export marketing data. Use when: sending data to BigQuery, Google Sheets, or Supabase for analysis or reporting.
> How to add a new ingestion warning type to the event ingestion pipeline. Use when emitting a new warning from nodejs ingestion code (emitIngestionWarning, captureIngestionWarning, pipeline `warnings` arrays, `drop()` with warnings), when adding a warning type, category, or severity, or when a typecheck error says a string is not assignable to IngestionWarningType. Covers the INGESTION_WARNING_TYPES registry (the single source of truth for type, category, and severity), the details-key conventions that ClickHouse v2 materializes into columns, debouncing, and the downstream surfaces to keep in sync (v1 UI map, resolving-ingestion-warnings skill, docs, v2 API).
> Guide for adding a new RPC to personhog-replica and personhog-router. Covers eligibility checks, proto definition, code generation for Python and Node.js clients, Rust implementation (storage trait, postgres queries, service handler, router wiring), and index compatibility validation. Use when adding a new gRPC endpoint to personhog, migrating a Django ORM query to personhog, or extending the personhog service API.
> Pull and interpret production experiment query-performance data from the staff-only slowest experiment queries, precompute read/build health, and preaggregation cache footprint. and response field semantics (exception codes, exposure paths, precompute skip reasons, job states). Use when investigating slow or failing experiment queries, precompute regressions, 307/159/241 errors, preaggregation table growth, or when asked how experiment query performance or the precompute rollout is doing in production.
> Analyze PostHog insights, dashboards, or teams beyond the current project by querying the prod Postgres replicas synced into the dogfood data warehouse (US project 2, "PostHog App + Website"). Use when asked to analyze insights across all teams or projects, another team's insights, or fleet-wide insight/dashboard usage — cases where `system.insights` only returns the current project's rows and the agent would otherwise report the data as inaccessible. Covers the synced table names for US and EU and the column-verification workflow.
ClickHouse migration patterns and rules. Use when creating or modifying ClickHouse migrations.
Django migration patterns and safety workflow for PostHog. Use when creating, adjusting, or reviewing Django/Postgres migrations, including non-blocking index/constraint changes, multi-phase schema changes, data backfills, migration conflict rebasing, and product model moves that require SeparateDatabaseAndState. Also use for any deletion or removal of a model, table, column, product, or app — including deleting migration files or retiring a feature — even when no migration is written.
> Produce and structure slow-query performance reports for PostHog's production ClickHouse (US and EU). Use when asked for a slow query report, query performance analysis over the last N days, per-team query cost, OOM or timeout investigation, cluster cost/memory regressions, or materialization candidates. Covers the modern `query_log_archive` source (typed `lc_*` columns, multi-day retention), how to categorize and attribute slow queries, root-cause patterns (unmaterialized JSONExtract, high-cardinality breakdowns, heavy joins), and the report structure. Runs queries via the `query-clickhouse-via-metabase` skill.
> Guide for using the Grafana MCP to monitor and diagnose the Node.js ingestion pipeline workers in production. Use when investigating event lag, drops, pipeline errors, person/group processing, Kafka consumer health, Redis, Postgres, ClickHouse downstream health, or any ingestion worker question. Covers prod-us and prod-eu environments.
Workflow for optimizing ClickHouse and HogQL queries. Use when a HogQL query, query runner, insight, or report is too slow; when a hand-written ClickHouse query (via `sync_execute` or in a migration) is too slow; when ClickHouse times out or hits memory limits; when investigating a slow `system.query_log` row; or when reviewing a proposed HogQL printer change for performance. Covers extracting the ClickHouse SQL, common smells (`FROM ... FINAL`, `JSONExtract` over properties, missing skip indexes, self-joins, CTE blow-up), measuring against a real cluster, and applying the fix at the right layer (printer, query runner, or migration). Does NOT cover Postgres / Django ORM / app-database queries; those need pganalyze and the Postgres section of `query-performance-optimization.md`.
> Run ClickHouse `system.query_log` analysis via the internal Metabase API. Use when investigating slow queries, materialization candidates, per-team query performance, ClickHouse cost or memory issues, or any system.query_log question. Covers prod-us and prod-eu, SSO-gated cookie auth via `hogli`, and ready-to-run query patterns.
Run read-only SQL against the local Postgres app database (SELECT, EXPLAIN, EXPLAIN ANALYZE on SELECT). Default local URL postgres://posthog:posthog@localhost:5432/posthog; else DATABASE_URL. Use when querying the local DB, inspecting tables, debugging data, or analyzing query plans. Mutations are strictly forbidden.
> Query tophog — the ingestion pipeline's heavy-hitter store in ClickHouse — to identify hot or expensive actors (team_id, distinct_id, session_id, partition) during incident triage. Use when investigating ingestion lag, a hot or lagging Kafka partition, expensive person processing, merge storms, or any "which team or distinct_id is causing this" question. Covers the internal Metabase access path (SSO via hogli), the tophog schema, and the identifiers.
Guide for writing performant ClickHouse queries in PostHog product code. Use when writing HogQL query runners, designing a ClickHouse table for a new product, adding materialized columns or skip indexes, or choosing a row ID format. For optimizing an existing query that is already too slow, use `/optimizing-clickhouse-and-hogql-queries` instead.
Run a ClickHouse query optimization campaign on one git branch using pi-autoresearch, dynamic lanes and hypotheses, baseline result capture, correctness checks, and stagnation-aware lane/campaign review.
Expert knowledge for Azure Cache for Redis development including troubleshooting, best practices, decision making, architecture & design patterns, security, configuration, integrations & coding patterns, and deployment. Use when configuring Redis clustering/replication, VNet/Private Link access, TLS/auth, CLI automation, or ARM/Bicep deployments, and other Azure Cache for Redis related development tasks. Not for Azure Managed Redis (use azure-managed-redis), Azure Cosmos DB (use azure-cosmos-db), Azure Table Storage (use azure-table-storage).
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 Cosmos DB NoSQL/Mongo/Cassandra/PostgreSQL APIs, change feed, multi-region HA, vector search, or Kafka/Spark, 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 Database for MySQL (use azure-database-mysql), Azure Database for PostgreSQL (use azure-database-postgresql).
Expert knowledge for Azure Database Migration service development including troubleshooting, decision making, limits & quotas, security, integrations & coding patterns, and deployment. Use when migrating MySQL, PostgreSQL, SQL Server/SSIS, MongoDB, or hybrid databases with Azure DMS, and other Azure Database Migration service related development tasks. Not for Azure Migrate (use azure-migrate), 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).
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 using MySQL Flexible Server, HA/DR, backups/geo-restore, Private Link/TLS, or AKS/app integrations, and other Azure Database for MySQL related development tasks. Not for Azure Database for MariaDB (use azure-database-mariadb), Azure Database for PostgreSQL (use azure-database-postgresql), Azure SQL Database (use azure-sql-database), Azure SQL Managed Instance (use azure-sql-managed-instance).
Expert knowledge for Azure Database for PostgreSQL development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when tuning flexible servers, configuring pgvector/AGE, setting replicas, securing with VNet/TLS, or planning migrations, and other Azure Database for PostgreSQL related development tasks. Not for Azure Database for MySQL (use azure-database-mysql), Azure Database for MariaDB (use azure-database-mariadb), Azure SQL Database (use azure-sql-database), Azure SQL Managed Instance (use azure-sql-managed-instance).
Expert knowledge for Azure Managed Redis development including troubleshooting, best practices, decision making, architecture & design patterns, security, configuration, integrations & coding patterns, and deployment. Use when using Azure Managed Redis clusters, sharding, persistence, geo-replication, Entra auth, or Private Link, and other Azure Managed Redis related development tasks. Not for Azure Cache for Redis (use azure-cache-redis).
Expert knowledge for Azure Migrate development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when configuring Azure Migrate appliances, Arc/AMA, AppCAT/.NET assessments, VMware agentless replication, or Resource Mover, and other Azure Migrate related development tasks. Not for Azure Database Migration service (use azure-database-migration), Azure Site Recovery (use azure-site-recovery), Azure Virtual Machines (use azure-virtual-machines), SQL Server on Azure Virtual Machines (use azure-sql-virtual-machines).
Expert knowledge for Azure Service Connector development including troubleshooting, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when wiring Azure compute to databases, messaging, storage, AI services, or Azure Cache for Redis via Service Connector, and other Azure Service Connector related development tasks. Not for Azure API Management (use azure-api-management), Azure Functions (use azure-functions), Azure Logic Apps (use azure-logic-apps), Azure App Service (use azure-app-service).
Expert knowledge for Azure SQL Database development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when choosing tiers/HA, tuning performance, configuring geo-replication, serverless/elastic pools, or Hyperscale, and other Azure SQL Database related development tasks. Not for Azure Database for MariaDB (use azure-database-mariadb), Azure Database for MySQL (use azure-database-mysql), Azure Database for PostgreSQL (use azure-database-postgresql), Azure SQL Managed Instance (use azure-sql-managed-instance).
Expert knowledge for Azure SQL Managed Instance development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when sizing MI tiers/subnets, configuring networking/VNet access, setting up backups/geo-replication, or Entra/KV-based security, and other Azure SQL Managed Instance related development tasks. Not for Azure SQL Database (use azure-sql-database), SQL Server on Azure Virtual Machines (use azure-sql-virtual-machines), Azure Database for MySQL (use azure-database-mysql), Azure Database for PostgreSQL (use azure-database-postgresql).
通过 Evotown Database MCP Proxy 对已注册的业务数据库执行只读 SQL 查询。当用户需要查 CRM、订单、报表等结构化数据时使用；禁止直连数据库或写入 SQL。
> Answer questions against the knowledge base wiki. Use when the user asks a question about their collected knowledge, wants to explore connections between topics, says "what do I know about X", or wants to search their wiki.
EU AI Act (Regulation (EU) 2024/1689) compliance advisor — risk classification across all four tiers, all 9 prohibited practices (Art. 5, including the nudification/CSAM prohibition from Dec 2, 2026), all 8 Annex III high-risk use case areas, provider and deployer obligations (Arts. 9–17, 26), GPAI model obligations including the July 2025 Code of Practice (Arts. 51–55), conformity assessment and CE marking (Arts. 43–48), EU AI database registration, Art. 50 transparency (chatbots, synthetic media, AI-generated content), governance (AI Office, AI Board), penalties (Art. 99), confirmed phase-in timeline (Digital Omnibus adopted June 29, 2026: Annex III deferred to Dec 2, 2027; Annex I to Aug 2, 2028), and cross-framework mapping to ISO 42001, NIST AI RMF, and GDPR. Use for any EU AI regulation, AI system classification, or AI compliance question. Current as of July 2026. GPAI enforcement powers activate August 2, 2026.
a skill that determines whether a given subject matter qualifies for copyright protection under EU law by applying the Cofemel two-step test (concept of work + originality), grounded in fifteen CJEU judgments (including the most recent Mio/Konektra and Calinescu) and the four EU directive provisions on originality. It argues from one of two positions: pro-rightsholder (establishing that the work is original) or pro-alleged-infringer (contesting work status or originality via the four exclusion grounds — technical function, rule-dictated outcome, sweat of the brow, idea/functionality). It includes sector-specific modules for photography, software/GUI, sporting events, databases, applied art, functional texts, derivative works/critical editions, and multimedia works.
Use when syncing real-time data, structuring JSON trees, reading/writing, creating listeners, enabling offline persistence, managing presence, sharding, or writing security rules.
Web 视觉设计 SKILL。输入 PRD / 参考 URL / 截图 / 关键词（任意组合），先产出一份标准化 DESIGN.md 设计规范，用户确认后据此生成 UI/UX、视觉、动效、响应式全部达标的 web 代码。专攻 web 端：Landing Page、Portfolio、产品页、博客、个人站、SaaS 介绍页等。当用户说"帮我做个网站""设计一个页面""参考 XX 做一个""把这个截图/PRD 做成网页""做一个 landing page""出一份 design 规范"时触发。不用于后端、数据库、纯逻辑 bug 修复。
>- Creates Dataverse tables, columns, and relationships for a Power Pages site based on a data model proposal. Use when the user wants to set up the data model, create database tables, or build the Dataverse schema for their site.
Domain-specific authoritative source databases, search strategies by topic category, and source freshness rules
Database comparison catalogs, RDBMS vs NoSQL selection criteria, CAP/ACID/BASE theory, OLTP vs OLAP, and technology-specific characteristics
SQL and NoSQL query optimization techniques, indexing strategies, execution plan analysis, JOIN algorithms, cardinality estimation, and database-specific query patterns
Database security (encryption, access control, injection prevention), data governance (lineage, quality, MDM), and compliance frameworks (GDPR, CCPA, HIPAA)
SwiftData class inheritance patterns for hierarchical models with type-based querying, polymorphic relationships, and when to choose inheritance vs enums. Use when designing SwiftData model hierarchies.
Monitors Hacker News for user-configured keywords, deduplicates against a local SQLite cache, and sends Slack alerts for new matching posts. Use when asked to monitor Hacker News for mentions, track keywords on HN, get alerts when something is posted about a topic on Hacker News, or set up HN keyword monitoring. Trigger when a user mentions Hacker News alerts, HN monitoring, keyword tracking on HN, or wants to know when a topic appears on Hacker News.
Plan additive, zero-downtime schema migrations with rollout, backfill, and rollback awareness. Use when renaming columns, backfilling data, or shipping risky database changes.
Apply MongoDB data-modeling, indexing, and query rules from access patterns. Use when designing schemas, choosing embed vs reference, or tuning MongoDB query behavior.