Loading...
Loading...
Found 53 Skills
MUST USE when designing ClickHouse architectures, selecting between ingestion or modeling patterns, or translating best practices into workload-specific system designs. Complements clickhouse-best-practices with decision frameworks and explicit provenance labels.
Audit and improve SQL quality systematically — catch performance smells in raw SQL, ORM-generated queries, schema/modeling decisions, migrations, or PR diffs, explain the database-level impact, give a corrected version, and make the tradeoff explicit. Generic by design: no project, domain, ORM, or language config — any context it needs (is this table transactional? is the scan intentional? what volume is expected?) is raised during analysis, never assumed. Reach for it whenever someone writes, reviews, or optimizes a query or data-access code — "review this query", "why is this slow", "check my migration", "is this index right", or when you see N+1, SELECT *, a cartesian/row explosion, three-plus joins, a missing date filter on a growing table, OFFSET pagination, LIKE '%term%', NOT IN with nullable columns, an unindexed ORDER BY, an unbounded list, or a long transaction — even when they never say the word "SQL". Use it both to validate new code and designs and to audit existing ones.
Use this skill when you need to create or modify a LookML Explore. This includes defining the Explore, joins, access grants, and basic configuration.
Converts legacy SQL to modular dbt models. Use when migrating SQL to dbt for: (1) Converting stored procedures, views, or raw SQL files to dbt models (2) Task mentions "migrate", "convert", "legacy SQL", "transform to dbt", or "modernize" (3) Breaking monolithic queries into modular layers (discovers project conventions first) (4) Porting existing data pipelines or ETL to dbt patterns Checks for existing models/sources, builds and validates layer by layer.
Design data architecture at enterprise and solution levels. Cover data mesh, lakehouse, governance, domain-driven design, conceptual/logical/physical data modeling, platform selection, and compliance frameworks. Produce ADRs, data model diagrams, platform comparison matrices, and governance policy templates. Triggers on "design data platform", "choose data warehouse", "data mesh", "lakehouse architecture", "data governance", "data modeling", "platform selection", "data architecture decision", "compliance framework", or "data strategy". For applied AI solution architecture (RAG data plane, embeddings, vector stores in commercial or enterprise products), use applied-ai-architect-commercial-enterprise. For dbt analytics layers and mart delivery, use analytics-data-engineer—not data-architect.
Create and manage NocoBase data models via MCP. Use when users want to inspect or change collections, fields, relations, or view-backed schemas in a NocoBase app.
Design NoSQL database schemas for MongoDB and DynamoDB. Use when modeling document structures, designing collections, or planning NoSQL data architectures.
Expert database architect specializing in data layer design from scratch, technology selection, schema modeling, and scalable database architectures. Masters SQL/NoSQL/TimeSeries database selection, normalization strategies, migration planning, and performance-first design. Handles both greenfield architectures and re-architecture of existing systems. Use PROACTIVELY for database architecture, technology selection, or data modeling decisions.
Create and edit Omni Analytics semantic model definitions — views, topics, dimensions, measures, relationships, and query views — using YAML through the Omni CLI. Use this skill whenever someone wants to add a field, create a new dimension or measure, define a topic, set up joins between tables, modify the data model, build a new view, add a calculated field, create a relationship, edit YAML, work on a branch, promote model changes, or any variant of "model this data", "add this metric", "create a view for", or "set up a join between". Also use for migrating modeling patterns since Omni's YAML is conceptually similar to other semantic layer definitions.
Orchestrates the full journey from zero to a running Neo4j application. Executes 8 named stages in order: prerequisites → context → provision → model → load → explore → query → build. Each stage has its own reference file in references/ that the agent reads and follows when entering that stage. Supports both HITL and fully autonomous operation. Time budget: ≤15 min after DB is running (autonomous), ≤90 min total (HITL).
Best practices for building with Gadget. Use when developers need guidance on models, actions, routes, access control, Shopify/BigCommerce integrations, frontend patterns, API usage, permissions, or framework decisions. Triggers "model", "action", "route", "permission", "access control", "multi-tenancy", "Shopify", "BigCommerce", "frontend", "API client", "filter", "pagination", "webhook", "background job"
Load automatically when planning, researching, or implementing ANY Medusa backend features (custom modules, API routes, workflows, data models, module links, business logic). REQUIRED for all Medusa backend work in ALL modes (planning, implementation, exploration). Contains architectural patterns, best practices, and critical rules that MCP servers don't provide.