'schemas' 검색 결과

Use BEFORE editing any file in `supabase/migrations/` or `supabase/schemas/`, OR when the user runs a `/database` subcommand (`compact local migration`, `rls scenarios`, `align`). Encodes the three contracts that protect the Grida database layer: applied migrations are immutable, RLS implementation mirrors tests (never the reverse), `schemas/*.sql` is the human-readable end-state. Companion to `supabase/AGENTS.md` (RLS, grants, security boundaries).

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Use this skill for general PostgreSQL table design. **Trigger when user asks to:** - Design PostgreSQL tables, schemas, or data models when creating new tables and when modifying existing ones. - Choose data types, constraints, or indexes for PostgreSQL - Create user tables, order tables, reference tables, or JSONB schemas - Understand PostgreSQL best practices for normalization, constraints, or indexing - Design update-heavy, upsert-heavy, or OLTP-style tables **Keywords:** PostgreSQL schema, table design, data types, PRIMARY KEY, FOREIGN KEY, indexes, B-tree, GIN, JSONB, constraints, normalization, identity columns, partitioning, row-level security Comprehensive reference covering data types, indexing strategies, constraints, JSONB patterns, partitioning, and PostgreSQL-specific best practices.

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Use this skill when creating database schemas or tables for Timescale, TimescaleDB, TigerData, or Tiger Cloud, especially for time-series, IoT, metrics, events, or log data. Use this to improve the performance of any insert-heavy table. **Trigger when user asks to:** - Create or design SQL schemas/tables AND Timescale/TimescaleDB/TigerData/Tiger Cloud is available - Set up hypertables, compression, retention policies, or continuous aggregates - Configure partition columns, segment_by, order_by, or chunk intervals - Optimize time-series database performance or storage - Create tables for sensors, metrics, telemetry, events, or transaction logs **Keywords:** CREATE TABLE, hypertable, Timescale, TimescaleDB, time-series, IoT, metrics, sensor data, compression policy, continuous aggregates, columnstore, retention policy, chunk interval, segment_by, order_by Step-by-step instructions for hypertable creation, column selection, compression policies, retention, continuous aggregates, and indexes.

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Governs Event Schema Sets, the Fabric registries of event types and message payload schemas. Covers create, rename, update and delete of a definition, and listing, property reads or base64 decoding. Every Event Schema Set task belongs here. Ingestion pipelines are eventstream-cli.

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Design relational schemas from requirements with normalization, migrations, ERDs, RLS policies, and indexes for PostgreSQL, MySQL, and SQLite. Use when designing new features, reviewing schemas, or adding multi-tenancy.

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Designs multi-agent system architectures with orchestration patterns, tool schemas, and performance evaluation. Use when building AI agent systems, designing agent workflows, creating tool schemas, or evaluating agent performance.

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Creates reusable Zod v4 schemas, validates API payloads, forms, and configuration input, transforms and coerces data safely, and handles validation errors with strong type inference for TypeScript applications. Use when designing validation layers, parsing `z.string()`, `z.object()`, or `z.email()` schemas, or implementing runtime type-safe data validation.

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Generate output schemas (dataset_schema.json, output_schema.json, key_value_store_schema.json) for an Apify Actor by analyzing its source code. Use when creating or updating Actor output schemas.

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Expert at evolving database schemas safely. Covers migration strategies, zero-downtime deployments, rollback planning, and data transformation. Knows how to change production schemas without breaking running applications. Use when "database migration, schema change, add column, alter table, drizzle migrate, prisma migrate, zero downtime migration, rollback migration, database, migrations, schema, drizzle, prisma, postgresql, zero-downtime" mentioned.

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CatColab Schemas - database schema modeling distinguishing entities (tables) from attributes (columns). Foundation for ACSets (Attributed C-Sets) and AlgebraicJulia data structures.

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VNX-specific domain expertise for the intelligence system, central state databases, and dispatch lifecycle. Use when working on quality_intelligence.db / runtime_coordination.db, code_snippets FTS5, snippet_metadata linkage, success_patterns / antipatterns, T0 state projection (t0_state.json), intelligence_injections lifecycle, dispatch → receipt → events → archive flow, central state architecture, project_id propagation across VNX, or any code that reads/writes VNX governance state. Triggers include: editing intelligence injection code, querying central DB for cross-project insights, debugging "intelligence not appearing in T0 state", modifying schema in `schemas/quality_intelligence.sql` or `schemas/runtime_coordination_v*.sql`, working with `scripts/quality_db_init.py` or `scripts/lib/coordination_db.py`.

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VNX-specific domain expertise for the intelligence system, central state databases, and dispatch lifecycle. Use when working on quality_intelligence.db / runtime_coordination.db / dispatch_tracker.db, code_snippets FTS5, snippet_metadata linkage, success_patterns / antipatterns, T0 state projection (t0_state.json), intelligence_injections lifecycle, dispatch → receipt → events → archive flow, central state architecture, project_id propagation across VNX, or any code that reads/writes VNX governance state. Triggers include: editing intelligence injection code, querying central DB for cross-project insights, debugging "intelligence not appearing in T0 state", modifying schema in `schemas/quality_intelligence.sql` or `schemas/runtime_coordination_v*.sql`, working with `scripts/quality_db_init.py` or `scripts/lib/coordination_db.py`.

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FastAPI web framework conventions: APIRouter with prefix/tags/dependencies, Pydantic v2 schemas (BaseModel, model_config, field_validator, model_validator), dependency injection with Depends, async SQLAlchemy session yielding, OAuth2PasswordBearer + JWT auth, lifespan context manager, pydantic-settings configuration, OpenAPI customization, and HTTPException error handling. Activated automatically by fastapi-plugin/stack.md. Use this skill to: - Structure FastAPI apps with per-feature APIRouter modules and a central app factory. - Write Pydantic v2 request and response schemas with proper validators. - Build a reusable Depends-based dependency chain for DB session, auth, and pagination. - Implement JWT-based authentication with get_current_user dependency. - Configure the app from environment variables via pydantic-settings. Do NOT use this skill for: - SQLAlchemy ORM model configuration and Alembic migrations — see fastapi-plugin:sqlalchemy-patterns. - Python language idioms — see python-foundation:python-conventions. - Testing patterns — see python-foundation:pytest-testing.

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Research-backed JSON Schema refresh workflow for updating existing schemas with validation checkpoints and cross-reference verification.

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Define Malli schemas for commands, events, and queries using defschemas

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End-to-end form handling with react-hook-form, Zod schemas, validation patterns, error messaging, field arrays, and multi-step wizards. Use for complex forms, validation architecture, autosave, field dependencies. Activate on "form validation", "react-hook-form", "Zod", "form error", "multi-step form", "wizard". NOT for simple HTML forms, backend validation only, or non-React frameworks.

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Validates agent outputs against expected schemas and quality criteria. Ensures outputs meet structural requirements and content standards. Activate on 'validate output', 'output validation', 'schema validation', 'check output', 'output quality'. NOT for confidence scoring (use dag-confidence-scorer) or hallucination detection (use dag-hallucination-detector).

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Data structures and serialization formats for agent-to-agent communication. Covers message envelopes, structured output schemas, capability declarations, task handoff payloads, error/retry signaling, and context windows as data structures. Deep comparison of A2A protocol, MCP, OpenAI function calling, and LangChain message types. Teaches when to use rigid schemas vs free-form with validation, typed vs untyped, streaming vs batch. Activate on: "agent message format", "agent communication schema", "agent-to-agent protocol", "A2A protocol", "MCP message format", "structured output for agents", "agent interop", "interchange format", "agent serialization", "task handoff format", "capability declaration". NOT for: what agents say to each other (use agent-conversation-protocols), orchestration topology (use multi-agent-coordination), building agent infrastructure (use agentic-infrastructure-2026).

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Use when the user asks to design a multi-agent system, pick an orchestration pattern (supervisor/swarm/pipeline), generate tool schemas for agents, or evaluate agent execution logs for cost, latency, and failure bottlenecks. Examples: 'design an agent architecture for research automation', 'generate Anthropic tool schemas from these tool descriptions', 'analyze these agent run logs for bottlenecks'. NOT for Claude Code workflow files (use workflow-builder) or single-agent prompt design (use agent-workflow-designer).

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Use Exasol document-file virtual schemas for federated read-only access to object and file storage — S3, Google Cloud Storage, Azure Blob Storage, and Azure Data Lake Storage Gen2 document files. Covers document-file adapter family selection, the EDML `MAPPING` definition, `CREATE VIRTUAL SCHEMA` usage, refresh workflows, and query-side troubleshooting.

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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.

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Align stable paths, APIs, CLI commands, schemas, events, IDs, storage keys, and UI/programmatic parity with the surface registries.

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Generate output schemas (dataset_schema.json, output_schema.json, key_value_store_schema.json) for an Apify Actor by analyzing its source code. Use when creating or updating Actor output schemas.

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Design database schemas with normalization, relationships, and constraints. Use when creating new database schemas, designing tables, or planning data models for any database paradigm.

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