「schema」の検索結果

Suede-owned operations-architecture discipline that fixes the shape of a system before anyone builds it: entity schema first, exactly one write path per entity, every unit of work routed to a deterministic automation, an AI agent, or a human decision, every existing tool marked absorb, keep, or kill, and the build sequenced into phases whose completion is proved by a command. Use when scoping an automation or agent build, deciding whether a workflow needs a model at all, consolidating a sprawling tool stack, designing the data model under a workflow, choosing a migration path off an old system, or ordering a build so nothing lands on a moving foundation. NOT FOR: executing a planned multi-file repo change (use suede-graph-flo-xr); coordinating parallel builders with file ownership (use suede-agent-teams); lead lifecycle, scoring, and CRM routing rules (use suede-revops); proving agent output quality against an eval set (use suede-ai-eval); reviewing the finished diff (use suede-code-review).

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Guide for writing Rust doc comments that produce accurate generated contract documentation. Use when editing Instantiate/Execute/Query/Response types or any public schema-facing API.

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Guide for regenerating Axone contract schemas and rendered Markdown docs. Use when contract APIs or metadata change, when checking generated-doc drift, or when preparing documentation commits.

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。

Repository quality gates for Rust and generated artifacts. Use when validating changes locally or before committing Rust, schema, or documentation updates.

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 this skill for any Python backend work in this project: building FastAPI endpoints, writing service functions, defining Pydantic/SQLModel schemas, running Alembic migrations, or debugging 422 errors. Essential for authentication and authorization patterns — setting up get_current_user, is_superuser checks, admin-only guards, role-based access, and dependency injection chains like Depends(). Also covers middleware, background tasks, async SQLAlchemy sessions, ORM relationship loading, and request/response design. Activate whenever the question involves Python API code, FastAPI patterns, or backend architecture in this codebase. Not for frontend, Docker, CI/CD, or infrastructure.

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 Neo4j command-line tools — neo4j-cli (modern unified CLI — Cypher via Bolt, schema inspection, Aura management, Docker containers, credential management, agent skill catalog install; install with curl -sSfL https://neo4j.sh/install.sh | bash), neo4j-admin (backup, restore, import, memory sizing), cypher-shell (ad-hoc queries, scripting, CI/CD), or neo4j-mcp (quick MCP server install). Does NOT cover Cypher query authoring — use neo4j-cypher-skill. Does NOT cover driver upgrades — use neo4j-migration-skill. Does NOT cover full MCP editor configuration — use neo4j-mcp-skill.

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 installing, configuring, or troubleshooting the official Neo4j MCP server (neo4j/mcp) — connecting Claude Code, Claude Desktop, Cursor, Windsurf, VS Code, Kiro, or other MCP-compatible editors to a Neo4j database via stdio or HTTP transport. Covers the four MCP tools (get-schema, read-cypher, write-cypher, list-gds-procedures), read-only mode, and multi-database configuration. Does NOT cover writing Cypher queries via those tools — use neo4j-cypher-skill. Does NOT cover agent memory — use neo4j-agent-memory-skill. Does NOT cover Aura instance provisioning — use neo4j-aura-provisioning-skill.

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。

Ingests unstructured and semi-structured documents into Neo4j as a knowledge graph. Use when chunking PDFs, HTML, plain text, or Markdown; extracting entities and relationships from text with an LLM (SimpleKGPipeline, neo4j-graphrag); loading JSON via apoc.load.json; building Document→Chunk→Entity graph structures; or connecting LangChain/LlamaIndex document loaders to Neo4j. Covers neo4j-graphrag SimpleKGPipeline, LLM Graph Builder web UI, entity resolution, chunking strategies, and graph schema design for RAG pipelines. Does NOT handle structured CSV/relational import — use neo4j-import-skill. Does NOT handle GraphRAG retrieval after ingestion — use neo4j-graphrag-skill. Does NOT handle vector index creation — use neo4j-vector-search-skill.

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。

Configure and operate the Neo4j Connector for Kafka (sink + source) and the native Neo4j CDC API. Covers Cypher/Pattern/CUD sink strategies, CDC-based and query-based source, exactly-once semantics, DLQ error handling, Confluent Cloud managed connector, schema registry (Avro/JSON), and native db.cdc.query cursor-loop patterns (Neo4j 5.13+ Enterprise/Aura BC/VDC). Use when streaming Kafka events into Neo4j, streaming Neo4j changes to Kafka, or querying Neo4j change events without Kafka. Does NOT handle Cypher query authoring — use neo4j-cypher-skill. Does NOT handle bulk CSV/file import — use neo4j-import-skill. Does NOT handle GDS algorithms — use neo4j-gds-skill.

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。

Build and configure a GraphQL API backed by Neo4j using @neo4j/graphql v7 (current) or v5 (LTS). Covers Neo4jGraphQL constructor, getSchema(), assertIndexesAndConstraints(), type definitions with @node, @relationship (IN/OUT/UNDIRECTED), @cypher for custom resolvers, @authorization/@authentication for JWT/JWKS security, auto-generated queries/mutations, OGM programmatic access, subscriptions via CDC, and Apollo Federation. Use when writing typeDefs, securing fields, or wiring Neo4j to Apollo Server. Does NOT handle raw Cypher outside resolvers — use neo4j-cypher-skill. Does NOT cover Spring Data Neo4j entity mapping — use neo4j-spring-data-skill.

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。

Manages Neo4j Aura Agents via the v2beta1 REST API — create, list, get, update, delete, and invoke Aura agents backed by an AuraDB instance. Use when configuring Aura Agent tools (CypherTemplate, SimilaritySearch, Text2Cypher), setting system prompts, deploying agents to REST or MCP endpoints, invoking agents with natural language queries, or generating evaluation datasets (max 50 questions) for the Aura Agent evaluation feature. Covers OAuth2 auth, organization/project scoping, tool parameter schemas, and InvokeAgentResponse format. Does NOT cover AuraDB instance provisioning — use neo4j-aura-provisioning-skill. Does NOT cover vector index creation — use neo4j-vector-index-skill.

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 tests for data or ML code — what to test at each layer (transforms, schemas, properties, pipeline smoke, model-quality thresholds, determinism and parity), how to build small synthetic fixtures, and what not to test

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 before and after calling rollback_release - what a rollback restores per delivery mode, when a schema change makes a code rollback unsafe, how to work and report manual_steps, and what to do when rollback_release refuses

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 changing the database schema - detect the repo's migration tool, ship a new migration in its layout, never edit an applied one, and avoid lock-unsafe DDL on a live table

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。

PostgreSQL database administration — backup/restore (pg_dump, PITR, WAL archiving), health monitoring (connections, bloat, cache hit ratio, dead tuples), connection pooling (PgBouncer), replication (streaming, logical), vacuum/autovacuum tuning, and scheduled backups with retention. Use when user asks to backup a database, restore from backup, monitor database health, set up replication, or perform DBA tasks. Do NOT use for schema design (use db-sculptor), query optimization (use db-sculptor), or migration planning (use db-sculptor).

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。

SEO + Generative Engine Optimization (GEO) for 2026 — technical SEO, on-page optimization, structured data (JSON-LD, schema.org), Core Web Vitals, citability scoring (llms.txt, entity clarity), AI search engine optimization (ChatGPT, Gemini, Perplexity, Claude, Google AI Overviews), and brand authority building. Use when user asks to optimize a site for search engines, improve SEO, write SEO content, implement structured data, optimize for AI search/GEO, audit a page, or improve Google rankings. Do NOT use for content writing strategy (use content-marketing), performance optimization beyond Core Web Vitals (use performance-profiler), or paid ad strategy.

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。

Performance profiling and optimization for web apps — Core Web Vitals (LCP, INP, CLS), Lighthouse audits, bundle analysis, backend profiling (CPU, memory, DB queries), N+1 detection, caching strategies (Redis, CDN, HTTP), and performance budgets. Use when user asks to improve performance, run Lighthouse audit, profile a Node.js app, optimize Core Web Vitals, reduce bundle size, or investigate slow response times. Do NOT use for database schema optimization (use db-sculptor), Docker image optimization (use docker), or CDN configuration.

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Optimize content for AI-generated responses and LLM citations (ChatGPT, Perplexity, Google AI Overview, Claude, Gemini). Use when the user mentions 'GEO', 'AEO', 'AI SEO', 'LLM optimization', 'citation rate', 'AI visibility', 'optimize for ChatGPT', 'roundup pages', or wants to audit pages for AI discoverability. Includes terminology alignment, FAQ schemas, and community signal strategies.

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Database design patterns including schema design, migrations, soft deletes, and Exposed ORM. Use when creating tables, writing migrations, or implementing repositories.

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Create, compose, migrate, and review TypeScript runtime type guards with is-kit. Use when adding reusable guards; replacing typeof, null checks, or handwritten type predicates; narrowing unknown values; validating JSON or API responses; building collection or object schemas with struct or typedStruct; or reviewing existing is-kit usage. Do not use for one-off environment checks or validation that requires data transformation or rich error reporting.

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。

飞书官方 CLI 工具 (lark-cli)，在终端中操作飞书。覆盖消息、文档、多维表格、电子表格、 日历、邮箱、任务、会议、审批等 12 大业务域，提供 200+ 命令。 当需要通过 CLI 方式操作飞书（而非飞书 OAuth API）时使用此 Skill。 触发词："lark-cli"、"飞书CLI"、"飞书命令行"、"lark命令"。 适用场景： (1) 需要通过命令行执行飞书操作（发消息、查日程、读写文档等） (2) 需要批量操作飞书数据 (3) AI Agent 需要以用户/机器人身份调用飞书 API (4) 需要底层 API 调试或 schema 查询 (5) 用户提到 "lark-cli"、"飞书CLI"

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Tests API contracts for REST, GraphQL, gRPC, AsyncAPI, and webhooks. Use when adding schema validation or breaking-change gates (Pact, can-i-deploy, oasdiff, buf); not API design.

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Designs streaming platforms for Kafka, Flink, CDC, and lakehouse ingestion. Use when planning event backbones, CDC pipelines, schema governance, or real-time lakehouse delivery.

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 GitNexus itself — available tools, how to query the knowledge graph, MCP resources, graph schema, or workflow reference. Examples: "What GitNexus tools are available?", "How do I use GitNexus?"

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。