菜单
Load all OpenMetadata connector development standards into context. Use before building or reviewing connectors to ensure consistent patterns.
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/open-metadata/OpenMetadata --skill connector-standards命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
Set up, verify, or repair a local OpenMetadata development environment on macOS or Linux. Installs the toolchain (Java 21, Maven, Node 22, Yarn 1.x, Python 3.10+, ANTLR 4.9.2, Docker), creates the Python venv, generates models, installs UI dependencies and pre-commit hooks. Use for a fresh clone, a new worktree, onboarding, or when a build fails with a missing/wrong tool.
Meta-skill loaded at session start. Directs Claude to check for applicable OpenMetadata skills before starting any task. Ensures structured workflows are followed.
Run `mvn spotless:apply` to fix Java checkstyle / formatting failures and verify the result. Invoke after authoring or modifying any `.java` files, or when CI reports a "Java checkstyle failed" or "Fix Java checkstyle" issue on a PR.
基于 SOC 职业分类
正在显示 SKILL.md
| name | connector-standards |
| description | Load all OpenMetadata connector development standards into context. Use before building or reviewing connectors to ensure consistent patterns. |
| user-invocable | true |
| argument-hint | [optional: specific standard name like 'testing' or 'database'] |
| allowed-tools | ["Read","Glob"] |
When a user asks to "load standards", "show connector standards", or before starting any connector development or review work.
If no specific standard is requested, load all standards in this order:
${CLAUDE_SKILL_DIR}/standards/main.md — Architecture overview${CLAUDE_SKILL_DIR}/standards/patterns.md — Error handling, logging, pagination${CLAUDE_SKILL_DIR}/standards/code_style.md — Python and JSON Schema conventions${CLAUDE_SKILL_DIR}/standards/schema.md — Connection schema patterns${CLAUDE_SKILL_DIR}/standards/connection.md — Connection class patterns${CLAUDE_SKILL_DIR}/standards/service_spec.md — ServiceSpec registration${CLAUDE_SKILL_DIR}/standards/testing.md — Unit and integration test patterns${CLAUDE_SKILL_DIR}/standards/registration.md — How to register a connector${CLAUDE_SKILL_DIR}/standards/performance.md — Performance best practices${CLAUDE_SKILL_DIR}/standards/memory.md — Memory management and OOM prevention${CLAUDE_SKILL_DIR}/standards/lineage.md — Lineage extraction methods${CLAUDE_SKILL_DIR}/standards/sql.md — SQLAlchemy patterns and URL buildingThen read all source-type standards:
${CLAUDE_SKILL_DIR}/standards/source_types/*.md
If a specific standard or service type is requested:
| Request | File to Load |
|---|---|
| "testing" | standards/testing.md |
| "patterns" | standards/patterns.md |
| "schema" | standards/schema.md |
| "lineage" | standards/lineage.md |
| "sql" | standards/sql.md |
| "memory" | standards/memory.md |
| "database" | standards/source_types/database.md |
| "sql databases" | standards/source_types/sql_databases.md |
| "data warehouses" | standards/source_types/data_warehouses.md |
| "nosql" | standards/source_types/nosql_databases.md |
| "dashboard" | standards/source_types/dashboard.md |
| "pipeline" | standards/source_types/pipeline.md |
| "messaging" | standards/source_types/messaging.md |
| "mlmodel" | standards/source_types/mlmodel.md |
| "storage" | standards/source_types/storage.md |
| "search" | standards/source_types/search.md |
| "api" | standards/source_types/api.md |
| etc. | standards/source_types/{name}.md |
Confirm to the user which standards were loaded and summarize the key points. Example:
Loaded 12 core standards + 11 source-type standards. Key points:
- Schema-first: one JSON Schema → Python, Java, TypeScript, UI forms
- Use
BaseConnectionfor SQLAlchemy,get_connection()/test_connection()for others- Use pytest with plain
assert, no unittest.TestCase- Always include copyright header, use
ingestion_logger()- Lineage via query logs (database), SQL parsing (dashboard), or task metadata (pipeline)