OpenMetadata is an open platform that organizes information about data, including its meaning, ownership, quality, history, and relationships. It provides governed context for data users, AI assistants, and agents that need to discover and understand trustworthy data.
Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/open-metadata/OpenMetadatanpx agentmods add skills/open-metadata/openmetadata/connector-buildingWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/open-metadata/openmetadata/connector-building)<a href="https://agentmods.dev/skills/open-metadata/openmetadata/connector-building"><img src="https://agentmods.dev/badge/skills/open-metadata/openmetadata/connector-building/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/open-metadata/openmetadata/connector-building"><img src="https://agentmods.dev/badge/skills/open-metadata/openmetadata/connector-building.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00047 | $0.03606 |
| Opus 5.5 | $0.00019 | $0.01442 |
| Sonnet 5.5 | $0.00009 | $0.00721 |
| Haiku 4.5 | $0.00005 | $0.00361 |
Grade A, and why
scaffold-connector scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 333 lines — stays where its author put it; the contents beside it link to each section on GitHub.
OpenMetadata Connector Building Skill
When to Activate
When a user asks to build, create, add, or scaffold a new connector, source, or integration for OpenMetadata.
Core Insight
One JSON Schema definition cascades through 6 layers: Python Pydantic models, Java models, UI forms (RJSF auto-render), API validation, test fixtures, and documentation. Define the schema once — everything else is generated or guided.
Workflow: 7 Phases
Phase 0: ENVIRONMENT — Set Up Python Dev Environment
Before any make or python commands, set up the environment from the repo root:
python3.11 -m venv env
source env/bin/activate
make install_dev generate
Always activate before running commands: source env/bin/activate
Phase 1: SCAFFOLD — Generate Boilerplate
Run the scaffold CLI to collect inputs and generate files:
source env/bin/activate
metadata scaffold-connector
Interactive mode collects: connector name, service type, connection type, auth types, capabilities, docs URL, SDK package, API endpoints, implementation notes, Docker image, container port.
Non-interactive mode:
metadata scaffold-connector \
--name my_db \
--service-type database \
--connection-type sqlalchemy \
--scheme "mydb+pymydb" \
--auth-types basic \
--capabilities metadata lineage usage profiler \
--docs-url "https://docs.example.com/api" \
--sdk-package "mydb-sdk" \
--docker-image "mydb/mydb:latest" \
--docker-port 5432
Output: JSON Schema + test connection JSON + Python files + CONNECTOR_CONTEXT.md as an AI working document. SQLAlchemy database connectors get concrete code templates; all others get skeleton files with pointers to reference connectors.
CONNECTOR_CONTEXT.md handling: The scaffold generates CONNECTOR_CONTEXT.md in the connector directory as a working document for any AI tool (Claude Code, Cursor, Codex, Copilot, Windsurf). It is gitignored — it stays local and is never committed to the repo. No cleanup needed.
What ships with it
9 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- connector-profile.schema.json 2.3 KB
- examples/dashboard-rest.yaml 1.1 KB
- examples/database-sqlalchemy.yaml 984 B
- examples/pipeline-sdk.yaml 1.0 KB
- GUIDE.md 15 KB
- references/architecture-decision-tree.md 3.5 KB
- references/capability-mapping.md 3.0 KB
- references/connection-type-guide.md 2.6 KB
- standards 12 B
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 333 lines · 47 tokens per session scan A 2c8733abdf7c
scaffold-connector is a skill published in the GitHub repository open-metadata/OpenMetadata (15,601 stars, last pushed today), licensed Apache-2.0. It adds 47 tokens to every session and 3,606 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
test-review
You are an expert DataHub test reviewer. Your role is to evaluate pytest smoke tests against established testing standards, identify issues, and provide actionable feedback.
apollo-router
Version-aware guide for configuring and running Apollo Router for federated GraphQL supergraphs. Generates correct YAML for both Router v1.x and v2.x. Use this skill when: (1) setting up Apollo Router to run a supergraph, (2) configuring routing, headers, or CORS, (3) implementing custom plugins (Rhai scripts or…
apollo-router-plugin-creator
Guide for writing Apollo Router native Rust plugins. Use this skill when: (1) users want to create a new router plugin, (2) users want to add service hooks (routerservice, supergraphservice, executionservice, subgraphservice), (3) users want to modify an existing router plugin, (4) users need to understand router…
apollo-server
Guide for building GraphQL servers with Apollo Server 5.x. Use this skill when: (1) setting up a new Apollo Server project, (2) writing resolvers or defining GraphQL schemas, (3) implementing authentication or authorization, (4) creating plugins or custom data sources, (5) troubleshooting Apollo Server errors or…
graphql-schema
Guide for designing and changing GraphQL schemas following industry best practices. Use this skill whenever you create or edit GraphQL SDL, such as a schema.graphql file or typeDefs, including when: (1) designing a new GraphQL schema or API, (2) adding or changing types, fields, arguments, mutations, or descriptions…
apollo-mcp-server
Guide for using Apollo MCP Server to connect AI agents with GraphQL APIs. Use this skill when: (1) setting up or configuring Apollo MCP Server, (2) defining MCP tools from GraphQL operations, (3) using introspection tools (introspect, search, validate, execute, explorer), (4) configuring authentication, CORS, health…