DATAMIMIC plugin
A plugin for Claude Code and Codex: an agent describes synthetic test data in XML and proves the project with the build command.
- 116 ★ on the repository
- Last commit
- MIT
Extended access
Why this level
- The setup skill installs the `datamimic-ce` package from PyPI into the project environment, and executing the XML can write into databases.
Install
- Works in
In your terminal, with SkillFoxx CLI
npx skillfoxx add plugins/datamimicDetects the agents on your machine, shows the access level and pins the version.
Other ways to install
Access and checks
How we reviewExtended accessThe tool runs code or makes network calls and only reads external data.
- The setup skill installs the `datamimic-ce` package from PyPI into the project environment, and executing the XML can write into databases.
This is third-party code. Review the repository files before installing.
What it does
The plugin installs the `generate-test-data` and `setup-datamimic` skills for DATAMIMIC Community Edition into Claude Code or Codex. The agent describes a dataset in `datamimic.xml` and `acceptance/build.json`, runs `datamimic authoring build` and repairs the project from a structured report until it reaches the `ready` status. CE generates deterministic synthetic data and pseudonymises exports for PostgreSQL, MySQL, Oracle, MS SQL, SQLite, MongoDB, CSV, JSON, XML and XLSX. If the package is missing from the project environment, the setup skill installs `datamimic-ce` version 5 from PyPI. MCP clients get an optional `datamimic-mcp` adapter.
Who it is for. QA engineers and developers who need reproducible test data for CI and staging without production database dumps.
Good fit when
- Four customers with two orders each, where every order references the real customer identifier.
- A staging export is pseudonymised with a repeatable result from a seed.
- Two runs of a model must give identical data, which the SHA-256 hash shows.
- Fresh data is written into PostgreSQL or SQLite before CI tests.
Not a fit when
- You need a PII scanner and an audit log: they belong to the Enterprise Platform and are absent from CE.
- Data must land consistently in Oracle, MongoDB and Kafka at once: multi-system execution exists only in Enterprise.
- EDIFACT, SWIFT MT, HL7 or FHIR messages are required: their templates belong to the platform.
Example request
Create deterministic test data with DATAMIMIC: four customers with two orders each, every order carrying the real customer identifier. Verify the totals and per-customer order counts.Limitations
Python 3.11 or newer is required in the project environment. The `build` command does not prove delivery to an external database, and the agent runs the XML against a real system only when the user asks. Fields with personal data are marked by hand in XML for pseudonymisation. The description targets CE 5.0. A connected Platform project uses its remote service and needs no local CE. The MCP adapter needs the separate `datamimic-ce[mcp]` package.
How to disable. Remove the plugin through the agent's plugin manager and uninstall the `datamimic-ce` package from the project environment with `python -m pip uninstall datamimic-ce`.
FAQ
How do I install the plugin in Claude Code?
Run `claude plugin marketplace add https://github.com/rapiddweller/datamimic.git`, then `claude plugin install datamimic@datamimic`. Invoke the skill as `/datamimic:generate-test-data`.
How do I install the plugin in Codex?
Run `codex plugin marketplace add https://github.com/rapiddweller/datamimic.git`, then `codex plugin add datamimic@datamimic`. Invoke the skill as `$generate-test-data`.
Do I need to install CE separately?
No. The setup skill installs `datamimic-ce` version 5 from PyPI when the package is missing. Manually, `python -m pip install "datamimic-ce>=5.0.0,<6"` works.
How do I connect the MCP adapter?
Install `datamimic-ce[mcp]` and run `datamimic-mcp --transport stdio --workspace-root <project path>`. The plugin does not configure MCP itself.
What belongs only to the Enterprise Platform?
The PII scanner, multi-system execution for Oracle, MongoDB and Kafka, EDIFACT, SWIFT MT and HL7 templates, roles, audit and a scheduler.
Discussion
No questions or reviews yet.
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README in short
XML is the persisted executable project, and DM JSON is a transient representation for the agent. Verification follows the expectations in `acceptance/build.json`, and a ready report lists every file read with its digest.