ZenML is a Python framework for turning machine-learning and AI workflows into tracked pipelines that run on different infrastructure backends. It is for ML and AI engineers building applications such as model-training workflows, language-model systems, and agent loops in company environments.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/zenml-io/zenmlWrote 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/rules/zenml-io/zenml/zenml-domain-models)<a href="https://agentmods.dev/rules/zenml-io/zenml/zenml-domain-models"><img src="https://agentmods.dev/badge/rules/zenml-io/zenml/zenml-domain-models/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/rules/zenml-io/zenml/zenml-domain-models"><img src="https://agentmods.dev/badge/rules/zenml-io/zenml/zenml-domain-models.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00000 | $0.02328 |
| Opus 5.5 | $0.00000 | $0.00931 |
| Sonnet 5.5 | $0.00000 | $0.00466 |
| Haiku 4.5 | $0.00000 | $0.00233 |
Grade A, and why
zenml-domain-models 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 — 283 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ZenML Domain Models
Overview
ZenML domain models represent the core business entities in the ZenML ecosystem. They are built using a sophisticated layered architecture that emphasizes clean separation of concerns, type safety, and maintainable code. This document outlines the key patterns and conventions used in implementing domain models. Make sure to follow these patterns when implementing new domain models or modifying existing ones.
All model files are located in the @src/zenml/models directory.
Base Class Hierarchy
The domain models follow two main hierarchies: the Model Hierarchy and the Filter Hierarchy.
Model Hierarchy
BaseZenModel (base.py)
├── BaseRequest
│ ├── UserScopedRequest
│ │ └── WorkspaceScopedRequest
│ └── [Entity]Request
├── BaseUpdate
│ └── [Entity]Update
└── BaseResponse[Body, Metadata, Resources]
├── BaseResponseBody
│ └── BaseDatedResponseBody
│ ├── UserScopedResponseBody
│ └── WorkspaceScopedResponseBody
├── BaseResponseMetadata
│ ├── UserScopedResponseMetadata
│ └── WorkspaceScopedResponseMetadata
├── BaseResponseResources
│ ├── UserScopedResponseResources
│ └── WorkspaceScopedResponseResources
└── BaseIdentifiedResponse[DatedBody, Metadata, Resources]
└── UserScopedResponse[Body, Metadata, Resources]
└── WorkspaceScopedResponse[Body, Metadata, Resources]
Filter Hierarchy
BaseFilter (filter.py)
├── Filter (ABC)
│ ├── BoolFilter
│ ├── StrFilter
│ │ └── UUIDFilter
│ ├── NumericFilter
│ └── DatetimeFilter
├── UserScopedFilter
│ └── WorkspaceScopedFilter
└── TaggableFilter
FilterGenerator
└── Used to create appropriate Filter instances
Key Characteristics
- Model Base Classes:
BaseZenModel: Root class with analytics tracking and YAML serializationBaseRequest: For creating new entitiesBaseUpdate: For modifying existing entitiesBaseResponse: Tripartite structure with Body/Metadata/ResourcesBaseIdentifiedResponse: Adds UUID and permission handlingBaseDatedResponseBody: Adds creation/update timestamps
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 · 283 lines · 0 tokens per session scan A fc08d2fa7ce6
zenml-domain-models is a cursor rule published in the GitHub repository zenml-io/zenml (5,616 stars, last pushed today), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 2,328 tokens. 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.
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