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.
Borrowing it
Nothing to install: this file belongs to zenml-io/zenml. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/zenml-io/zenml/main/.agents/skills/zenml-repo-workflows/SKILL.mdgit 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/skills/zenml-io/zenml/zenml-repo-workflows)<a href="https://agentmods.dev/skills/zenml-io/zenml/zenml-repo-workflows"><img src="https://agentmods.dev/badge/skills/zenml-io/zenml/zenml-repo-workflows/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/zenml-io/zenml/zenml-repo-workflows"><img src="https://agentmods.dev/badge/skills/zenml-io/zenml/zenml-repo-workflows.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 | $0.00035 | $0.04588 |
| Opus 5 | $0.00017 | $0.02294 |
| Sonnet 5 | $0.00007 | $0.00918 |
| Haiku 4.5 | $0.00003 | $0.00459 |
Grade A, and why
zenml-repo-workflows 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 11d 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 — 575 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ZenML Repo Workflows
Use this skill when work in the ZenML repository needs more detail than the
always-loaded AGENTS.md files provide. The root guide keeps safety rules,
universal conventions, and the most common commands in memory. This skill keeps
the longer recipes, examples, and subsystem checklists.
Moved Content Index
Former root headings covered here:
- Code Style & Quality Standards
- Commenting policy
- Formatting and Linting
- Python Standards
- Util Function Placement
- Private Methods and API Stability
- FastAPI Agent Profile
- FastAPI Project Structure
- Prefer typing over dynamic attribute checks
- Error Handling & Validation
- Testing Requirements
- Dependencies & Runtime Constraints
- Development Workflow
- Prerequisites
- Documentation Access via MCP
- Environment Variables
- Branch Management
- Making Changes
- Security Guidelines
- Database and Migration Guidelines
- Migration Testing Workflow
- Commit Message Guidelines
- When Implementing Features
- When Fixing Bugs
- Pull Request Guidelines
- Continuous Integration
- Core Concepts
- Important Terminology
- Pipeline Architecture
- Key Abstractions
- Cross-Cutting Architecture Areas
- Common Tasks
- Adding New Integrations
- Modifying Core Functionality
- Expert Tips
- Summary Checklist for PR Reviewers
- Documentation Guidelines
- Structure
- Format and Style
- Content Standards
Development Workflow
Setup
- Install ZenML in development mode with dev dependencies.
- ZenML recommends
uvfor Python package installation because it resolves dependencies more quickly and reliably than plainpip. - Useful development environment variables:
ZENML_LOGGING_VERBOSITY=DEBUGMLSTACKS_ANALYTICS_OPT_OUT=trueAUTO_OPEN_DASHBOARD=falseZENML_ENABLE_RICH_TRACEBACK=falseTOKENIZERS_PARALLELISM=false
- Always set the following environment variables when developing:
ZENML_ANALYTICS_OPT_IN=false: Disables analytics during developmentZENML_DEBUG=true: Uses the development ZenML analytics server to avoid sending analytics to the official ZenML analytics server (IMPORTANT!). This must be set even ifZENML_ANALYTICS_OPT_IN=truebecause in a client-server setup, the server controls the client-side analytics opt-in status.
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.
- 11d ago First seen · 575 lines · 35 tokens per session scan A f9b69cc0e0b9
zenml-repo-workflows is a skill published in the GitHub repository zenml-io/zenml (5,580 stars, last pushed today), licensed Apache-2.0. It adds 35 tokens to every session and 4,588 once invoked, about $0.0002 per session on Opus 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.
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