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/comments)<a href="https://agentmods.dev/rules/zenml-io/zenml/comments"><img src="https://agentmods.dev/badge/rules/zenml-io/zenml/comments/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/comments"><img src="https://agentmods.dev/badge/rules/zenml-io/zenml/comments.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 | $0.00250 | $0.00250 |
| Opus 5.5 | $0.00100 | $0.00100 |
| Sonnet 5.5 | $0.00050 | $0.00050 |
| Haiku 4.5 | $0.00025 | $0.00025 |
Grade A, and why
comments 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.
What it actually says
Commenting Policy: Explain Why, Not What
Goal: Comments should help a future maintainer understand intent and constraints months later—not narrate edits.
Use comments to explain
- Non-obvious design decisions / trade-offs and invariants
- Complex logic or algorithms (what makes them tricky)
- Business rules and constraints that drive the design
- Purpose and contract of functions/classes (especially public APIs)
- Edge cases and gotchas being handled and why they matter
Avoid change-tracking comments
- “Updated from previous version”
- “New implementation”
- “Changed to use X instead of Y”
- “Refactored this section”
Avoid simple explanatory comments
- Avoid comments that are obvious descriptions of what the code does when it's already clear from the code itself.
Guidance
- Prefer self-explanatory code; add comments only where extra context is genuinely needed.
- Write for “future you” 6+ months from now.
- In Python, prefer docstrings for API intent and inline comments only for truly non-obvious lines.
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 · 31 lines · 250 tokens per session scan A dac8999ae81f
comments is a cursor rule published in the GitHub repository zenml-io/zenml (5,616 stars, last pushed today), licensed Apache-2.0. It adds 250 tokens to every session, about $0.0010 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 cursor rules, from other repositories
specs
This directory contains product and tech specs for Streamlit features.
workflows
This folder contains all GitHub Actions workflows for the Streamlit repository. Workflows automate CI/CD, testing, releases, and maintenance tasks.
python_tests
We use the unit tests to cover internal behavior that can work without the web / backend counterpart. We aim for 95%+ unit test coverage of our Python code in lib/streamlit.
skills
This file provides guidance to AI coding agents (Claude Code, Cursor, Copilot, etc.) when working with skills in this repository.
e2e_playwright
We use playwright with pytest to e2e test Streamlit library. E2E tests verify the complete Streamlit system (frontend, backend, communication, state, visual appearance) from a user's perspective (black-box). They complement Python/JS unit tests, which are faster and focus on internal logic, input/output validation…
embedded_skills
Skills under lib/streamlit/.agents/skills/ ship with the library and load from the user's installed Streamlit, so keep them current as features evolve. developing-with-streamlit uses SKILL.md for routing and references/ for topic files. Follow these conventions when adding or editing guidance.